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- <h1>Source code for ptbench.configs.datasets</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">random</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">torchvision.transforms</span> <span class="kn">import</span> <span class="n">RandomRotation</span>
-
-<span class="sd">"""Standard configurations for dataset setup"""</span>
-
-<span class="n">RANDOM_ROTATION</span> <span class="o">=</span> <span class="p">[</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">15</span><span class="p">)]</span>
-<span class="sd">"""Shared data augmentation based on random rotation only."""</span>
-
-
-<div class="viewcode-block" id="make_subset"><a class="viewcode-back" href="../../../api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.make_subset">[docs]</a><span class="k">def</span> <span class="nf">make_subset</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="p">[],</span> <span class="n">prefixes</span><span class="o">=</span><span class="p">[],</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">[]):</span>
-<span class="w"> </span><span class="sd">"""Creates a new data set, applying transforms.</span>
-
-<span class="sd"> .. note::</span>
-
-<span class="sd"> This is a convenience function for our own dataset definitions inside</span>
-<span class="sd"> this module, guaranteeting homogenity between dataset definitions</span>
-<span class="sd"> provided in this package. It assumes certain strategies for data</span>
-<span class="sd"> augmentation that may not be translatable to other applications.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> samples : list</span>
-<span class="sd"> List of delayed samples</span>
-
-<span class="sd"> transforms : list</span>
-<span class="sd"> A list of transforms that needs to be applied to all samples in the set</span>
-
-<span class="sd"> prefixes : list</span>
-<span class="sd"> A list of data augmentation operations that needs to be applied</span>
-<span class="sd"> **before** the transforms above</span>
-
-<span class="sd"> suffixes : list</span>
-<span class="sd"> A list of data augmentation operations that needs to be applied</span>
-<span class="sd"> **after** the transforms above</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> subset : :py:class:`ptbench.data.utils.SampleListDataset`</span>
-<span class="sd"> A pre-formatted dataset that can be fed to one of our engines</span>
-<span class="sd"> """</span>
- <span class="kn">from</span> <span class="nn">...data.utils</span> <span class="kn">import</span> <span class="n">SampleListDataset</span> <span class="k">as</span> <span class="n">wrapper</span>
-
- <span class="k">return</span> <span class="n">wrapper</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">prefixes</span> <span class="o">+</span> <span class="n">transforms</span> <span class="o">+</span> <span class="n">suffixes</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="make_dataset"><a class="viewcode-back" href="../../../api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.make_dataset">[docs]</a><span class="k">def</span> <span class="nf">make_dataset</span><span class="p">(</span>
- <span class="n">subsets_groups</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="p">[],</span> <span class="n">t_transforms</span><span class="o">=</span><span class="p">[],</span> <span class="n">post_transforms</span><span class="o">=</span><span class="p">[]</span>
-<span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Creates a new configuration dataset from a list of dictionaries and</span>
-<span class="sd"> transforms.</span>
-
-<span class="sd"> This function takes as input a list of dictionaries as those that can be</span>
-<span class="sd"> returned by :py:meth:`ptbench.data.dataset.JSONDataset.subsets`</span>
-<span class="sd"> mapping protocol names (such as ``train``, ``dev`` and ``test``) to</span>
-<span class="sd"> :py:class:`ptbench.data.sample.DelayedSample` lists, and a set of</span>
-<span class="sd"> transforms, and returns a dictionary applying</span>
-<span class="sd"> :py:class:`ptbench.data.utils.SampleListDataset` to these</span>
-<span class="sd"> lists, and our standard data augmentation if a ``train`` set exists.</span>
-
-<span class="sd"> For example, if ``subsets`` is composed of two sets named ``train`` and</span>
-<span class="sd"> ``test``, this function will yield a dictionary with the following entries:</span>
-
-<span class="sd"> * ``__train__``: Wraps the ``train`` subset, includes data augmentation</span>
-<span class="sd"> (note: datasets with names starting with ``_`` (underscore) are excluded</span>
-<span class="sd"> from prediction and evaluation by default, as they contain data</span>
-<span class="sd"> augmentation transformations.)</span>
-<span class="sd"> * ``train``: Wraps the ``train`` subset, **without** data augmentation</span>
-<span class="sd"> * ``test``: Wraps the ``test`` subset, **without** data augmentation</span>
-
-<span class="sd"> .. note::</span>
-
-<span class="sd"> This is a convenience function for our own dataset definitions inside</span>
-<span class="sd"> this module, guaranteeting homogenity between dataset definitions</span>
-<span class="sd"> provided in this package. It assumes certain strategies for data</span>
-<span class="sd"> augmentation that may not be translatable to other applications.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> subsets : list</span>
-<span class="sd"> A list of dictionaries that contains the delayed sample lists</span>
-<span class="sd"> for a number of named lists. The subsets will be aggregated in one</span>
-<span class="sd"> final subset. If one of the keys is ``train``, our standard dataset</span>
-<span class="sd"> augmentation transforms are appended to the definition of that subset.</span>
-<span class="sd"> All other subsets remain un-augmented.</span>
-
-<span class="sd"> transforms : list</span>
-<span class="sd"> A list of transforms that needs to be applied to all samples in the set</span>
-
-<span class="sd"> t_transforms : list</span>
-<span class="sd"> A list of transforms that needs to be applied to the train samples</span>
-
-<span class="sd"> post_transforms : list</span>
-<span class="sd"> A list of transforms that needs to be applied to all samples in the set</span>
-<span class="sd"> after all the other transforms</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> dataset : dict</span>
-<span class="sd"> A pre-formatted dataset that can be fed to one of our engines. It maps</span>
-<span class="sd"> string names to :py:class:`ptbench.data.utils.SampleListDataset`'s.</span>
-<span class="sd"> """</span>
-
- <span class="n">retval</span> <span class="o">=</span> <span class="p">{}</span>
-
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">subsets_groups</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">subsets</span> <span class="o">=</span> <span class="n">subsets_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># If multiple subsets groups: aggregation</span>
- <span class="n">aggregated_subsets</span> <span class="o">=</span> <span class="p">{}</span>
- <span class="k">for</span> <span class="n">subsets</span> <span class="ow">in</span> <span class="n">subsets_groups</span><span class="p">:</span>
- <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">subsets</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
- <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">aggregated_subsets</span><span class="p">:</span>
- <span class="n">aggregated_subsets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">+=</span> <span class="n">subsets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
- <span class="c1"># Shuffle if data comes from multiple datasets</span>
- <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">aggregated_subsets</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">aggregated_subsets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">subsets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
- <span class="n">subsets</span> <span class="o">=</span> <span class="n">aggregated_subsets</span>
-
- <span class="c1"># Add post_transforms after t_transforms for the train set</span>
- <span class="n">t_transforms</span> <span class="o">+=</span> <span class="n">post_transforms</span>
-
- <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">subsets</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
- <span class="n">retval</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">make_subset</span><span class="p">(</span>
- <span class="n">subsets</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="n">post_transforms</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s2">"train"</span><span class="p">:</span>
- <span class="n">retval</span><span class="p">[</span><span class="s2">"__train__"</span><span class="p">]</span> <span class="o">=</span> <span class="n">make_subset</span><span class="p">(</span>
- <span class="n">subsets</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="n">t_transforms</span><span class="p">)</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s2">"validation"</span><span class="p">:</span>
- <span class="c1"># also use it for validation during training</span>
- <span class="n">retval</span><span class="p">[</span><span class="s2">"__valid__"</span><span class="p">]</span> <span class="o">=</span> <span class="n">retval</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-
- <span class="k">if</span> <span class="p">(</span>
- <span class="p">(</span><span class="s2">"__train__"</span> <span class="ow">in</span> <span class="n">retval</span><span class="p">)</span>
- <span class="ow">and</span> <span class="p">(</span><span class="s2">"train"</span> <span class="ow">in</span> <span class="n">retval</span><span class="p">)</span>
- <span class="ow">and</span> <span class="p">(</span><span class="s2">"__valid__"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retval</span><span class="p">)</span>
- <span class="p">):</span>
- <span class="c1"># if the dataset does not have a validation set, we use the unaugmented</span>
- <span class="c1"># training set as validation set</span>
- <span class="n">retval</span><span class="p">[</span><span class="s2">"__valid__"</span><span class="p">]</span> <span class="o">=</span> <span class="n">retval</span><span class="p">[</span><span class="s2">"train"</span><span class="p">]</span>
-
- <span class="k">return</span> <span class="n">retval</span></div>
-
-
-<div class="viewcode-block" id="get_samples_weights"><a class="viewcode-back" href="../../../api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.get_samples_weights">[docs]</a><span class="k">def</span> <span class="nf">get_samples_weights</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Compute the weights of all the samples of the dataset to balance it</span>
-<span class="sd"> using the sampler of the dataloader.</span>
-
-<span class="sd"> This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`</span>
-<span class="sd"> and computes the weights to balance each class in the dataset and the</span>
-<span class="sd"> datasets themselves if we have a ConcatDataset.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> dataset : torch.utils.data.dataset.Dataset</span>
-<span class="sd"> An instance of torch.utils.data.dataset.Dataset</span>
-<span class="sd"> ConcatDataset are supported</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> samples_weights : :py:class:`torch.Tensor`</span>
-<span class="sd"> the weights for all the samples in the dataset given as input</span>
-<span class="sd"> """</span>
- <span class="n">samples_weights</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ConcatDataset</span><span class="p">):</span>
- <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
- <span class="c1"># Weighting only for binary labels</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">_samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
- <span class="c1"># Groundtruth</span>
- <span class="n">targets</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">_samples</span><span class="p">:</span>
- <span class="n">targets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
- <span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-
- <span class="c1"># Count number of samples per class</span>
- <span class="n">class_sample_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
- <span class="p">[</span>
- <span class="p">(</span><span class="n">targets</span> <span class="o">==</span> <span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="p">]</span>
- <span class="p">)</span>
-
- <span class="n">weight</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">class_sample_count</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-
- <span class="n">samples_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">weight</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">])</span>
- <span class="p">)</span>
-
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># We only weight to sample equally from each dataset</span>
- <span class="n">samples_weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="p">),),</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="p">)))</span>
-
- <span class="c1"># Concatenate sample weights from all the datasets</span>
- <span class="n">samples_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">samples_weights</span><span class="p">)</span>
-
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># Weighting only for binary labels</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">_samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
- <span class="c1"># Groundtruth</span>
- <span class="n">targets</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">_samples</span><span class="p">:</span>
- <span class="n">targets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
- <span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-
- <span class="c1"># Count number of samples per class</span>
- <span class="n">class_sample_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
- <span class="p">[</span>
- <span class="p">(</span><span class="n">targets</span> <span class="o">==</span> <span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="p">]</span>
- <span class="p">)</span>
-
- <span class="n">weight</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">class_sample_count</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-
- <span class="n">samples_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">weight</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">])</span>
-
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># Equal weights for non-binary labels</span>
- <span class="n">samples_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">_samples</span><span class="p">))</span>
-
- <span class="k">return</span> <span class="n">samples_weights</span></div>
-
-
-<div class="viewcode-block" id="get_positive_weights"><a class="viewcode-back" href="../../../api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.get_positive_weights">[docs]</a><span class="k">def</span> <span class="nf">get_positive_weights</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Compute the positive weights of each class of the dataset to balance the</span>
-<span class="sd"> BCEWithLogitsLoss criterion.</span>
-
-<span class="sd"> This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`</span>
-<span class="sd"> and computes the positive weights of each class to use them to have</span>
-<span class="sd"> a balanced loss.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> dataset : torch.utils.data.dataset.Dataset</span>
-<span class="sd"> An instance of torch.utils.data.dataset.Dataset</span>
-<span class="sd"> ConcatDataset are supported</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> positive_weights : :py:class:`torch.Tensor`</span>
-<span class="sd"> the positive weight of each class in the dataset given as input</span>
-<span class="sd"> """</span>
- <span class="n">targets</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ConcatDataset</span><span class="p">):</span>
- <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
- <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">_samples</span><span class="p">:</span>
- <span class="n">targets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
-
- <span class="k">else</span><span class="p">:</span>
- <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">_samples</span><span class="p">:</span>
- <span class="n">targets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">label</span><span class="p">)</span>
-
- <span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-
- <span class="c1"># Binary labels</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">targets</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">class_sample_count</span> <span class="o">=</span> <span class="p">[</span>
- <span class="nb">float</span><span class="p">((</span><span class="n">targets</span> <span class="o">==</span> <span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
- <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="p">]</span>
-
- <span class="c1"># Divide negatives by positives</span>
- <span class="n">positive_weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
- <span class="p">[</span><span class="n">class_sample_count</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">class_sample_count</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
- <span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-
- <span class="c1"># Multiclass labels</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">class_sample_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">negative_class_sample_count</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">targets</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">],),</span> <span class="nb">float</span><span class="p">(</span><span class="n">targets</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]))</span>
- <span class="o">-</span> <span class="n">class_sample_count</span>
- <span class="p">)</span>
-
- <span class="n">positive_weights</span> <span class="o">=</span> <span class="n">negative_class_sample_count</span> <span class="o">/</span> <span class="p">(</span>
- <span class="n">class_sample_count</span> <span class="o">+</span> <span class="n">negative_class_sample_count</span>
- <span class="p">)</span>
-
- <span class="k">return</span> <span class="n">positive_weights</span></div>
-</pre></div>
- </article>
- </div>
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- <h1>Source code for ptbench.data.dataset</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">csv</span>
-<span class="kn">import</span> <span class="nn">json</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">pathlib</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="JSONDataset"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset">[docs]</a><span class="k">class</span> <span class="nc">JSONDataset</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Generic multi-protocol/subset filelist dataset that yields samples.</span>
-
-<span class="sd"> To create a new dataset, you need to provide one or more JSON formatted</span>
-<span class="sd"> filelists (one per protocol) with the following contents:</span>
-
-<span class="sd"> .. code-block:: json</span>
-
-<span class="sd"> {</span>
-<span class="sd"> "subset1": [</span>
-<span class="sd"> [</span>
-<span class="sd"> "value1",</span>
-<span class="sd"> "value2",</span>
-<span class="sd"> "value3"</span>
-<span class="sd"> ],</span>
-<span class="sd"> [</span>
-<span class="sd"> "value4",</span>
-<span class="sd"> "value5",</span>
-<span class="sd"> "value6"</span>
-<span class="sd"> ]</span>
-<span class="sd"> ],</span>
-<span class="sd"> "subset2": [</span>
-<span class="sd"> ]</span>
-<span class="sd"> }</span>
-
-<span class="sd"> Your dataset many contain any number of subsets, but all sample entries</span>
-<span class="sd"> must contain the same number of fields.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> protocols : list, dict</span>
-<span class="sd"> Paths to one or more JSON formatted files containing the various</span>
-<span class="sd"> protocols to be recognized by this dataset, or a dictionary, mapping</span>
-<span class="sd"> protocol names to paths (or opened file objects) of CSV files.</span>
-<span class="sd"> Internally, we save a dictionary where keys default to the basename of</span>
-<span class="sd"> paths (list input).</span>
-
-<span class="sd"> fieldnames : list, tuple</span>
-<span class="sd"> An iterable over the field names (strings) to assign to each entry in</span>
-<span class="sd"> the JSON file. It should have as many items as fields in each entry of</span>
-<span class="sd"> the JSON file.</span>
-
-<span class="sd"> loader : object</span>
-<span class="sd"> A function that receives as input, a context dictionary (with at least</span>
-<span class="sd"> a "protocol" and "subset" keys indicating which protocol and subset are</span>
-<span class="sd"> being served), and a dictionary with ``{fieldname: value}`` entries,</span>
-<span class="sd"> and returns an object with at least 2 attributes:</span>
-
-<span class="sd"> * ``key``: which must be a unique string for every sample across</span>
-<span class="sd"> subsets in a protocol, and</span>
-<span class="sd"> * ``data``: which contains the data associated witht this sample</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">protocols</span><span class="p">,</span> <span class="n">fieldnames</span><span class="p">,</span> <span class="n">loader</span><span class="p">):</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocols</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span> <span class="o">=</span> <span class="n">protocols</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span> <span class="o">=</span> <span class="p">{</span>
- <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span>
- <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">suffixes</span><span class="p">),</span> <span class="s2">""</span><span class="p">)</span>
- <span class="p">):</span> <span class="n">k</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">protocols</span>
- <span class="p">}</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fieldnames</span> <span class="o">=</span> <span class="n">fieldnames</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_loader</span> <span class="o">=</span> <span class="n">loader</span>
-
-<div class="viewcode-block" id="JSONDataset.check"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset.check">[docs]</a> <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""For each protocol, check if all data can be correctly accessed.</span>
-
-<span class="sd"> This function assumes each sample has a ``data`` and a ``key``</span>
-<span class="sd"> attribute. The ``key`` attribute should be a string, or representable</span>
-<span class="sd"> as such.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> limit : int</span>
-<span class="sd"> Maximum number of samples to check (in each protocol/subset</span>
-<span class="sd"> combination) in this dataset. If set to zero, then check</span>
-<span class="sd"> everything.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> errors : int</span>
-<span class="sd"> Number of errors found</span>
-<span class="sd"> """</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Checking dataset..."</span><span class="p">)</span>
- <span class="n">errors</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="k">for</span> <span class="n">proto</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Checking protocol '</span><span class="si">{</span><span class="n">proto</span><span class="si">}</span><span class="s2">'..."</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">subsets</span><span class="p">(</span><span class="n">proto</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Checking subset '</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">'..."</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">limit</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Checking at most first '</span><span class="si">{</span><span class="n">limit</span><span class="si">}</span><span class="s2">' samples..."</span><span class="p">)</span>
- <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[:</span><span class="n">limit</span><span class="p">]</span>
- <span class="k">for</span> <span class="n">pos</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="n">sample</span><span class="o">.</span><span class="n">data</span> <span class="c1"># may trigger data loading</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">sample</span><span class="o">.</span><span class="n">key</span><span class="si">}</span><span class="s2">: OK"</span><span class="p">)</span>
- <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Found error loading entry </span><span class="si">{</span><span class="n">pos</span><span class="si">}</span><span class="s2"> in subset </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> "</span>
- <span class="sa">f</span><span class="s2">"of protocol </span><span class="si">{</span><span class="n">proto</span><span class="si">}</span><span class="s2"> from file "</span>
- <span class="sa">f</span><span class="s2">"'</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span><span class="p">[</span><span class="n">proto</span><span class="p">]</span><span class="si">}</span><span class="s2">': </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span>
- <span class="p">)</span>
- <span class="n">errors</span> <span class="o">+=</span> <span class="mi">1</span>
- <span class="k">return</span> <span class="n">errors</span></div>
-
-<div class="viewcode-block" id="JSONDataset.subsets"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset.subsets">[docs]</a> <span class="k">def</span> <span class="nf">subsets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">protocol</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns all subsets in a protocol.</span>
-
-<span class="sd"> This method will load JSON information for a given protocol and return</span>
-<span class="sd"> all subsets of the given protocol after converting each entry through</span>
-<span class="sd"> the loader function.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> protocol : str</span>
-<span class="sd"> Name of the protocol data to load</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> subsets : dict</span>
-<span class="sd"> A dictionary mapping subset names to lists of objects (respecting</span>
-<span class="sd"> the ``key``, ``data`` interface).</span>
-<span class="sd"> """</span>
- <span class="n">fileobj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span><span class="p">[</span><span class="n">protocol</span><span class="p">]</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fileobj</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">,</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">)):</span>
- <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">fileobj</span><span class="p">)</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">".bz2"</span><span class="p">):</span>
- <span class="kn">import</span> <span class="nn">bz2</span>
-
- <span class="k">with</span> <span class="n">bz2</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span><span class="p">[</span><span class="n">protocol</span><span class="p">])</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_protocols</span><span class="p">[</span><span class="n">protocol</span><span class="p">])</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fileobj</span><span class="p">)</span>
- <span class="n">fileobj</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
- <span class="n">retval</span> <span class="o">=</span> <span class="p">{}</span>
- <span class="k">for</span> <span class="n">subset</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">retval</span><span class="p">[</span><span class="n">subset</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_loader</span><span class="p">(</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">protocol</span><span class="o">=</span><span class="n">protocol</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="n">subset</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">n</span><span class="p">),</span>
- <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fieldnames</span><span class="p">,</span> <span class="n">k</span><span class="p">)),</span>
- <span class="p">)</span>
- <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
- <span class="p">]</span>
-
- <span class="k">return</span> <span class="n">retval</span></div></div>
-
-
-<div class="viewcode-block" id="CSVDataset"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset">[docs]</a><span class="k">class</span> <span class="nc">CSVDataset</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Generic multi-subset filelist dataset that yields samples.</span>
-
-<span class="sd"> To create a new dataset, you only need to provide a CSV formatted filelist</span>
-<span class="sd"> using any separator (e.g. comma, space, semi-colon) with the following</span>
-<span class="sd"> information:</span>
-
-<span class="sd"> .. code-block:: text</span>
-
-<span class="sd"> value1,value2,value3</span>
-<span class="sd"> value4,value5,value6</span>
-<span class="sd"> ...</span>
-
-<span class="sd"> Notice that all rows must have the same number of entries.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> subsets : list, dict</span>
-<span class="sd"> Paths to one or more CSV formatted files containing the various subsets</span>
-<span class="sd"> to be recognized by this dataset, or a dictionary, mapping subset names</span>
-<span class="sd"> to paths (or opened file objects) of CSV files. Internally, we save a</span>
-<span class="sd"> dictionary where keys default to the basename of paths (list input).</span>
-
-<span class="sd"> fieldnames : list, tuple</span>
-<span class="sd"> An iterable over the field names (strings) to assign to each column in</span>
-<span class="sd"> the CSV file. It should have as many items as fields in each row of</span>
-<span class="sd"> the CSV file(s).</span>
-
-<span class="sd"> loader : object</span>
-<span class="sd"> A function that receives as input, a context dictionary (with, at</span>
-<span class="sd"> least, a "subset" key indicating which subset is being served), and a</span>
-<span class="sd"> dictionary with ``{key: path}`` entries, and returns a dictionary with</span>
-<span class="sd"> the loaded data.</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subsets</span><span class="p">,</span> <span class="n">fieldnames</span><span class="p">,</span> <span class="n">loader</span><span class="p">):</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subsets</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span> <span class="o">=</span> <span class="n">subsets</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span> <span class="o">=</span> <span class="p">{</span>
- <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span>
- <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">suffixes</span><span class="p">),</span> <span class="s2">""</span><span class="p">)</span>
- <span class="p">):</span> <span class="n">k</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">subsets</span>
- <span class="p">}</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fieldnames</span> <span class="o">=</span> <span class="n">fieldnames</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_loader</span> <span class="o">=</span> <span class="n">loader</span>
-
-<div class="viewcode-block" id="CSVDataset.check"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.check">[docs]</a> <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""For each subset, check if all data can be correctly accessed.</span>
-
-<span class="sd"> This function assumes each sample has a ``data`` and a ``key``</span>
-<span class="sd"> attribute. The ``key`` attribute should be a string, or representable</span>
-<span class="sd"> as such.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> limit : int</span>
-<span class="sd"> Maximum number of samples to check (in each protocol/subset</span>
-<span class="sd"> combination) in this dataset. If set to zero, then check</span>
-<span class="sd"> everything.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> errors : int</span>
-<span class="sd"> Number of errors found</span>
-<span class="sd"> """</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Checking dataset..."</span><span class="p">)</span>
- <span class="n">errors</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Checking subset '</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">'..."</span><span class="p">)</span>
- <span class="n">samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">limit</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Checking at most first '</span><span class="si">{</span><span class="n">limit</span><span class="si">}</span><span class="s2">' samples..."</span><span class="p">)</span>
- <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[:</span><span class="n">limit</span><span class="p">]</span>
- <span class="k">for</span> <span class="n">pos</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="n">sample</span><span class="o">.</span><span class="n">data</span> <span class="c1"># may trigger data loading</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">sample</span><span class="o">.</span><span class="n">key</span><span class="si">}</span><span class="s2">: OK"</span><span class="p">)</span>
- <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Found error loading entry </span><span class="si">{</span><span class="n">pos</span><span class="si">}</span><span class="s2"> in subset </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> "</span>
- <span class="sa">f</span><span class="s2">"from file '</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="si">}</span><span class="s2">': </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span>
- <span class="p">)</span>
- <span class="n">errors</span> <span class="o">+=</span> <span class="mi">1</span>
- <span class="k">return</span> <span class="n">errors</span></div>
-
-<div class="viewcode-block" id="CSVDataset.subsets"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.subsets">[docs]</a> <span class="k">def</span> <span class="nf">subsets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns all available subsets at once.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> subsets : dict</span>
-<span class="sd"> A dictionary mapping subset names to lists of objects (respecting</span>
-<span class="sd"> the ``key``, ``data`` interface).</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span></div>
-
-<div class="viewcode-block" id="CSVDataset.samples"><a class="viewcode-back" href="../../../api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.samples">[docs]</a> <span class="k">def</span> <span class="nf">samples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subset</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns all samples in a subset.</span>
-
-<span class="sd"> This method will load CSV information for a given subset and return</span>
-<span class="sd"> all samples of the given subset after passing each entry through the</span>
-<span class="sd"> loading function.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> subset : str</span>
-<span class="sd"> Name of the subset data to load</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> subset : list</span>
-<span class="sd"> A lists of objects (respecting the ``key``, ``data`` interface).</span>
-<span class="sd"> """</span>
- <span class="n">fileobj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span><span class="p">[</span><span class="n">subset</span><span class="p">]</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fileobj</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">,</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">)):</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_subsets</span><span class="p">[</span><span class="n">subset</span><span class="p">],</span> <span class="n">newline</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">cf</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
- <span class="n">samples</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">cf</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">cf</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="n">fileobj</span><span class="p">)</span>
- <span class="n">samples</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">cf</span><span class="p">]</span>
- <span class="n">fileobj</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="p">[</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_loader</span><span class="p">(</span>
- <span class="nb">dict</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="n">subset</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">n</span><span class="p">),</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fieldnames</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
- <span class="p">)</span>
- <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
- <span class="p">]</span></div></div>
-</pre></div>
- </article>
- </div>
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- <h1>Source code for ptbench.data.loader</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-
-<span class="sd">"""Data loading code."""</span>
-
-
-<span class="kn">import</span> <span class="nn">functools</span>
-
-<span class="kn">import</span> <span class="nn">PIL.Image</span>
-
-<span class="kn">from</span> <span class="nn">.sample</span> <span class="kn">import</span> <span class="n">DelayedSample</span>
-
-
-<div class="viewcode-block" id="load_pil"><a class="viewcode-back" href="../../../api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil">[docs]</a><span class="k">def</span> <span class="nf">load_pil</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Loads a sample data.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> path : str</span>
-<span class="sd"> The full path leading to the image to be loaded</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> image : PIL.Image.Image</span>
-<span class="sd"> A PIL image</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="load_pil_baw"><a class="viewcode-back" href="../../../api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil_baw">[docs]</a><span class="k">def</span> <span class="nf">load_pil_baw</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Loads a sample data.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> path : str</span>
-<span class="sd"> The full path leading to the image to be loaded</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> image : PIL.Image.Image</span>
-<span class="sd"> A PIL image in grayscale mode</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">load_pil</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"L"</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="load_pil_rgb"><a class="viewcode-back" href="../../../api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil_rgb">[docs]</a><span class="k">def</span> <span class="nf">load_pil_rgb</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Loads a sample data.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> path : str</span>
-<span class="sd"> The full path leading to the image to be loaded</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> image : PIL.Image.Image</span>
-<span class="sd"> A PIL image in RGB mode</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">load_pil</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"RGB"</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="make_delayed"><a class="viewcode-back" href="../../../api/data/ptbench.data.loader.html#ptbench.data.loader.make_delayed">[docs]</a><span class="k">def</span> <span class="nf">make_delayed</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">loader</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns a delayed-loading Sample object.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> sample : dict</span>
-<span class="sd"> A dictionary that maps field names to sample data values (e.g. paths)</span>
-
-<span class="sd"> loader : object</span>
-<span class="sd"> A function that inputs ``sample`` dictionaries and returns the loaded</span>
-<span class="sd"> data.</span>
-
-<span class="sd"> key : str</span>
-<span class="sd"> A unique key identifier for this sample. If not provided, assumes</span>
-<span class="sd"> ``sample`` is a dictionary with a ``data`` entry and uses its path as</span>
-<span class="sd"> key.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> sample : ptbench.data.sample.DelayedSample</span>
-<span class="sd"> In which ``key`` is as provided and ``data`` can be accessed to trigger</span>
-<span class="sd"> sample loading.</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">DelayedSample</span><span class="p">(</span>
- <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">loader</span><span class="p">,</span> <span class="n">sample</span><span class="p">),</span>
- <span class="n">key</span><span class="o">=</span><span class="n">key</span> <span class="ow">or</span> <span class="n">sample</span><span class="p">[</span><span class="s2">"data"</span><span class="p">],</span>
- <span class="n">label</span><span class="o">=</span><span class="n">sample</span><span class="p">[</span><span class="s2">"label"</span><span class="p">],</span>
- <span class="p">)</span></div>
-</pre></div>
- </article>
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- <h1>Source code for ptbench.data.sample</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="sd">"""Base definition of sample."""</span>
-
-
-<span class="k">def</span> <span class="nf">_copy_attributes</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Copies attributes from a dictionary to self."""</span>
- <span class="n">s</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
- <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"data"</span><span class="p">,</span> <span class="s2">"load"</span><span class="p">,</span> <span class="s2">"samples"</span><span class="p">)}</span>
- <span class="p">)</span>
-
-
-<div class="viewcode-block" id="DelayedSample"><a class="viewcode-back" href="../../../api/data/ptbench.data.sample.html#ptbench.data.sample.DelayedSample">[docs]</a><span class="k">class</span> <span class="nc">DelayedSample</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Representation of sample that can be loaded via a callable.</span>
-
-<span class="sd"> The optional ``**kwargs`` argument allows you to attach more attributes to</span>
-<span class="sd"> this sample instance.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> load : object</span>
-<span class="sd"> A python function that can be called parameterlessly, to load the</span>
-<span class="sd"> sample in question from whatever medium</span>
-
-<span class="sd"> parent : :py:class:`DelayedSample`, :py:class:`Sample`, None</span>
-<span class="sd"> If passed, consider this as a parent of this sample, to copy</span>
-<span class="sd"> information</span>
-
-<span class="sd"> kwargs : dict</span>
-<span class="sd"> Further attributes of this sample, to be stored and eventually</span>
-<span class="sd"> transmitted to transformed versions of the sample</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">load</span><span class="p">,</span> <span class="n">parent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">load</span> <span class="o">=</span> <span class="n">load</span>
- <span class="k">if</span> <span class="n">parent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">_copy_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
- <span class="n">_copy_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Loads the data from the disk file."""</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">load</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="Sample"><a class="viewcode-back" href="../../../api/data/ptbench.data.sample.html#ptbench.data.sample.Sample">[docs]</a><span class="k">class</span> <span class="nc">Sample</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Representation of sample that is sufficient for the blocks in this</span>
-<span class="sd"> module.</span>
-
-<span class="sd"> Each sample must have the following attributes:</span>
-
-<span class="sd"> * attribute ``data``: Contains the data for this sample</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> data : object</span>
-<span class="sd"> Object representing the data to initialize this sample with.</span>
-
-<span class="sd"> parent : object</span>
-<span class="sd"> A parent object from which to inherit all other attributes (except</span>
-<span class="sd"> ``data``)</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
- <span class="k">if</span> <span class="n">parent</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">_copy_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
- <span class="n">_copy_attributes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
-</pre></div>
- </article>
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diff --git a/sphinx/_modules/ptbench/data/transforms.html b/sphinx/_modules/ptbench/data/transforms.html
deleted file mode 100644
index 3c63db12af22c4643880a95416a40e35bb2603c4..0000000000000000000000000000000000000000
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- <h1>Source code for ptbench.data.transforms</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="sd">"""Image transformations for our pipelines.</span>
-
-<span class="sd">Differences between methods here and those from</span>
-<span class="sd">:py:mod:`torchvision.transforms` is that these support multiple</span>
-<span class="sd">simultaneous image inputs, which are required to feed segmentation</span>
-<span class="sd">networks (e.g. image and labels or masks). We also take care of data</span>
-<span class="sd">augmentations, in which random flipping and rotation needs to be applied</span>
-<span class="sd">across all input images, but color jittering, for example, only on the</span>
-<span class="sd">input image.</span>
-<span class="sd">"""</span>
-
-<span class="kn">import</span> <span class="nn">random</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">PIL.Image</span>
-
-<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span><span class="p">,</span> <span class="n">map_coordinates</span>
-
-
-<div class="viewcode-block" id="SingleAutoLevel16to8"><a class="viewcode-back" href="../../../api/data/ptbench.data.transforms.html#ptbench.data.transforms.SingleAutoLevel16to8">[docs]</a><span class="k">class</span> <span class="nc">SingleAutoLevel16to8</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Converts a 16-bit image to 8-bit representation using "auto-level".</span>
-
-<span class="sd"> This transform assumes that the input image is gray-scaled.</span>
-
-<span class="sd"> To auto-level, we calculate the maximum and the minimum of the</span>
-<span class="sd"> image, and</span>
-<span class="sd"> consider such a range should be mapped to the [0,255] range of the</span>
-<span class="sd"> destination image.</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
- <span class="n">imin</span><span class="p">,</span> <span class="n">imax</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">getextrema</span><span class="p">()</span>
- <span class="n">irange</span> <span class="o">=</span> <span class="n">imax</span> <span class="o">-</span> <span class="n">imin</span>
- <span class="k">return</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">round</span><span class="p">(</span>
- <span class="mf">255.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span> <span class="o">-</span> <span class="n">imin</span><span class="p">)</span> <span class="o">/</span> <span class="n">irange</span>
- <span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"uint8"</span><span class="p">),</span>
- <span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"L"</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RemoveBlackBorders"><a class="viewcode-back" href="../../../api/data/ptbench.data.transforms.html#ptbench.data.transforms.RemoveBlackBorders">[docs]</a><span class="k">class</span> <span class="nc">RemoveBlackBorders</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Remove black borders of CXR."""</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
- <span class="n">img</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
- <span class="n">mask</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span>
- <span class="k">return</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="n">mask</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">mask</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="mi">0</span><span class="p">))])</span></div>
-
-
-<div class="viewcode-block" id="ElasticDeformation"><a class="viewcode-back" href="../../../api/data/ptbench.data.transforms.html#ptbench.data.transforms.ElasticDeformation">[docs]</a><span class="k">class</span> <span class="nc">ElasticDeformation</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Elastic deformation of 2D image slightly adapted from [SIMARD-2003]_.</span>
-
-<span class="sd"> Source: https://gist.github.com/oeway/2e3b989e0343f0884388ed7ed82eb3b0</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
- <span class="bp">self</span><span class="p">,</span>
- <span class="n">alpha</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
- <span class="n">sigma</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
- <span class="n">spline_order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
- <span class="n">mode</span><span class="o">=</span><span class="s2">"nearest"</span><span class="p">,</span>
- <span class="n">random_state</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="p">,</span>
- <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
- <span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">spline_order</span> <span class="o">=</span> <span class="n">spline_order</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span>
- <span class="n">img</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
-
- <span class="k">assert</span> <span class="n">img</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span>
-
- <span class="n">shape</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span>
-
- <span class="n">dx</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">gaussian_filter</span><span class="p">(</span>
- <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span>
- <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span>
- <span class="n">cval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
- <span class="p">)</span>
- <span class="n">dy</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">gaussian_filter</span><span class="p">(</span>
- <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">,</span>
- <span class="n">mode</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span>
- <span class="n">cval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
- <span class="p">)</span>
-
- <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">"ij"</span>
- <span class="p">)</span>
- <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">dx</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">y</span> <span class="o">+</span> <span class="n">dy</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
- <span class="p">]</span>
- <span class="n">result</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
- <span class="n">result</span><span class="p">[:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">map_coordinates</span><span class="p">(</span>
- <span class="n">img</span><span class="p">[:,</span> <span class="p">:],</span> <span class="n">indices</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spline_order</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span>
- <span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">img</span></div>
-</pre></div>
- </article>
- </div>
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-
- <div class="related-pages">
-
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- </div>
- <div class="bottom-of-page">
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- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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- <h1>Source code for ptbench.data.utils</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-
-<span class="sd">"""Common utilities."""</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">PIL</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.utils.data</span>
-
-<span class="kn">from</span> <span class="nn">torchvision.transforms</span> <span class="kn">import</span> <span class="n">Compose</span><span class="p">,</span> <span class="n">ToTensor</span>
-
-
-<div class="viewcode-block" id="SampleListDataset"><a class="viewcode-back" href="../../../api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset">[docs]</a><span class="k">class</span> <span class="nc">SampleListDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""PyTorch dataset wrapper around Sample lists.</span>
-
-<span class="sd"> A transform object can be passed that will be applied to the image, ground</span>
-<span class="sd"> truth and mask (if present).</span>
-
-<span class="sd"> It supports indexing such that dataset[i] can be used to get ith sample.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> samples : list</span>
-<span class="sd"> A list of :py:class:`ptbench.data.sample.Sample` objects</span>
-
-<span class="sd"> transforms : :py:class:`list`, Optional</span>
-<span class="sd"> a list of transformations to be applied to **both** image and</span>
-<span class="sd"> ground-truth data. Notice a last transform</span>
-<span class="sd"> (:py:class:`torchvision.transforms.transforms.ToTensor`) is always</span>
-<span class="sd"> applied - you do not need to add that.</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="p">[]):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_samples</span> <span class="o">=</span> <span class="n">samples</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">transforms</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transforms</span><span class="o">.</span><span class="n">transforms</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
- <span class="nd">@transforms</span><span class="o">.</span><span class="n">setter</span>
- <span class="k">def</span> <span class="nf">transforms</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
- <span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ToTensor</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_transforms</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_transforms</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span><span class="n">data</span> <span class="o">+</span> <span class="p">[</span><span class="n">ToTensor</span><span class="p">()])</span>
-
-<div class="viewcode-block" id="SampleListDataset.copy"><a class="viewcode-back" href="../../../api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset.copy">[docs]</a> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns a deep copy of itself, optionally resetting transforms.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> transforms : :py:class:`list`, Optional</span>
-<span class="sd"> An optional list of transforms to set in the copy. If not</span>
-<span class="sd"> specified, use ``self.transforms``.</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">SampleListDataset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_samples</span><span class="p">,</span> <span class="n">transforms</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="SampleListDataset.random_permute"><a class="viewcode-back" href="../../../api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset.random_permute">[docs]</a> <span class="k">def</span> <span class="nf">random_permute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Randomly permute feature values from all samples.</span>
-
-<span class="sd"> Useful for permutation feature importance computation</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> feature : int</span>
-<span class="sd"> The position of the feature</span>
-<span class="sd"> """</span>
- <span class="n">feature_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span>
-
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_samples</span><span class="p">):</span>
- <span class="n">features</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">"data"</span><span class="p">]</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
- <span class="n">feature_values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span>
-
- <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">feature_values</span><span class="p">)</span>
-
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_samples</span><span class="p">):</span>
- <span class="n">features</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">"data"</span><span class="p">]</span>
- <span class="n">features</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">=</span> <span class="n">feature_values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span></div>
-
- <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> size : int</span>
-<span class="sd"> size of the dataset</span>
-
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_samples</span><span class="p">)</span>
-
- <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> key : int, slice</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> sample : list</span>
-<span class="sd"> The sample data: ``[key, image, label]``</span>
-
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
- <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">*</span><span class="n">key</span><span class="o">.</span><span class="n">indices</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)))]</span>
- <span class="k">else</span><span class="p">:</span> <span class="c1"># we try it as an int</span>
- <span class="n">item</span> <span class="o">=</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_samples</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
- <span class="n">key</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">key</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">data</span> <span class="c1"># triggers data loading</span>
-
- <span class="n">retval</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"data"</span><span class="p">]</span>
-
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transforms</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">retval</span><span class="p">,</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">Image</span><span class="p">):</span>
- <span class="n">retval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transforms</span><span class="p">(</span><span class="n">retval</span><span class="p">)</span>
- <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">retval</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
- <span class="n">retval</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">retval</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="s2">"label"</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">"label"</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
- <span class="k">return</span> <span class="p">[</span><span class="n">key</span><span class="p">,</span> <span class="n">retval</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">"label"</span><span class="p">])]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="p">[</span><span class="n">key</span><span class="p">,</span> <span class="n">retval</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]]</span>
-
- <span class="k">return</span> <span class="p">[</span><span class="n">item</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> <span class="n">retval</span><span class="p">]</span></div>
-</pre></div>
- </article>
- </div>
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- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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diff --git a/sphinx/_modules/ptbench/engine/evaluator.html b/sphinx/_modules/ptbench/engine/evaluator.html
deleted file mode 100644
index 60f4fcbc10b56844c7da342eea3e3486d1c4dc5b..0000000000000000000000000000000000000000
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- <h1>Source code for ptbench.engine.evaluator</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="sd">"""Defines functionality for the evaluation of predictions."""</span>
-
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">re</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
-
-<span class="kn">from</span> <span class="nn">..utils.measure</span> <span class="kn">import</span> <span class="n">base_measures</span><span class="p">,</span> <span class="n">get_centered_maxf1</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="eer_threshold"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.eer_threshold">[docs]</a><span class="k">def</span> <span class="nf">eer_threshold</span><span class="p">(</span><span class="n">neg</span><span class="p">,</span> <span class="n">pos</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Evaluates the EER threshold from negative and positive scores.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> neg : typing.Iterable[float]</span>
-<span class="sd"> Negative scores</span>
-
-<span class="sd"> pos : typing.Iterable[float]</span>
-<span class="sd"> Positive scores</span>
-
-
-<span class="sd"> Returns:</span>
-
-<span class="sd"> Threshold</span>
-<span class="sd"> """</span>
- <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
- <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">brentq</span>
-
- <span class="n">y_predictions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">neg</span><span class="p">,</span> <span class="n">pos</span><span class="p">))</span>
- <span class="n">y_true</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">numpy</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">neg</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">pos</span><span class="p">)))</span>
-
- <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_predictions</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
- <span class="n">eer</span> <span class="o">=</span> <span class="n">brentq</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">x</span> <span class="o">-</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)(</span><span class="n">x</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">)(</span><span class="n">eer</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="posneg"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.posneg">[docs]</a><span class="k">def</span> <span class="nf">posneg</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">,</span> <span class="n">threshold</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Calculates true and false positives and negatives."""</span>
- <span class="c1"># threshold</span>
- <span class="n">binary_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
-
- <span class="c1"># equals and not-equals</span>
- <span class="n">equals</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">binary_pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
- <span class="n">notequals</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">binary_pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
-
- <span class="c1"># true positives</span>
- <span class="n">tp_tensor</span> <span class="o">=</span> <span class="p">(</span><span class="n">gt</span> <span class="o">*</span> <span class="n">binary_pred</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
-
- <span class="c1"># false positives</span>
- <span class="n">fp_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eq</span><span class="p">((</span><span class="n">binary_pred</span> <span class="o">+</span> <span class="n">tp_tensor</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
-
- <span class="c1"># true negatives</span>
- <span class="n">tn_tensor</span> <span class="o">=</span> <span class="p">(</span><span class="n">equals</span> <span class="o">-</span> <span class="n">tp_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
-
- <span class="c1"># false negatives</span>
- <span class="n">fn_tensor</span> <span class="o">=</span> <span class="n">notequals</span> <span class="o">-</span> <span class="n">fp_tensor</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">tp_tensor</span><span class="p">,</span> <span class="n">fp_tensor</span><span class="p">,</span> <span class="n">tn_tensor</span><span class="p">,</span> <span class="n">fn_tensor</span></div>
-
-
-<div class="viewcode-block" id="sample_measures_for_threshold"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.sample_measures_for_threshold">[docs]</a><span class="k">def</span> <span class="nf">sample_measures_for_threshold</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">,</span> <span class="n">threshold</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Calculates measures on one single sample, for a specific threshold.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> pred : torch.Tensor</span>
-<span class="sd"> pixel-wise predictions</span>
-
-<span class="sd"> gt : torch.Tensor</span>
-<span class="sd"> ground-truth (annotations)</span>
-
-<span class="sd"> threshold : float</span>
-<span class="sd"> a particular threshold in which to calculate the performance</span>
-<span class="sd"> measures</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> precision: float</span>
-
-<span class="sd"> recall: float</span>
-
-<span class="sd"> specificity: float</span>
-
-<span class="sd"> accuracy: float</span>
-
-<span class="sd"> jaccard: float</span>
-
-<span class="sd"> f1_score: float</span>
-<span class="sd"> """</span>
- <span class="n">tp_tensor</span><span class="p">,</span> <span class="n">fp_tensor</span><span class="p">,</span> <span class="n">tn_tensor</span><span class="p">,</span> <span class="n">fn_tensor</span> <span class="o">=</span> <span class="n">posneg</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
-
- <span class="c1"># calc measures from scalars</span>
- <span class="n">tp_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tp_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">fp_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fp_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">tn_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tn_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">fn_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fn_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="k">return</span> <span class="n">base_measures</span><span class="p">(</span><span class="n">tp_count</span><span class="p">,</span> <span class="n">fp_count</span><span class="p">,</span> <span class="n">tn_count</span><span class="p">,</span> <span class="n">fn_count</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="run"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.run">[docs]</a><span class="k">def</span> <span class="nf">run</span><span class="p">(</span>
- <span class="n">dataset</span><span class="p">,</span>
- <span class="n">name</span><span class="p">,</span>
- <span class="n">predictions_folder</span><span class="p">,</span>
- <span class="n">output_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
- <span class="n">f1_thresh</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
- <span class="n">eer_thresh</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
- <span class="n">steps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Runs inference and calculates measures.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ---------</span>
-
-<span class="sd"> dataset : py:class:`torch.utils.data.Dataset`</span>
-<span class="sd"> a dataset to iterate on</span>
-
-<span class="sd"> name : str</span>
-<span class="sd"> the local name of this dataset (e.g. ``train``, or ``test``), to be</span>
-<span class="sd"> used when saving measures files.</span>
-
-<span class="sd"> predictions_folder : str</span>
-<span class="sd"> folder where predictions for the dataset images has been previously</span>
-<span class="sd"> stored</span>
-
-<span class="sd"> output_folder : :py:class:`str`, Optional</span>
-<span class="sd"> folder where to store results.</span>
-
-<span class="sd"> f1_thresh : :py:class:`float`, Optional</span>
-<span class="sd"> This number should come from</span>
-<span class="sd"> the training set or a separate validation set. Using a test set value</span>
-<span class="sd"> may bias your analysis. This number is also used to print the a priori</span>
-<span class="sd"> F1-score on the evaluated set.</span>
-
-<span class="sd"> eer_thresh : :py:class:`float`, Optional</span>
-<span class="sd"> This number should come from</span>
-<span class="sd"> the training set or a separate validation set. Using a test set value</span>
-<span class="sd"> may bias your analysis. This number is used to print the a priori</span>
-<span class="sd"> EER.</span>
-
-<span class="sd"> steps : :py:class:`float`, Optional</span>
-<span class="sd"> number of threshold steps to consider when evaluating thresholds.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> f1_threshold : float</span>
-<span class="sd"> Threshold to achieve the highest possible F1-score for this dataset</span>
-
-<span class="sd"> eer_threshold : float</span>
-<span class="sd"> Threshold achieving Equal Error Rate for this dataset</span>
-<span class="sd"> """</span>
-
- <span class="n">predictions_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictions_folder</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="s2">"predictions.csv"</span><span class="p">)</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">):</span>
- <span class="n">predictions_path</span> <span class="o">=</span> <span class="n">predictions_folder</span>
-
- <span class="c1"># Load predictions</span>
- <span class="n">pred_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
- <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span>
- <span class="p">[</span>
- <span class="nb">eval</span><span class="p">(</span><span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">" +"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="s2">""</span><span class="p">))</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" "</span><span class="p">,</span> <span class="s2">","</span><span class="p">))</span>
- <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">pred_data</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
- <span class="p">]</span>
- <span class="p">)</span><span class="o">.</span><span class="n">double</span><span class="p">()</span>
- <span class="n">gt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span>
- <span class="p">[</span>
- <span class="nb">eval</span><span class="p">(</span><span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">" +"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="s2">""</span><span class="p">))</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" "</span><span class="p">,</span> <span class="s2">","</span><span class="p">))</span>
- <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">pred_data</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
- <span class="p">]</span>
- <span class="p">)</span><span class="o">.</span><span class="n">double</span><span class="p">()</span>
-
- <span class="k">if</span> <span class="n">pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">gt</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span>
- <span class="n">gt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">gt</span><span class="p">)</span>
-
- <span class="n">pred_data</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span>
- <span class="n">pred_data</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">]</span> <span class="o">=</span> <span class="n">gt</span>
-
- <span class="c1"># Multiclass f1 score computation</span>
- <span class="k">if</span> <span class="n">pred</span><span class="o">.</span><span class="n">ndim</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">auc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">gt</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Evaluating multiclass classification"</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"AUC: </span><span class="si">{</span><span class="n">auc</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"F1 and EER are not implemented for multiclass"</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
-
- <span class="c1"># Generate measures for each threshold</span>
- <span class="n">step_size</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">steps</span>
- <span class="n">data</span> <span class="o">=</span> <span class="p">[</span>
- <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span> <span class="o">+</span> <span class="n">sample_measures_for_threshold</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">threshold</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">step_size</span><span class="p">))</span>
- <span class="p">]</span>
-
- <span class="n">data_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
- <span class="n">data</span><span class="p">,</span>
- <span class="n">columns</span><span class="o">=</span><span class="p">(</span>
- <span class="s2">"index"</span><span class="p">,</span>
- <span class="s2">"threshold"</span><span class="p">,</span>
- <span class="s2">"precision"</span><span class="p">,</span>
- <span class="s2">"recall"</span><span class="p">,</span>
- <span class="s2">"specificity"</span><span class="p">,</span>
- <span class="s2">"accuracy"</span><span class="p">,</span>
- <span class="s2">"jaccard"</span><span class="p">,</span>
- <span class="s2">"f1_score"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">)</span>
- <span class="n">data_df</span> <span class="o">=</span> <span class="n">data_df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"index"</span><span class="p">)</span>
-
- <span class="c1"># Save evaluation csv</span>
- <span class="k">if</span> <span class="n">output_folder</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">fullpath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.csv"</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Saving </span><span class="si">{</span><span class="n">fullpath</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">fullpath</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="n">data_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">fullpath</span><span class="p">)</span>
-
- <span class="c1"># Find max F1 score</span>
- <span class="n">f1_scores</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data_df</span><span class="p">[</span><span class="s2">"f1_score"</span><span class="p">])</span>
- <span class="n">thresholds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data_df</span><span class="p">[</span><span class="s2">"threshold"</span><span class="p">])</span>
-
- <span class="n">maxf1</span><span class="p">,</span> <span class="n">maxf1_threshold</span> <span class="o">=</span> <span class="n">get_centered_maxf1</span><span class="p">(</span><span class="n">f1_scores</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">)</span>
-
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Maximum F1-score of </span><span class="si">{</span><span class="n">maxf1</span><span class="si">:</span><span class="s2">.5f</span><span class="si">}</span><span class="s2">, achieved at "</span>
- <span class="sa">f</span><span class="s2">"threshold </span><span class="si">{</span><span class="n">maxf1_threshold</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> (chosen *a posteriori*)"</span>
- <span class="p">)</span>
-
- <span class="c1"># Find EER</span>
- <span class="n">neg_gt</span> <span class="o">=</span> <span class="n">pred_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pred_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">"ground_truth"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
- <span class="n">pos_gt</span> <span class="o">=</span> <span class="n">pred_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pred_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">"ground_truth"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
- <span class="n">post_eer_threshold</span> <span class="o">=</span> <span class="n">eer_threshold</span><span class="p">(</span>
- <span class="n">neg_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">],</span> <span class="n">pos_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span>
- <span class="p">)</span>
-
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Equal error rate achieved at "</span>
- <span class="sa">f</span><span class="s2">"threshold </span><span class="si">{</span><span class="n">post_eer_threshold</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> (chosen *a posteriori*)"</span>
- <span class="p">)</span>
-
- <span class="c1"># Save score table</span>
- <span class="k">if</span> <span class="n">output_folder</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
- <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">pad</span><span class="o">=</span><span class="mf">3.0</span><span class="p">)</span>
-
- <span class="c1"># Names and bounds</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Normalized counts"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
-
- <span class="n">neg_weights</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">neg_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span>
- <span class="n">pred_data</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="n">pos_weights</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">pos_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span>
- <span class="n">pred_data</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span>
- <span class="p">)</span>
-
- <span class="n">axes</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span>
- <span class="p">[</span><span class="n">neg_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">],</span> <span class="n">pos_gt</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]],</span>
- <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="n">neg_weights</span><span class="p">,</span> <span class="n">pos_weights</span><span class="p">],</span>
- <span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
- <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">"tab:blue"</span><span class="p">,</span> <span class="s2">"tab:orange"</span><span class="p">],</span>
- <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s2">"Negatives"</span><span class="p">,</span> <span class="s2">"Positives"</span><span class="p">],</span>
- <span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="p">{</span><span class="s2">"size"</span><span class="p">:</span> <span class="mi">10</span><span class="p">},</span> <span class="n">loc</span><span class="o">=</span><span class="s2">"upper center"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Score table for </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> subset"</span><span class="p">)</span>
-
- <span class="c1"># we should see some of axes 1 axes</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"right"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"top"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"left"</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">"data"</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.015</span><span class="p">))</span>
-
- <span class="n">fullpath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">_score_table.pdf"</span><span class="p">)</span>
- <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fullpath</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">f1_thresh</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">eer_thresh</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="c1"># get the closest possible threshold we have</span>
- <span class="n">index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">steps</span> <span class="o">*</span> <span class="n">f1_thresh</span><span class="p">))</span>
- <span class="n">f1_a_priori</span> <span class="o">=</span> <span class="n">data_df</span><span class="p">[</span><span class="s2">"f1_score"</span><span class="p">][</span><span class="n">index</span><span class="p">]</span>
- <span class="n">actual_threshold</span> <span class="o">=</span> <span class="n">data_df</span><span class="p">[</span><span class="s2">"threshold"</span><span class="p">][</span><span class="n">index</span><span class="p">]</span>
-
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"F1-score of </span><span class="si">{</span><span class="n">f1_a_priori</span><span class="si">:</span><span class="s2">.5f</span><span class="si">}</span><span class="s2">, at threshold "</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">actual_threshold</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> (chosen *a priori*)"</span>
- <span class="p">)</span>
-
- <span class="c1"># Print the a priori EER threshold</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Equal error rate (chosen *a priori*) </span><span class="si">{</span><span class="n">eer_thresh</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">maxf1_threshold</span><span class="p">,</span> <span class="n">post_eer_threshold</span></div>
-</pre></div>
- </article>
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- <h1>Source code for ptbench.engine.predictor</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">csv</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">import</span> <span class="nn">time</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">PIL</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">matplotlib.gridspec</span> <span class="kn">import</span> <span class="n">GridSpec</span>
-<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Rectangle</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="kn">from</span> <span class="nn">..utils.grad_cams</span> <span class="kn">import</span> <span class="n">GradCAM</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span>
- <span class="p">[(</span><span class="mi">47</span><span class="p">,</span> <span class="mi">79</span><span class="p">,</span> <span class="mi">79</span><span class="p">),</span> <span class="s2">"Cardiomegaly"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">"Emphysema"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">"Pleural effusion"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="s2">"Hernia"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">84</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">"Infiltration"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">222</span><span class="p">,</span> <span class="mi">184</span><span class="p">,</span> <span class="mi">135</span><span class="p">),</span> <span class="s2">"Mass"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">"Nodule"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">191</span><span class="p">,</span> <span class="mi">255</span><span class="p">),</span> <span class="s2">"Atelectasis"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">),</span> <span class="s2">"Pneumothorax"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">),</span> <span class="s2">"Pleural thickening"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">"Pneumonia"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">126</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">),</span> <span class="s2">"Fibrosis"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">147</span><span class="p">),</span> <span class="s2">"Edema"</span><span class="p">],</span>
- <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">180</span><span class="p">),</span> <span class="s2">"Consolidation"</span><span class="p">],</span>
-<span class="p">]</span>
-
-
-<div class="viewcode-block" id="run"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.predictor.html#ptbench.engine.predictor.run">[docs]</a><span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">output_folder</span><span class="p">,</span> <span class="n">grad_cams</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Runs inference on input data, outputs HDF5 files with predictions.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ---------</span>
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> neural network model (e.g. pasa)</span>
-
-<span class="sd"> data_loader : py:class:`torch.torch.utils.data.DataLoader`</span>
-
-<span class="sd"> name : str</span>
-<span class="sd"> the local name of this dataset (e.g. ``train``, or ``test``), to be</span>
-<span class="sd"> used when saving measures files.</span>
-
-<span class="sd"> device : str</span>
-<span class="sd"> device to use ``cpu`` or ``cuda:0``</span>
-
-<span class="sd"> output_folder : str</span>
-<span class="sd"> folder where to store output prediction and model</span>
-<span class="sd"> summary</span>
-
-<span class="sd"> grad_cams : bool</span>
-<span class="sd"> if we export grad cams for every prediction (must be used along</span>
-<span class="sd"> a batch size of 1 with the DensenetRS model)</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> all_predictions : list</span>
-<span class="sd"> All the predictions associated with filename and groundtruth</span>
-<span class="sd"> """</span>
- <span class="n">output_folder</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
-
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Output folder: </span><span class="si">{</span><span class="n">output_folder</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Device: </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
- <span class="n">logfile_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="s2">"predictions.csv"</span><span class="p">)</span>
- <span class="n">logfile_fields</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"filename"</span><span class="p">,</span> <span class="s2">"likelihood"</span><span class="p">,</span> <span class="s2">"ground_truth"</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">):</span>
- <span class="n">backup</span> <span class="o">=</span> <span class="n">logfile_name</span> <span class="o">+</span> <span class="s2">"~"</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">backup</span><span class="p">):</span>
- <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">backup</span><span class="p">)</span>
- <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="n">backup</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">grad_cams</span><span class="p">:</span>
- <span class="n">grad_folder</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="s2">"cams"</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grad cams folder: </span><span class="si">{</span><span class="n">grad_folder</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">grad_folder</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="s2">"a+"</span><span class="p">,</span> <span class="n">newline</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">as</span> <span class="n">logfile</span><span class="p">:</span>
- <span class="n">logwriter</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">DictWriter</span><span class="p">(</span><span class="n">logfile</span><span class="p">,</span> <span class="n">fieldnames</span><span class="o">=</span><span class="n">logfile_fields</span><span class="p">)</span>
-
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writeheader</span><span class="p">()</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> <span class="c1"># set evaluation mode</span>
- <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="c1"># set/cast parameters to device</span>
-
- <span class="c1"># Setup timers</span>
- <span class="n">start_total_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
- <span class="n">times</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">len_samples</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="n">all_predictions</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">for</span> <span class="n">samples</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
- <span class="n">data_loader</span><span class="p">,</span>
- <span class="n">desc</span><span class="o">=</span><span class="s2">"batches"</span><span class="p">,</span>
- <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">disable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
- <span class="p">):</span>
- <span class="n">names</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">images</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
- <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
- <span class="p">)</span>
-
- <span class="c1"># Gradcams generation</span>
- <span class="n">allowed_models</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"DensenetRS"</span><span class="p">]</span>
- <span class="k">if</span> <span class="n">grad_cams</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">allowed_models</span><span class="p">:</span>
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">GradCAM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
- <span class="n">probs</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
-
- <span class="c1"># To store signs overlays</span>
- <span class="n">cams_img</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-
- <span class="c1"># Top k number of radiological signs for which we generate cams</span>
- <span class="n">topk</span> <span class="o">=</span> <span class="mi">1</span>
-
- <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">topk</span><span class="p">):</span>
- <span class="c1"># Keep only "positive" signs</span>
- <span class="k">if</span> <span class="n">probs</span><span class="p">[:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span>
- <span class="c1"># Grad-CAM</span>
- <span class="n">b</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]]</span>
- <span class="n">gcam</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">[:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]])</span>
- <span class="n">regions</span> <span class="o">=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
- <span class="n">target_layer</span><span class="o">=</span><span class="s2">"model_ft.features.denseblock4.denselayer16.conv2"</span>
- <span class="p">)</span>
-
- <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">)):</span>
- <span class="n">current_cam</span> <span class="o">=</span> <span class="n">regions</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">current_cam</span><span class="p">[</span><span class="n">current_cam</span> <span class="o"><</span> <span class="mf">0.75</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
- <span class="n">current_cam</span><span class="p">[</span><span class="n">current_cam</span> <span class="o">>=</span> <span class="mf">0.75</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
- <span class="n">current_cam</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">current_cam</span> <span class="o">*</span> <span class="mi">255</span><span class="p">),</span> <span class="s2">"L"</span>
- <span class="p">)</span>
- <span class="n">cams_img</span><span class="p">[</span><span class="n">b</span><span class="o">.</span><span class="n">item</span><span class="p">()]</span> <span class="o">=</span> <span class="p">[</span>
- <span class="n">current_cam</span><span class="p">,</span>
- <span class="nb">round</span><span class="p">(</span><span class="n">probs</span><span class="p">[:,</span> <span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">2</span><span class="p">),</span>
- <span class="p">]</span>
-
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cams_img</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
- <span class="c1"># Convert original image tensor into PIL Image</span>
- <span class="n">original_image</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToPILImage</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s2">"RGB"</span><span class="p">)(</span>
- <span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="p">)</span>
-
- <span class="k">for</span> <span class="n">sign_id</span><span class="p">,</span> <span class="n">label_prob</span> <span class="ow">in</span> <span class="n">cams_img</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">label</span> <span class="o">=</span> <span class="n">label_prob</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="c1"># Create the colored overlay for current sign</span>
- <span class="n">colored_sign</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">ImageOps</span><span class="o">.</span><span class="n">colorize</span><span class="p">(</span>
- <span class="n">label</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"L"</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">colors</span><span class="p">[</span><span class="n">sign_id</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
- <span class="p">)</span>
-
- <span class="c1"># blend image and label together - first blend to get signs drawn with a</span>
- <span class="c1"># slight "label_color" tone on top, then composite with original image, to</span>
- <span class="c1"># avoid loosing brightness.</span>
- <span class="n">retval</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">blend</span><span class="p">(</span>
- <span class="n">original_image</span><span class="p">,</span> <span class="n">colored_sign</span><span class="p">,</span> <span class="mf">0.5</span>
- <span class="p">)</span>
- <span class="n">composite_mask</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">ImageOps</span><span class="o">.</span><span class="n">invert</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">"L"</span><span class="p">))</span>
- <span class="n">original_image</span> <span class="o">=</span> <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">composite</span><span class="p">(</span>
- <span class="n">original_image</span><span class="p">,</span> <span class="n">retval</span><span class="p">,</span> <span class="n">composite_mask</span>
- <span class="p">)</span>
-
- <span class="n">handles</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">colors</span><span class="p">):</span>
- <span class="c1"># If sign present on image</span>
- <span class="k">if</span> <span class="n">cams_img</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">Rectangle</span><span class="p">(</span>
- <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
- <span class="mi">1</span><span class="p">,</span>
- <span class="mi">1</span><span class="p">,</span>
- <span class="n">color</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="o">/</span> <span class="mi">255</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
- <span class="p">)</span>
- <span class="p">)</span>
- <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s2">" ("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">cams_img</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="s2">")"</span>
- <span class="p">)</span>
-
- <span class="n">gs</span> <span class="o">=</span> <span class="n">GridSpec</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
- <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
- <span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:])</span> <span class="c1"># For the plot</span>
- <span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:])</span> <span class="c1"># For the legend</span>
-
- <span class="n">ax1</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">original_image</span><span class="p">)</span>
- <span class="n">ax1</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
- <span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
- <span class="n">handles</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"expand"</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span>
- <span class="p">)</span>
- <span class="n">ax2</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
-
- <span class="n">original_filename</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"/"</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"."</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="n">cam_filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
- <span class="n">grad_folder</span><span class="p">,</span> <span class="n">original_filename</span> <span class="o">+</span> <span class="s2">"_cam.png"</span>
- <span class="p">)</span>
- <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">cam_filename</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
- <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
- <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
- <span class="n">probabilities</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
-
- <span class="c1"># necessary check for HED architecture that uses several outputs</span>
- <span class="c1"># for loss calculation instead of just the last concatfuse block</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
- <span class="n">outputs</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
- <span class="c1"># predictions = sigmoid(outputs)</span>
-
- <span class="n">batch_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span>
- <span class="n">times</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch_time</span><span class="p">)</span>
- <span class="n">len_samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">))</span>
-
- <span class="n">logdata</span> <span class="o">=</span> <span class="p">(</span>
- <span class="p">(</span><span class="s2">"filename"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span>
- <span class="p">(</span>
- <span class="s2">"likelihood"</span><span class="p">,</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">probabilities</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">(</span>
- <span class="s2">"ground_truth"</span><span class="p">,</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">)</span>
-
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writerow</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">))</span>
- <span class="n">logfile</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
- <span class="n">tqdm</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">" | "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">[:</span><span class="mi">4</span><span class="p">]]))</span>
-
- <span class="c1"># Keep prediction for relevance analysis</span>
- <span class="n">all_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="p">[</span>
- <span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
- <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">probabilities</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
- <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
- <span class="p">]</span>
- <span class="p">)</span>
-
- <span class="c1"># report operational summary</span>
- <span class="n">total_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span>
- <span class="n">seconds</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_total_time</span><span class="p">)</span>
- <span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total time: </span><span class="si">{</span><span class="n">total_time</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
- <span class="n">average_batch_time</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">times</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average batch time: </span><span class="si">{</span><span class="n">average_batch_time</span><span class="si">:</span><span class="s2">g</span><span class="si">}</span><span class="s2">s"</span><span class="p">)</span>
-
- <span class="n">average_image_time</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">times</span><span class="p">)</span> <span class="o">*</span> <span class="n">len_samples</span>
- <span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">len_samples</span><span class="p">))</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average image time: </span><span class="si">{</span><span class="n">average_image_time</span><span class="si">:</span><span class="s2">g</span><span class="si">}</span><span class="s2">s"</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">all_predictions</span></div>
-</pre></div>
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
-
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- <div class="bottom-of-page">
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- Copyright © 2023, Idiap Research Institute
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- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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diff --git a/sphinx/_modules/ptbench/engine/trainer.html b/sphinx/_modules/ptbench/engine/trainer.html
deleted file mode 100644
index 107f5c753418c7623bc523c664759707ae6eb564..0000000000000000000000000000000000000000
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-<html class="no-js" lang="en">
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- --color-code-background: #eeffcc;
- --color-code-foreground: black;
-
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-
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- body:not([data-theme="light"]) {
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- <h1>Source code for ptbench.engine.trainer</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">contextlib</span>
-<span class="kn">import</span> <span class="nn">csv</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">time</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="c1"># from ..utils.resources import cpu_constants, gpu_constants, cpu_log, gpu_log</span>
-<span class="kn">from</span> <span class="nn">..utils.resources</span> <span class="kn">import</span> <span class="n">ResourceMonitor</span><span class="p">,</span> <span class="n">cpu_constants</span><span class="p">,</span> <span class="n">gpu_constants</span>
-<span class="kn">from</span> <span class="nn">..utils.summary</span> <span class="kn">import</span> <span class="n">summary</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="torch_evaluation"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.torch_evaluation">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
-<span class="k">def</span> <span class="nf">torch_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Context manager to turn ON/OFF model evaluation.</span>
-
-<span class="sd"> This context manager will turn evaluation mode ON on entry and turn it OFF</span>
-<span class="sd"> when exiting the ``with`` statement block.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network</span>
-
-
-<span class="sd"> Yields</span>
-<span class="sd"> ------</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network</span>
-<span class="sd"> """</span>
- <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
- <span class="k">yield</span> <span class="n">model</span>
- <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="check_gpu"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.check_gpu">[docs]</a><span class="k">def</span> <span class="nf">check_gpu</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Check the device type and the availability of GPU.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> device : :py:class:`torch.device`</span>
-<span class="sd"> device to use</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"cuda"</span><span class="p">:</span>
- <span class="c1"># asserts we do have a GPU</span>
- <span class="k">assert</span> <span class="nb">bool</span><span class="p">(</span>
- <span class="n">gpu_constants</span><span class="p">()</span>
- <span class="p">),</span> <span class="sa">f</span><span class="s2">"Device set to '</span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2">', but nvidia-smi is not installed"</span></div>
-
-
-<div class="viewcode-block" id="save_model_summary"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.save_model_summary">[docs]</a><span class="k">def</span> <span class="nf">save_model_summary</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Save a little summary of the model in a txt file.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> output_folder : str</span>
-<span class="sd"> output path</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network (e.g. driu, hed, unet)</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-<span class="sd"> r : str</span>
-<span class="sd"> The model summary in a text format.</span>
-
-<span class="sd"> n : int</span>
-<span class="sd"> The number of parameters of the model.</span>
-<span class="sd"> """</span>
- <span class="n">summary_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="s2">"model_summary.txt"</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Saving model summary at </span><span class="si">{</span><span class="n">summary_path</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">summary_path</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">r</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">summary</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Model has </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2"> parameters..."</span><span class="p">)</span>
- <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">r</span><span class="p">,</span> <span class="n">n</span></div>
-
-
-<div class="viewcode-block" id="static_information_to_csv"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.static_information_to_csv">[docs]</a><span class="k">def</span> <span class="nf">static_information_to_csv</span><span class="p">(</span><span class="n">static_logfile_name</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Save the static information in a csv file.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> static_logfile_name : str</span>
-<span class="sd"> The static file name which is a join between the output folder and "constant.csv"</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">static_logfile_name</span><span class="p">):</span>
- <span class="n">backup</span> <span class="o">=</span> <span class="n">static_logfile_name</span> <span class="o">+</span> <span class="s2">"~"</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">backup</span><span class="p">):</span>
- <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">backup</span><span class="p">)</span>
- <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">static_logfile_name</span><span class="p">,</span> <span class="n">backup</span><span class="p">)</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">static_logfile_name</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">,</span> <span class="n">newline</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">logdata</span> <span class="o">=</span> <span class="n">cpu_constants</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"cuda"</span><span class="p">:</span>
- <span class="n">logdata</span> <span class="o">+=</span> <span class="n">gpu_constants</span><span class="p">()</span>
- <span class="n">logdata</span> <span class="o">+=</span> <span class="p">((</span><span class="s2">"model_size"</span><span class="p">,</span> <span class="n">n</span><span class="p">),)</span>
- <span class="n">logwriter</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">DictWriter</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">fieldnames</span><span class="o">=</span><span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">])</span>
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writeheader</span><span class="p">()</span>
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writerow</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="check_exist_logfile"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.check_exist_logfile">[docs]</a><span class="k">def</span> <span class="nf">check_exist_logfile</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="n">arguments</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Check existance of logfile (trainlog.csv), If the logfile exist the and</span>
-<span class="sd"> the epochs number are still 0, The logfile will be replaced.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> logfile_name : str</span>
-<span class="sd"> The logfile_name which is a join between the output_folder and trainlog.csv</span>
-
-<span class="sd"> arguments : dict</span>
-<span class="sd"> start and end epochs</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">arguments</span><span class="p">[</span><span class="s2">"epoch"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">):</span>
- <span class="n">backup</span> <span class="o">=</span> <span class="n">logfile_name</span> <span class="o">+</span> <span class="s2">"~"</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">backup</span><span class="p">):</span>
- <span class="n">os</span><span class="o">.</span><span class="n">unlink</span><span class="p">(</span><span class="n">backup</span><span class="p">)</span>
- <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="n">backup</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="create_logfile_fields"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.create_logfile_fields">[docs]</a><span class="k">def</span> <span class="nf">create_logfile_fields</span><span class="p">(</span><span class="n">valid_loader</span><span class="p">,</span> <span class="n">extra_valid_loaders</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Creation of the logfile fields that will appear in the logfile.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> valid_loader : :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to validate the model and enable automatic checkpointing.</span>
-<span class="sd"> If set to ``None``, then do not validate it.</span>
-
-<span class="sd"> extra_valid_loaders : :py:class:`list` of :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to validate the model, however **does not affect** automatic</span>
-<span class="sd"> checkpointing. If set to ``None``, or empty, then does not log anything</span>
-<span class="sd"> else. Otherwise, an extra column with the loss of every dataset in</span>
-<span class="sd"> this list is kept on the final training log.</span>
-
-<span class="sd"> device : :py:class:`torch.device`</span>
-<span class="sd"> device to use</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> logfile_fields: tuple</span>
-<span class="sd"> The fields that will appear in trainlog.csv</span>
-<span class="sd"> """</span>
- <span class="n">logfile_fields</span> <span class="o">=</span> <span class="p">(</span>
- <span class="s2">"epoch"</span><span class="p">,</span>
- <span class="s2">"total_time"</span><span class="p">,</span>
- <span class="s2">"eta"</span><span class="p">,</span>
- <span class="s2">"loss"</span><span class="p">,</span>
- <span class="s2">"learning_rate"</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">valid_loader</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">logfile_fields</span> <span class="o">+=</span> <span class="p">(</span><span class="s2">"validation_loss"</span><span class="p">,)</span>
- <span class="k">if</span> <span class="n">extra_valid_loaders</span><span class="p">:</span>
- <span class="n">logfile_fields</span> <span class="o">+=</span> <span class="p">(</span><span class="s2">"extra_validation_losses"</span><span class="p">,)</span>
- <span class="n">logfile_fields</span> <span class="o">+=</span> <span class="nb">tuple</span><span class="p">(</span>
- <span class="n">ResourceMonitor</span><span class="o">.</span><span class="n">monitored_keys</span><span class="p">(</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"cuda"</span><span class="p">)</span>
- <span class="p">)</span>
- <span class="k">return</span> <span class="n">logfile_fields</span></div>
-
-
-<div class="viewcode-block" id="train_epoch"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.train_epoch">[docs]</a><span class="k">def</span> <span class="nf">train_epoch</span><span class="p">(</span><span class="n">loader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">batch_chunk_count</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Trains the model for a single epoch (through all batches)</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> loader : :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to train the model</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network (e.g. driu, hed, unet)</span>
-
-<span class="sd"> optimizer : :py:mod:`torch.optim`</span>
-
-<span class="sd"> device : :py:class:`torch.device`</span>
-<span class="sd"> device to use</span>
-
-<span class="sd"> criterion : :py:class:`torch.nn.modules.loss._Loss`</span>
-
-<span class="sd"> batch_chunk_count: int</span>
-<span class="sd"> If this number is different than 1, then each batch will be divided in</span>
-<span class="sd"> this number of chunks. Gradients will be accumulated to perform each</span>
-<span class="sd"> mini-batch. This is particularly interesting when one has limited RAM</span>
-<span class="sd"> on the GPU, but would like to keep training with larger batches. One</span>
-<span class="sd"> exchanges for longer processing times in this case. To better understand</span>
-<span class="sd"> gradient accumulation, read</span>
-<span class="sd"> https://stackoverflow.com/questions/62067400/understanding-accumulated-gradients-in-pytorch.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> loss : float</span>
-<span class="sd"> A floating-point value corresponding the weighted average of this</span>
-<span class="sd"> epoch's loss</span>
-<span class="sd"> """</span>
- <span class="n">losses_in_epoch</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">samples_in_epoch</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">losses_in_batch</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">samples_in_batch</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="c1"># progress bar only on interactive jobs</span>
- <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
- <span class="n">tqdm</span><span class="p">(</span><span class="n">loader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
- <span class="p">):</span>
- <span class="n">images</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
- <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
- <span class="p">)</span>
- <span class="n">labels</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
- <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
- <span class="p">)</span>
-
- <span class="c1"># Increase label dimension if too low</span>
- <span class="c1"># Allows single and multiclass usage</span>
- <span class="k">if</span> <span class="n">labels</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
-
- <span class="c1"># Forward pass on the network</span>
- <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
-
- <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="o">.</span><span class="n">double</span><span class="p">())</span>
-
- <span class="n">losses_in_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
- <span class="n">samples_in_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
-
- <span class="c1"># Normalize loss to account for batch accumulation</span>
- <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">batch_chunk_count</span>
-
- <span class="c1"># Accumulate gradients - does not update weights just yet...</span>
- <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-
- <span class="c1"># Weight update on the network</span>
- <span class="k">if</span> <span class="p">((</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">batch_chunk_count</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">loader</span><span class="p">)):</span>
- <span class="c1"># Advances optimizer to the "next" state and applies weight update</span>
- <span class="c1"># over the whole model</span>
- <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-
- <span class="c1"># Zeroes gradients for the next batch</span>
- <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-
- <span class="c1"># Normalize loss for current batch</span>
- <span class="n">batch_loss</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">average</span><span class="p">(</span>
- <span class="n">losses_in_batch</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">samples_in_batch</span>
- <span class="p">)</span>
- <span class="n">losses_in_epoch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch_loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
- <span class="n">samples_in_epoch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
-
- <span class="n">losses_in_batch</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
- <span class="n">samples_in_batch</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">"batch loss: </span><span class="si">{</span><span class="n">batch_loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">losses_in_epoch</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">samples_in_epoch</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="validate_epoch"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.validate_epoch">[docs]</a><span class="k">def</span> <span class="nf">validate_epoch</span><span class="p">(</span><span class="n">loader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">pbar_desc</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Processes input samples and returns loss (scalar)</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> loader : :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to validate the model</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network (e.g. driu, hed, unet)</span>
-
-<span class="sd"> optimizer : :py:mod:`torch.optim`</span>
-
-<span class="sd"> device : :py:class:`torch.device`</span>
-<span class="sd"> device to use</span>
-
-<span class="sd"> criterion : :py:class:`torch.nn.modules.loss._Loss`</span>
-<span class="sd"> loss function</span>
-
-<span class="sd"> pbar_desc : str</span>
-<span class="sd"> A string for the progress bar descriptor</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> loss : float</span>
-<span class="sd"> A floating-point value corresponding the weighted average of this</span>
-<span class="sd"> epoch's loss</span>
-<span class="sd"> """</span>
- <span class="n">batch_losses</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">samples_in_batch</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">(),</span> <span class="n">torch_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
- <span class="k">for</span> <span class="n">samples</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">loader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="n">pbar_desc</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
- <span class="n">images</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
- <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
- <span class="n">non_blocking</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">(),</span>
- <span class="p">)</span>
- <span class="n">labels</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
- <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
- <span class="n">non_blocking</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">(),</span>
- <span class="p">)</span>
-
- <span class="c1"># Increase label dimension if too low</span>
- <span class="c1"># Allows single and multiclass usage</span>
- <span class="k">if</span> <span class="n">labels</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
-
- <span class="c1"># data forwarding on the existing network</span>
- <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
- <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="o">.</span><span class="n">double</span><span class="p">())</span>
-
- <span class="n">batch_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
- <span class="n">samples_in_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
-
- <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">batch_losses</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">samples_in_batch</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="checkpointer_process"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.checkpointer_process">[docs]</a><span class="k">def</span> <span class="nf">checkpointer_process</span><span class="p">(</span>
- <span class="n">checkpointer</span><span class="p">,</span>
- <span class="n">checkpoint_period</span><span class="p">,</span>
- <span class="n">valid_loss</span><span class="p">,</span>
- <span class="n">lowest_validation_loss</span><span class="p">,</span>
- <span class="n">arguments</span><span class="p">,</span>
- <span class="n">epoch</span><span class="p">,</span>
- <span class="n">max_epoch</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Process the checkpointer, save the final model and keep track of the</span>
-<span class="sd"> best model.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> checkpointer : :py:class:`ptbench.utils.checkpointer.Checkpointer`</span>
-<span class="sd"> checkpointer implementation</span>
-
-<span class="sd"> checkpoint_period : int</span>
-<span class="sd"> save a checkpoint every ``n`` epochs. If set to ``0`` (zero), then do</span>
-<span class="sd"> not save intermediary checkpoints</span>
-
-<span class="sd"> valid_loss : float</span>
-<span class="sd"> Current epoch validation loss</span>
-
-<span class="sd"> lowest_validation_loss : float</span>
-<span class="sd"> Keeps track of the best (lowest) validation loss</span>
-
-<span class="sd"> arguments : dict</span>
-<span class="sd"> start and end epochs</span>
-
-<span class="sd"> max_epoch : int</span>
-<span class="sd"> end_potch</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> lowest_validation_loss : float</span>
-<span class="sd"> The lowest validation loss currently observed</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">checkpoint_period</span> <span class="ow">and</span> <span class="p">(</span><span class="n">epoch</span> <span class="o">%</span> <span class="n">checkpoint_period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
- <span class="n">checkpointer</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"model_periodic_save"</span><span class="p">,</span> <span class="o">**</span><span class="n">arguments</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">valid_loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">valid_loss</span> <span class="o"><</span> <span class="n">lowest_validation_loss</span><span class="p">:</span>
- <span class="n">lowest_validation_loss</span> <span class="o">=</span> <span class="n">valid_loss</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Found new low on validation set:"</span> <span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">lowest_validation_loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span>
- <span class="p">)</span>
- <span class="n">checkpointer</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"model_lowest_valid_loss"</span><span class="p">,</span> <span class="o">**</span><span class="n">arguments</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">epoch</span> <span class="o">>=</span> <span class="n">max_epoch</span><span class="p">:</span>
- <span class="n">checkpointer</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"model_final_epoch"</span><span class="p">,</span> <span class="o">**</span><span class="n">arguments</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">lowest_validation_loss</span></div>
-
-
-<div class="viewcode-block" id="write_log_info"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.write_log_info">[docs]</a><span class="k">def</span> <span class="nf">write_log_info</span><span class="p">(</span>
- <span class="n">epoch</span><span class="p">,</span>
- <span class="n">current_time</span><span class="p">,</span>
- <span class="n">eta_seconds</span><span class="p">,</span>
- <span class="n">loss</span><span class="p">,</span>
- <span class="n">valid_loss</span><span class="p">,</span>
- <span class="n">extra_valid_losses</span><span class="p">,</span>
- <span class="n">optimizer</span><span class="p">,</span>
- <span class="n">logwriter</span><span class="p">,</span>
- <span class="n">logfile</span><span class="p">,</span>
- <span class="n">resource_data</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Write log info in trainlog.csv.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> epoch : int</span>
-<span class="sd"> Current epoch</span>
-
-<span class="sd"> current_time : float</span>
-<span class="sd"> Current training time</span>
-
-<span class="sd"> eta_seconds : float</span>
-<span class="sd"> estimated time-of-arrival taking into consideration previous epoch performance</span>
-
-<span class="sd"> loss : float</span>
-<span class="sd"> Current epoch's training loss</span>
-
-<span class="sd"> valid_loss : :py:class:`float`, None</span>
-<span class="sd"> Current epoch's validation loss</span>
-
-<span class="sd"> extra_valid_losses : :py:class:`list` of :py:class:`float`</span>
-<span class="sd"> Validation losses from other validation datasets being currently</span>
-<span class="sd"> tracked</span>
-
-<span class="sd"> optimizer : :py:mod:`torch.optim`</span>
-
-<span class="sd"> logwriter : csv.DictWriter</span>
-<span class="sd"> Dictionary writer that give the ability to write on the trainlog.csv</span>
-
-<span class="sd"> logfile : io.TextIOWrapper</span>
-
-<span class="sd"> resource_data : tuple</span>
-<span class="sd"> Monitored resources at the machine (CPU and GPU)</span>
-<span class="sd"> """</span>
-
- <span class="n">logdata</span> <span class="o">=</span> <span class="p">(</span>
- <span class="p">(</span><span class="s2">"epoch"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span>
- <span class="p">(</span>
- <span class="s2">"total_time"</span><span class="p">,</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">current_time</span><span class="p">))</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">(</span><span class="s2">"eta"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">eta_seconds</span><span class="p">))</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"loss"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"learning_rate"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'lr'</span><span class="p">]</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span>
- <span class="p">)</span>
-
- <span class="k">if</span> <span class="n">valid_loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">logdata</span> <span class="o">+=</span> <span class="p">((</span><span class="s2">"validation_loss"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">valid_loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span><span class="p">),)</span>
-
- <span class="k">if</span> <span class="n">extra_valid_losses</span><span class="p">:</span>
- <span class="n">entry</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array_str</span><span class="p">(</span>
- <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">extra_valid_losses</span><span class="p">),</span>
- <span class="n">max_line_width</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span><span class="p">,</span>
- <span class="n">precision</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="n">logdata</span> <span class="o">+=</span> <span class="p">((</span><span class="s2">"extra_validation_losses"</span><span class="p">,</span> <span class="n">entry</span><span class="p">),)</span>
-
- <span class="n">logdata</span> <span class="o">+=</span> <span class="n">resource_data</span>
-
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writerow</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">))</span>
- <span class="n">logfile</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
- <span class="n">tqdm</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"|"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">logdata</span><span class="p">[:</span><span class="mi">4</span><span class="p">]]))</span></div>
-
-
-<div class="viewcode-block" id="run"><a class="viewcode-back" href="../../../api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.run">[docs]</a><span class="k">def</span> <span class="nf">run</span><span class="p">(</span>
- <span class="n">model</span><span class="p">,</span>
- <span class="n">data_loader</span><span class="p">,</span>
- <span class="n">valid_loader</span><span class="p">,</span>
- <span class="n">extra_valid_loaders</span><span class="p">,</span>
- <span class="n">optimizer</span><span class="p">,</span>
- <span class="n">criterion</span><span class="p">,</span>
- <span class="n">checkpointer</span><span class="p">,</span>
- <span class="n">checkpoint_period</span><span class="p">,</span>
- <span class="n">device</span><span class="p">,</span>
- <span class="n">arguments</span><span class="p">,</span>
- <span class="n">output_folder</span><span class="p">,</span>
- <span class="n">monitoring_interval</span><span class="p">,</span>
- <span class="n">batch_chunk_count</span><span class="p">,</span>
- <span class="n">criterion_valid</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Fits a CNN model using supervised learning and save it to disk.</span>
-
-<span class="sd"> This method supports periodic checkpointing and the output of a</span>
-<span class="sd"> CSV-formatted log with the evolution of some figures during training.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> Network (e.g. driu, hed, unet)</span>
-
-<span class="sd"> data_loader : :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to train the model</span>
-
-<span class="sd"> valid_loaders : :py:class:`list` of :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to validate the model and enable automatic checkpointing.</span>
-<span class="sd"> If ``None``, then do not validate it.</span>
-
-<span class="sd"> extra_valid_loaders : :py:class:`list` of :py:class:`torch.utils.data.DataLoader`</span>
-<span class="sd"> To be used to validate the model, however **does not affect** automatic</span>
-<span class="sd"> checkpointing. If empty, then does not log anything else. Otherwise,</span>
-<span class="sd"> an extra column with the loss of every dataset in this list is kept on</span>
-<span class="sd"> the final training log.</span>
-
-<span class="sd"> optimizer : :py:mod:`torch.optim`</span>
-
-<span class="sd"> criterion : :py:class:`torch.nn.modules.loss._Loss`</span>
-<span class="sd"> loss function</span>
-
-<span class="sd"> checkpointer : :py:class:`ptbench.utils.checkpointer.Checkpointer`</span>
-<span class="sd"> checkpointer implementation</span>
-
-<span class="sd"> checkpoint_period : int</span>
-<span class="sd"> save a checkpoint every ``n`` epochs. If set to ``0`` (zero), then do</span>
-<span class="sd"> not save intermediary checkpoints</span>
-
-<span class="sd"> device : :py:class:`torch.device`</span>
-<span class="sd"> device to use</span>
-
-<span class="sd"> arguments : dict</span>
-<span class="sd"> start and end epochs</span>
-
-<span class="sd"> output_folder : str</span>
-<span class="sd"> output path</span>
-
-<span class="sd"> monitoring_interval : int, float</span>
-<span class="sd"> interval, in seconds (or fractions), through which we should monitor</span>
-<span class="sd"> resources during training.</span>
-
-<span class="sd"> batch_chunk_count: int</span>
-<span class="sd"> If this number is different than 1, then each batch will be divided in</span>
-<span class="sd"> this number of chunks. Gradients will be accumulated to perform each</span>
-<span class="sd"> mini-batch. This is particularly interesting when one has limited RAM</span>
-<span class="sd"> on the GPU, but would like to keep training with larger batches. One</span>
-<span class="sd"> exchanges for longer processing times in this case.</span>
-
-<span class="sd"> criterion_valid : :py:class:`torch.nn.modules.loss._Loss`</span>
-<span class="sd"> specific loss function for the validation set</span>
-<span class="sd"> """</span>
-
- <span class="n">start_epoch</span> <span class="o">=</span> <span class="n">arguments</span><span class="p">[</span><span class="s2">"epoch"</span><span class="p">]</span>
- <span class="n">max_epoch</span> <span class="o">=</span> <span class="n">arguments</span><span class="p">[</span><span class="s2">"max_epoch"</span><span class="p">]</span>
-
- <span class="n">check_gpu</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
- <span class="c1"># Save model summary</span>
- <span class="n">r</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">save_model_summary</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
-
- <span class="c1"># write static information to a CSV file</span>
- <span class="n">static_logfile_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="s2">"constants.csv"</span><span class="p">)</span>
-
- <span class="n">static_information_to_csv</span><span class="p">(</span><span class="n">static_logfile_name</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
-
- <span class="c1"># Log continous information to (another) file</span>
- <span class="n">logfile_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_folder</span><span class="p">,</span> <span class="s2">"trainlog.csv"</span><span class="p">)</span>
-
- <span class="n">check_exist_logfile</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="n">arguments</span><span class="p">)</span>
-
- <span class="n">logfile_fields</span> <span class="o">=</span> <span class="n">create_logfile_fields</span><span class="p">(</span>
- <span class="n">valid_loader</span><span class="p">,</span> <span class="n">extra_valid_loaders</span><span class="p">,</span> <span class="n">device</span>
- <span class="p">)</span>
-
- <span class="c1"># the lowest validation loss obtained so far - this value is updated only</span>
- <span class="c1"># if a validation set is available</span>
- <span class="n">lowest_validation_loss</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">float_info</span><span class="o">.</span><span class="n">max</span>
-
- <span class="c1"># set a specific validation criterion if the user has set one</span>
- <span class="n">criterion_valid</span> <span class="o">=</span> <span class="n">criterion_valid</span> <span class="ow">or</span> <span class="n">criterion</span>
-
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">logfile_name</span><span class="p">,</span> <span class="s2">"a+"</span><span class="p">,</span> <span class="n">newline</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span> <span class="k">as</span> <span class="n">logfile</span><span class="p">:</span>
- <span class="n">logwriter</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">DictWriter</span><span class="p">(</span><span class="n">logfile</span><span class="p">,</span> <span class="n">fieldnames</span><span class="o">=</span><span class="n">logfile_fields</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">arguments</span><span class="p">[</span><span class="s2">"epoch"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">logwriter</span><span class="o">.</span><span class="n">writeheader</span><span class="p">()</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span> <span class="c1"># set training mode</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="c1"># set/cast parameters to device</span>
- <span class="k">for</span> <span class="n">state</span> <span class="ow">in</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">state</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
- <span class="n">state</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
- <span class="c1"># Total training timer</span>
- <span class="n">start_training_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-
- <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
- <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">),</span>
- <span class="n">desc</span><span class="o">=</span><span class="s2">"epoch"</span><span class="p">,</span>
- <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">disable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
- <span class="p">):</span>
- <span class="k">with</span> <span class="n">ResourceMonitor</span><span class="p">(</span>
- <span class="n">interval</span><span class="o">=</span><span class="n">monitoring_interval</span><span class="p">,</span>
- <span class="n">has_gpu</span><span class="o">=</span><span class="p">(</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">"cuda"</span><span class="p">),</span>
- <span class="n">main_pid</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">getpid</span><span class="p">(),</span>
- <span class="n">logging_level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">ERROR</span><span class="p">,</span>
- <span class="p">)</span> <span class="k">as</span> <span class="n">resource_monitor</span><span class="p">:</span>
- <span class="n">epoch</span> <span class="o">=</span> <span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span>
- <span class="n">arguments</span><span class="p">[</span><span class="s2">"epoch"</span><span class="p">]</span> <span class="o">=</span> <span class="n">epoch</span>
-
- <span class="c1"># Epoch time</span>
- <span class="n">start_epoch_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-
- <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span>
- <span class="n">data_loader</span><span class="p">,</span>
- <span class="n">model</span><span class="p">,</span>
- <span class="n">optimizer</span><span class="p">,</span>
- <span class="n">device</span><span class="p">,</span>
- <span class="n">criterion</span><span class="p">,</span>
- <span class="n">batch_chunk_count</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="n">valid_loss</span> <span class="o">=</span> <span class="p">(</span>
- <span class="n">validate_epoch</span><span class="p">(</span>
- <span class="n">valid_loader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">criterion_valid</span><span class="p">,</span> <span class="s2">"valid"</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">valid_loader</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
- <span class="k">else</span> <span class="kc">None</span>
- <span class="p">)</span>
-
- <span class="n">extra_valid_losses</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">pos</span><span class="p">,</span> <span class="n">extra_valid_loader</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">extra_valid_loaders</span><span class="p">):</span>
- <span class="n">loss</span> <span class="o">=</span> <span class="n">validate_epoch</span><span class="p">(</span>
- <span class="n">extra_valid_loader</span><span class="p">,</span>
- <span class="n">model</span><span class="p">,</span>
- <span class="n">device</span><span class="p">,</span>
- <span class="n">criterion_valid</span><span class="p">,</span>
- <span class="sa">f</span><span class="s2">"xval@</span><span class="si">{</span><span class="n">pos</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="n">extra_valid_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
-
- <span class="n">lowest_validation_loss</span> <span class="o">=</span> <span class="n">checkpointer_process</span><span class="p">(</span>
- <span class="n">checkpointer</span><span class="p">,</span>
- <span class="n">checkpoint_period</span><span class="p">,</span>
- <span class="n">valid_loss</span><span class="p">,</span>
- <span class="n">lowest_validation_loss</span><span class="p">,</span>
- <span class="n">arguments</span><span class="p">,</span>
- <span class="n">epoch</span><span class="p">,</span>
- <span class="n">max_epoch</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="c1"># computes ETA (estimated time-of-arrival; end of training) taking</span>
- <span class="c1"># into consideration previous epoch performance</span>
- <span class="n">epoch_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_epoch_time</span>
- <span class="n">eta_seconds</span> <span class="o">=</span> <span class="n">epoch_time</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_epoch</span> <span class="o">-</span> <span class="n">epoch</span><span class="p">)</span>
- <span class="n">current_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_training_time</span>
-
- <span class="n">write_log_info</span><span class="p">(</span>
- <span class="n">epoch</span><span class="p">,</span>
- <span class="n">current_time</span><span class="p">,</span>
- <span class="n">eta_seconds</span><span class="p">,</span>
- <span class="n">train_loss</span><span class="p">,</span>
- <span class="n">valid_loss</span><span class="p">,</span>
- <span class="n">extra_valid_losses</span><span class="p">,</span>
- <span class="n">optimizer</span><span class="p">,</span>
- <span class="n">logwriter</span><span class="p">,</span>
- <span class="n">logfile</span><span class="p">,</span>
- <span class="n">resource_monitor</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="n">total_training_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_training_time</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"Total training time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">total_training_time</span><span class="p">)</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="p">(</span><span class="n">total_training_time</span><span class="o">/</span><span class="n">max_epoch</span><span class="p">)</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">s in average per epoch)"</span>
- <span class="p">)</span></div>
-</pre></div>
- </article>
- </div>
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diff --git a/sphinx/_modules/ptbench/models/alexnet.html b/sphinx/_modules/ptbench/models/alexnet.html
deleted file mode 100644
index ad74d17099db529691347b6aadca5eeae76b640f..0000000000000000000000000000000000000000
--- a/sphinx/_modules/ptbench/models/alexnet.html
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-<!doctype html>
-<html class="no-js" lang="en">
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- document.body.dataset.theme = localStorage.getItem("theme") || "auto";
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- <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
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- <polyline points="9 18 15 12 9 6"></polyline>
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- <h1>Source code for ptbench.models.alexnet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
-
-<span class="kn">from</span> <span class="nn">.normalizer</span> <span class="kn">import</span> <span class="n">TorchVisionNormalizer</span>
-
-
-<div class="viewcode-block" id="Alexnet"><a class="viewcode-back" href="../../../api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.Alexnet">[docs]</a><span class="k">class</span> <span class="nc">Alexnet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Alexnet module.</span>
-
-<span class="sd"> Note: only usable with a normalized dataset</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
- <span class="c1"># Load pretrained model</span>
- <span class="n">weights</span> <span class="o">=</span> <span class="p">(</span>
- <span class="kc">None</span> <span class="k">if</span> <span class="n">pretrained</span> <span class="ow">is</span> <span class="kc">False</span> <span class="k">else</span> <span class="n">models</span><span class="o">.</span><span class="n">AlexNet_Weights</span><span class="o">.</span><span class="n">DEFAULT</span>
- <span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
-
- <span class="c1"># Adapt output features</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Alexnet.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.Alexnet.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="build_alexnet"><a class="viewcode-back" href="../../../api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.build_alexnet">[docs]</a><span class="k">def</span> <span class="nf">build_alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Build Alexnet CNN.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">Alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">)</span>
- <span class="n">model</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"normalizer"</span><span class="p">,</span> <span class="n">TorchVisionNormalizer</span><span class="p">()),</span> <span class="p">(</span><span class="s2">"model"</span><span class="p">,</span> <span class="n">model</span><span class="p">)]</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"AlexNet"</span>
- <span class="k">return</span> <span class="n">model</span></div>
-</pre></div>
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- <h1>Source code for ptbench.models.densenet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
-
-<span class="kn">from</span> <span class="nn">.normalizer</span> <span class="kn">import</span> <span class="n">TorchVisionNormalizer</span>
-
-
-<div class="viewcode-block" id="Densenet"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet.html#ptbench.models.densenet.Densenet">[docs]</a><span class="k">class</span> <span class="nc">Densenet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Densenet module.</span>
-
-<span class="sd"> Note: only usable with a normalized dataset</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
- <span class="c1"># Load pretrained model</span>
- <span class="n">weights</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">pretrained</span> <span class="k">else</span> <span class="n">models</span><span class="o">.</span><span class="n">DenseNet121_Weights</span><span class="o">.</span><span class="n">DEFAULT</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
-
- <span class="c1"># Adapt output features</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
- <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
- <span class="p">)</span>
-
-<div class="viewcode-block" id="Densenet.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet.html#ptbench.models.densenet.Densenet.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="build_densenet"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet.html#ptbench.models.densenet.build_densenet">[docs]</a><span class="k">def</span> <span class="nf">build_densenet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">nb_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Build Densenet CNN.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">Densenet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">)</span>
- <span class="n">model</span> <span class="o">=</span> <span class="p">[</span>
- <span class="p">(</span><span class="s2">"normalizer"</span><span class="p">,</span> <span class="n">TorchVisionNormalizer</span><span class="p">(</span><span class="n">nb_channels</span><span class="o">=</span><span class="n">nb_channels</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"model"</span><span class="p">,</span> <span class="n">model</span><span class="p">),</span>
- <span class="p">]</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Densenet"</span>
- <span class="k">return</span> <span class="n">model</span></div>
-</pre></div>
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- <h1>Source code for ptbench.models.densenet_rs</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
-
-<span class="kn">from</span> <span class="nn">.normalizer</span> <span class="kn">import</span> <span class="n">TorchVisionNormalizer</span>
-
-
-<div class="viewcode-block" id="DensenetRS"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.DensenetRS">[docs]</a><span class="k">class</span> <span class="nc">DensenetRS</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Densenet121 module for radiological extraction."""</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
- <span class="c1"># Load pretrained model</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span>
- <span class="n">weights</span><span class="o">=</span><span class="n">models</span><span class="o">.</span><span class="n">DenseNet121_Weights</span><span class="o">.</span><span class="n">DEFAULT</span>
- <span class="p">)</span>
-
- <span class="c1"># Adapt output features</span>
- <span class="n">num_ftrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">in_features</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">num_ftrs</span><span class="p">,</span> <span class="mi">14</span><span class="p">)</span>
-
-<div class="viewcode-block" id="DensenetRS.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.DensenetRS.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_ft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="build_densenetrs"><a class="viewcode-back" href="../../../api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.build_densenetrs">[docs]</a><span class="k">def</span> <span class="nf">build_densenetrs</span><span class="p">():</span>
-<span class="w"> </span><span class="sd">"""Build DensenetRS CNN.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">DensenetRS</span><span class="p">()</span>
- <span class="n">model</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"normalizer"</span><span class="p">,</span> <span class="n">TorchVisionNormalizer</span><span class="p">()),</span> <span class="p">(</span><span class="s2">"model"</span><span class="p">,</span> <span class="n">model</span><span class="p">)]</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"DensenetRS"</span>
- <span class="k">return</span> <span class="n">model</span></div>
-</pre></div>
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- <h1>Source code for ptbench.models.logistic_regression</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-
-
-<div class="viewcode-block" id="LogisticRegression"><a class="viewcode-back" href="../../../api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.LogisticRegression">[docs]</a><span class="k">class</span> <span class="nc">LogisticRegression</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Radiological signs to Tuberculosis module."""</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-<div class="viewcode-block" id="LogisticRegression.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.LogisticRegression.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">output</span></div></div>
-
-
-<div class="viewcode-block" id="build_logistic_regression"><a class="viewcode-back" href="../../../api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.build_logistic_regression">[docs]</a><span class="k">def</span> <span class="nf">build_logistic_regression</span><span class="p">(</span><span class="n">input_size</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Build logistic regression module.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">input_size</span><span class="p">)</span>
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"logistic_regression"</span>
- <span class="k">return</span> <span class="n">model</span></div>
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- <h1>Source code for ptbench.models.normalizer</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="sd">"""A network model that prefixes a z-normalization step to any other module."""</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span>
-
-
-<div class="viewcode-block" id="TorchVisionNormalizer"><a class="viewcode-back" href="../../../api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer">[docs]</a><span class="k">class</span> <span class="nc">TorchVisionNormalizer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""A simple normalizer that applies the standard torchvision normalization.</span>
-
-<span class="sd"> This module does not learn.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> nb_channels : :py:class:`int`, Optional</span>
-<span class="sd"> Number of images channels fed to the model</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nb_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
- <span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nb_channels</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
- <span class="n">std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">nb_channels</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"mean"</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"std"</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"torchvision-normalizer"</span>
-
-<div class="viewcode-block" id="TorchVisionNormalizer.set_mean_std"><a class="viewcode-back" href="../../../api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer.set_mean_std">[docs]</a> <span class="k">def</span> <span class="nf">set_mean_std</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span>
- <span class="n">mean</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">mean</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
- <span class="n">std</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">std</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"mean"</span><span class="p">,</span> <span class="n">mean</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">"std"</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="TorchVisionNormalizer.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">inputs</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">std</span><span class="p">)</span></div></div>
-</pre></div>
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- <h1>Source code for ptbench.models.pasa</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
-
-<span class="kn">from</span> <span class="nn">.normalizer</span> <span class="kn">import</span> <span class="n">TorchVisionNormalizer</span>
-
-
-<div class="viewcode-block" id="PASA"><a class="viewcode-back" href="../../../api/models/ptbench.models.pasa.html#ptbench.models.pasa.PASA">[docs]</a><span class="k">class</span> <span class="nc">PASA</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""PASA module.</span>
-
-<span class="sd"> Based on paper by [PASA-2019]_.</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
- <span class="c1"># First convolution block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_16</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_16_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
-
- <span class="c1"># Second convolution block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc4</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc5</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">24</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc6</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># Original stride (2, 2)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_24</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">24</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_32</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_32_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
-
- <span class="c1"># Third convolution block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc7</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc8</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc9</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># Original stride (2, 2)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_40</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_48</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">48</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_48_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">48</span><span class="p">)</span>
-
- <span class="c1"># Fourth convolution block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc10</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">48</span><span class="p">,</span> <span class="mi">56</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc11</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">56</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc12</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">48</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># Original stride (2, 2)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_56</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">56</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_64</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_64_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span>
-
- <span class="c1"># Fifth convolution block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc13</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">72</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc14</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">72</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc15</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># Original stride (2, 2)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_72</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">72</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_80</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">80</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_80_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">80</span><span class="p">)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">pool2d</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="c1"># Pool after conv. block</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">80</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># Fully connected layer</span>
-
-<div class="viewcode-block" id="PASA.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.pasa.html#ptbench.models.pasa.PASA.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="c1"># First convolution block</span>
- <span class="n">_x</span> <span class="o">=</span> <span class="n">x</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_4</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 1st convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_16</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 2nd convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_16_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">_x</span><span class="p">))))</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># Parallel</span>
- <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Pooling</span>
-
- <span class="c1"># Second convolution block</span>
- <span class="n">_x</span> <span class="o">=</span> <span class="n">x</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_24</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc4</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 1st convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_32</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc5</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 2nd convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_32_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc6</span><span class="p">(</span><span class="n">_x</span><span class="p">))))</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># Parallel</span>
- <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Pooling</span>
-
- <span class="c1"># Third convolution block</span>
- <span class="n">_x</span> <span class="o">=</span> <span class="n">x</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_40</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc7</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 1st convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_48</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc8</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 2nd convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_48_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc9</span><span class="p">(</span><span class="n">_x</span><span class="p">))))</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># Parallel</span>
- <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Pooling</span>
-
- <span class="c1"># Fourth convolution block</span>
- <span class="n">_x</span> <span class="o">=</span> <span class="n">x</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_56</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc10</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 1st convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_64</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc11</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 2nd convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_64_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc12</span><span class="p">(</span><span class="n">_x</span><span class="p">))))</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># Parallel</span>
- <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Pooling</span>
-
- <span class="c1"># Fifth convolution block</span>
- <span class="n">_x</span> <span class="o">=</span> <span class="n">x</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_72</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc13</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 1st convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_80</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc14</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span> <span class="c1"># 2nd convolution</span>
- <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batchNorm2d_80_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc15</span><span class="p">(</span><span class="n">_x</span><span class="p">))))</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># Parallel</span>
- <span class="c1"># no pooling</span>
-
- <span class="c1"># Global average pooling</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
- <span class="c1"># Dense layer</span>
- <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
- <span class="c1"># x = F.log_softmax(x, dim=1) # 0 is batch size</span>
-
- <span class="k">return</span> <span class="n">x</span></div></div>
-
-
-<div class="viewcode-block" id="build_pasa"><a class="viewcode-back" href="../../../api/models/ptbench.models.pasa.html#ptbench.models.pasa.build_pasa">[docs]</a><span class="k">def</span> <span class="nf">build_pasa</span><span class="p">():</span>
-<span class="w"> </span><span class="sd">"""Build pasa CNN.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">PASA</span><span class="p">()</span>
- <span class="n">model</span> <span class="o">=</span> <span class="p">[</span>
- <span class="p">(</span><span class="s2">"normalizer"</span><span class="p">,</span> <span class="n">TorchVisionNormalizer</span><span class="p">(</span><span class="n">nb_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"model"</span><span class="p">,</span> <span class="n">model</span><span class="p">),</span>
- <span class="p">]</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
-
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"pasa"</span>
- <span class="k">return</span> <span class="n">model</span></div>
-</pre></div>
- </article>
- </div>
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-
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diff --git a/sphinx/_modules/ptbench/models/signs_to_tb.html b/sphinx/_modules/ptbench/models/signs_to_tb.html
deleted file mode 100644
index 2c86c049624bae330d889baf404263bc0c026a9a..0000000000000000000000000000000000000000
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- <h1>Source code for ptbench.models.signs_to_tb</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-
-
-<div class="viewcode-block" id="SignsToTB"><a class="viewcode-back" href="../../../api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.SignsToTB">[docs]</a><span class="k">class</span> <span class="nc">SignsToTB</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Radiological signs to Tuberculosis module."""</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">input_size</span> <span class="o">=</span> <span class="n">input_size</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-<div class="viewcode-block" id="SignsToTB.forward"><a class="viewcode-back" href="../../../api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.SignsToTB.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> x : list</span>
-<span class="sd"> list of tensors.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> tensor : :py:class:`torch.Tensor`</span>
-
-<span class="sd"> """</span>
- <span class="n">hidden</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
- <span class="n">relu</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">hidden</span><span class="p">)</span>
-
- <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">relu</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">output</span></div></div>
-
-
-<div class="viewcode-block" id="build_signs_to_tb"><a class="viewcode-back" href="../../../api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.build_signs_to_tb">[docs]</a><span class="k">def</span> <span class="nf">build_signs_to_tb</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Build SignsToTB shallow model.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> module : :py:class:`torch.nn.Module`</span>
-<span class="sd"> """</span>
- <span class="n">model</span> <span class="o">=</span> <span class="n">SignsToTB</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">)</span>
- <span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"signs_to_tb"</span>
- <span class="k">return</span> <span class="n">model</span></div>
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- <h1>Source code for ptbench.utils.checkpointer</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="Checkpointer"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer">[docs]</a><span class="k">class</span> <span class="nc">Checkpointer</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""A simple pytorch checkpointer.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> model : torch.nn.Module</span>
-<span class="sd"> Network model, eventually loaded from a checkpointed file</span>
-
-<span class="sd"> optimizer : :py:mod:`torch.optim`, Optional</span>
-<span class="sd"> Optimizer</span>
-
-<span class="sd"> scheduler : :py:mod:`torch.optim`, Optional</span>
-<span class="sd"> Learning rate scheduler</span>
-
-<span class="sd"> path : :py:class:`str`, Optional</span>
-<span class="sd"> Directory where to save checkpoints.</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scheduler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">"."</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">scheduler</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">realpath</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Checkpointer.save"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
- <span class="n">data</span><span class="p">[</span><span class="s2">"model"</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">data</span><span class="p">[</span><span class="s2">"optimizer"</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">data</span><span class="p">[</span><span class="s2">"scheduler"</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
- <span class="n">data</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
-
- <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.pth"</span>
- <span class="n">outf</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Saving checkpoint to </span><span class="si">{</span><span class="n">outf</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
- <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">outf</span><span class="p">)</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_last_checkpoint_filename</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
- <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Checkpointer.load"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.load">[docs]</a> <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Loads model, optimizer and scheduler from file.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ==========</span>
-
-<span class="sd"> f : :py:class:`str`, Optional</span>
-<span class="sd"> Name of a file (absolute or relative to ``self.path``), that</span>
-<span class="sd"> contains the checkpoint data to load into the model, and optionally</span>
-<span class="sd"> into the optimizer and the scheduler. If not specified, loads data</span>
-<span class="sd"> from current path.</span>
-
-<span class="sd"> partial : :py:class:`bool`, Optional</span>
-<span class="sd"> If True, loading is not strict and only the model is loaded</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">f</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_checkpoint</span><span class="p">()</span>
-
- <span class="k">if</span> <span class="n">f</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="c1"># no checkpoint could be found</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"No checkpoint found (and none passed)"</span><span class="p">)</span>
- <span class="k">return</span> <span class="p">{}</span>
-
- <span class="c1"># loads file data into memory</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Loading checkpoint from </span><span class="si">{</span><span class="n">f</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
- <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cpu"</span><span class="p">))</span>
-
- <span class="c1"># converts model entry to model parameters</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"model"</span><span class="p">),</span> <span class="n">strict</span><span class="o">=</span><span class="n">strict</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">strict</span><span class="p">:</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"optimizer"</span><span class="p">))</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"scheduler"</span><span class="p">))</span>
-
- <span class="k">return</span> <span class="n">checkpoint</span></div>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">_last_checkpoint_filename</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="s2">"last_checkpoint"</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Checkpointer.has_checkpoint"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.has_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">has_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_last_checkpoint_filename</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Checkpointer.last_checkpoint"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.last_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">last_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_checkpoint</span><span class="p">():</span>
- <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_last_checkpoint_filename</span><span class="p">)</span> <span class="k">as</span> <span class="n">fobj</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="n">fobj</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
- <span class="k">return</span> <span class="kc">None</span></div></div>
-</pre></div>
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- <h1>Source code for ptbench.utils.download</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">tempfile</span>
-<span class="kn">import</span> <span class="nn">urllib.request</span>
-
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="download_to_tempfile"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.download.html#ptbench.utils.download.download_to_tempfile">[docs]</a><span class="k">def</span> <span class="nf">download_to_tempfile</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Downloads a file to a temporary named file and returns it.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> url : str</span>
-<span class="sd"> The URL pointing to the file to download</span>
-
-<span class="sd"> progress : :py:class:`bool`, Optional</span>
-<span class="sd"> If a progress bar should be displayed for downloading the URL.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> f : :py:func:`tempfile.NamedTemporaryFile`</span>
-<span class="sd"> A named temporary file that contains the downloaded URL</span>
-<span class="sd"> """</span>
- <span class="n">file_size</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="n">response</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
- <span class="n">meta</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
- <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="s2">"getheaders"</span><span class="p">):</span>
- <span class="n">content_length</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">getheaders</span><span class="p">(</span><span class="s2">"Content-Length"</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">content_length</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">get_all</span><span class="p">(</span><span class="s2">"Content-Length"</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">content_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">content_length</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">file_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">content_length</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
- <span class="n">progress</span> <span class="o">&=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">file_size</span><span class="p">)</span>
-
- <span class="n">f</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">()</span>
-
- <span class="k">with</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">file_size</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">progress</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
- <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
- <span class="n">buffer</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="mi">8192</span><span class="p">)</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
- <span class="k">break</span>
- <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
- <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">buffer</span><span class="p">))</span>
-
- <span class="n">f</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
- <span class="n">f</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">f</span></div>
-</pre></div>
- </article>
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diff --git a/sphinx/_modules/ptbench/utils/grad_cams.html b/sphinx/_modules/ptbench/utils/grad_cams.html
deleted file mode 100644
index 461363032490a3e7e14d91c5191cc9724ce06e46..0000000000000000000000000000000000000000
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- <article role="main">
- <h1>Source code for ptbench.utils.grad_cams</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1"># SPDX-FileContributor: Kazuto Nakashima <k nakashima@irvs.ait.kyushu-u.ac.jp></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
-
-
-<div class="viewcode-block" id="BaseWrapper"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper">[docs]</a><span class="k">class</span> <span class="nc">BaseWrapper</span><span class="p">:</span>
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_with_norm</span> <span class="o">=</span> <span class="n">model</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">model</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># a set of hook function handlers</span>
-
- <span class="k">def</span> <span class="nf">_encode_one_hot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="p">):</span>
- <span class="n">one_hot</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
- <span class="n">one_hot</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">one_hot</span>
-
-<div class="viewcode-block" id="BaseWrapper.forward"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">image_shape</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_with_norm</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">descending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># ordered results</span></div>
-
-<div class="viewcode-block" id="BaseWrapper.backward"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Class-specific backpropagation."""</span>
- <span class="n">one_hot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_encode_one_hot</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">model_with_norm</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">gradient</span><span class="o">=</span><span class="n">one_hot</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="BaseWrapper.generate"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.generate">[docs]</a> <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
-
-<div class="viewcode-block" id="BaseWrapper.remove_hook"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.remove_hook">[docs]</a> <span class="k">def</span> <span class="nf">remove_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Remove all the forward/backward hook functions."""</span>
- <span class="k">for</span> <span class="n">handle</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="p">:</span>
- <span class="n">handle</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span></div></div>
-
-
-<div class="viewcode-block" id="GradCAM"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.GradCAM">[docs]</a><span class="k">class</span> <span class="nc">GradCAM</span><span class="p">(</span><span class="n">BaseWrapper</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-<span class="sd"> "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization"</span>
-<span class="sd"> https://arxiv.org/pdf/1610.02391.pdf</span>
-<span class="sd"> Look at Figure 2 on page 4</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">candidate_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
- <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fmap_pool</span> <span class="o">=</span> <span class="p">{}</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">grad_pool</span> <span class="o">=</span> <span class="p">{}</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">candidate_layers</span> <span class="o">=</span> <span class="n">candidate_layers</span> <span class="c1"># list</span>
-
- <span class="k">def</span> <span class="nf">save_fmaps</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
- <span class="k">def</span> <span class="nf">forward_hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fmap_pool</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
-
- <span class="k">return</span> <span class="n">forward_hook</span>
-
- <span class="k">def</span> <span class="nf">save_grads</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
- <span class="k">def</span> <span class="nf">backward_hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">grad_in</span><span class="p">,</span> <span class="n">grad_out</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">grad_pool</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">grad_out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
-
- <span class="k">return</span> <span class="n">backward_hook</span>
-
- <span class="c1"># If any candidates are not specified, the hook is registered to all the layers.</span>
- <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_modules</span><span class="p">():</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">candidate_layers</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">candidate_layers</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">module</span><span class="o">.</span><span class="n">register_forward_hook</span><span class="p">(</span><span class="n">save_fmaps</span><span class="p">(</span><span class="n">name</span><span class="p">))</span>
- <span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">module</span><span class="o">.</span><span class="n">register_backward_hook</span><span class="p">(</span><span class="n">save_grads</span><span class="p">(</span><span class="n">name</span><span class="p">))</span>
- <span class="p">)</span>
-
- <span class="k">def</span> <span class="nf">_find</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool</span><span class="p">,</span> <span class="n">target_layer</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">target_layer</span> <span class="ow">in</span> <span class="n">pool</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
- <span class="k">return</span> <span class="n">pool</span><span class="p">[</span><span class="n">target_layer</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Invalid layer name: </span><span class="si">{</span><span class="n">target_layer</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GradCAM.generate"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.GradCAM.generate">[docs]</a> <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_layer</span><span class="p">):</span>
- <span class="n">fmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fmap_pool</span><span class="p">,</span> <span class="n">target_layer</span><span class="p">)</span>
- <span class="n">grads</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_find</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grad_pool</span><span class="p">,</span> <span class="n">target_layer</span><span class="p">)</span>
- <span class="n">weights</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">adaptive_avg_pool2d</span><span class="p">(</span><span class="n">grads</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">fmaps</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">gcam</span><span class="p">)</span>
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span>
- <span class="n">gcam</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_shape</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"bilinear"</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span>
- <span class="p">)</span>
-
- <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">W</span> <span class="o">=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">shape</span>
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
- <span class="n">gcam</span> <span class="o">-=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">gcam</span> <span class="o">/=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">gcam</span> <span class="o">=</span> <span class="n">gcam</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">gcam</span></div></div>
-</pre></div>
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- <h1>Source code for ptbench.utils.measure</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">deque</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">scipy.special</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-
-<div class="viewcode-block" id="SmoothedValue"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue">[docs]</a><span class="k">class</span> <span class="nc">SmoothedValue</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Track a series of values and provide access to smoothed values over a</span>
-<span class="sd"> window or the global series average."""</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">window_size</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">deque</span> <span class="o">=</span> <span class="n">deque</span><span class="p">(</span><span class="n">maxlen</span><span class="o">=</span><span class="n">window_size</span><span class="p">)</span>
-
-<div class="viewcode-block" id="SmoothedValue.update"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">deque</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">value</span><span class="p">)</span></div>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">median</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">deque</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">d</span><span class="o">.</span><span class="n">median</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">avg</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">deque</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">d</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="tricky_division"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.tricky_division">[docs]</a><span class="k">def</span> <span class="nf">tricky_division</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Divides n by d.</span>
-
-<span class="sd"> Returns 0.0 in case of a division by zero</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">n</span> <span class="o">/</span> <span class="p">(</span><span class="n">d</span> <span class="o">+</span> <span class="p">(</span><span class="n">d</span> <span class="o">==</span> <span class="mi">0</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="base_measures"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.base_measures">[docs]</a><span class="k">def</span> <span class="nf">base_measures</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">tn</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Calculates measures from true/false positive and negative counts.</span>
-
-<span class="sd"> This function can return standard machine learning measures from true and</span>
-<span class="sd"> false positive counts of positives and negatives. For a thorough look into</span>
-<span class="sd"> these and alternate names for the returned values, please check Wikipedia's</span>
-<span class="sd"> entry on `Precision and Recall</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/Precision_and_recall>`_.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> tp : int</span>
-<span class="sd"> True positive count, AKA "hit"</span>
-
-<span class="sd"> fp : int</span>
-<span class="sd"> False positive count, AKA, "correct rejection"</span>
-
-<span class="sd"> tn : int</span>
-<span class="sd"> True negative count, AKA "false alarm", or "Type I error"</span>
-
-<span class="sd"> fn : int</span>
-<span class="sd"> False Negative count, AKA "miss", or "Type II error"</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> precision : float</span>
-<span class="sd"> P, AKA positive predictive value (PPV). It corresponds arithmetically</span>
-<span class="sd"> to ``tp/(tp+fp)``. In the case ``tp+fp == 0``, this function returns</span>
-<span class="sd"> zero for precision.</span>
-
-<span class="sd"> recall : float</span>
-<span class="sd"> R, AKA sensitivity, hit rate, or true positive rate (TPR). It</span>
-<span class="sd"> corresponds arithmetically to ``tp/(tp+fn)``. In the special case</span>
-<span class="sd"> where ``tp+fn == 0``, this function returns zero for recall.</span>
-
-<span class="sd"> specificity : float</span>
-<span class="sd"> S, AKA selectivity or true negative rate (TNR). It</span>
-<span class="sd"> corresponds arithmetically to ``tn/(tn+fp)``. In the special case</span>
-<span class="sd"> where ``tn+fp == 0``, this function returns zero for specificity.</span>
-
-<span class="sd"> accuracy : float</span>
-<span class="sd"> A, see `Accuracy</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers>`_. is</span>
-<span class="sd"> the proportion of correct predictions (both true positives and true</span>
-<span class="sd"> negatives) among the total number of pixels examined. It corresponds</span>
-<span class="sd"> arithmetically to ``(tp+tn)/(tp+tn+fp+fn)``. This measure includes</span>
-<span class="sd"> both true-negatives and positives in the numerator, what makes it</span>
-<span class="sd"> sensitive to data or regions without annotations.</span>
-
-<span class="sd"> jaccard : float</span>
-<span class="sd"> J, see `Jaccard Index or Similarity</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/Jaccard_index>`_. It corresponds</span>
-<span class="sd"> arithmetically to ``tp/(tp+fp+fn)``. In the special case where</span>
-<span class="sd"> ``tn+fp+fn == 0``, this function returns zero for the Jaccard index.</span>
-<span class="sd"> The Jaccard index depends on a TP-only numerator, similarly to the F1</span>
-<span class="sd"> score. For regions where there are no annotations, the Jaccard index</span>
-<span class="sd"> will always be zero, irrespective of the model output. Accuracy may be</span>
-<span class="sd"> a better proxy if one needs to consider the true abscence of</span>
-<span class="sd"> annotations in a region as part of the measure.</span>
-
-<span class="sd"> f1_score : float</span>
-<span class="sd"> F1, see `F1-score <https://en.wikipedia.org/wiki/F1_score>`_. It</span>
-<span class="sd"> corresponds arithmetically to ``2*P*R/(P+R)`` or ``2*tp/(2*tp+fp+fn)``.</span>
-<span class="sd"> In the special case where ``P+R == (2*tp+fp+fn) == 0``, this function</span>
-<span class="sd"> returns zero for the Jaccard index. The F1 or Dice score depends on a</span>
-<span class="sd"> TP-only numerator, similarly to the Jaccard index. For regions where</span>
-<span class="sd"> there are no annotations, the F1-score will always be zero,</span>
-<span class="sd"> irrespective of the model output. Accuracy may be a better proxy if</span>
-<span class="sd"> one needs to consider the true abscence of annotations in a region as</span>
-<span class="sd"> part of the measure.</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="p">(</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fp</span><span class="p">),</span> <span class="c1"># precision</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">),</span> <span class="c1"># recall</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">tn</span><span class="p">),</span> <span class="c1"># specificity</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="n">tp</span> <span class="o">+</span> <span class="n">tn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span> <span class="o">+</span> <span class="n">tn</span><span class="p">),</span> <span class="c1"># accuracy</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">),</span> <span class="c1"># jaccard index</span>
- <span class="n">tricky_division</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">tp</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">tp</span><span class="p">)</span> <span class="o">+</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">),</span> <span class="c1"># f1-score</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="beta_credible_region"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.beta_credible_region">[docs]</a><span class="k">def</span> <span class="nf">beta_credible_region</span><span class="p">(</span><span class="n">successes</span><span class="p">,</span> <span class="n">failures</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns the mode, upper and lower bounds of the equal-tailed credible</span>
-<span class="sd"> region of a probability estimate following Bernoulli trials.</span>
-
-<span class="sd"> This implemetnation is based on [GOUTTE-2005]_. It assumes :math:`k`</span>
-<span class="sd"> successes and :math:`l` failures (:math:`n = k+l` total trials) are issued</span>
-<span class="sd"> from a series of Bernoulli trials (likelihood is binomial). The posterior</span>
-<span class="sd"> is derivated using the Bayes Theorem with a beta prior. As there is no</span>
-<span class="sd"> reason to favour high vs. low precision, we use a symmetric Beta prior</span>
-<span class="sd"> (:math:`\\alpha=\\beta`):</span>
-
-<span class="sd"> .. math::</span>
-
-<span class="sd"> P(p|k,n) &= \\frac{P(k,n|p)P(p)}{P(k,n)} \\\\</span>
-<span class="sd"> P(p|k,n) &= \\frac{\\frac{n!}{k!(n-k)!}p^{k}(1-p)^{n-k}P(p)}{P(k)} \\\\</span>
-<span class="sd"> P(p|k,n) &= \\frac{1}{B(k+\\alpha, n-k+\beta)}p^{k+\\alpha-1}(1-p)^{n-k+\\beta-1} \\\\</span>
-<span class="sd"> P(p|k,n) &= \\frac{1}{B(k+\\alpha, n-k+\\alpha)}p^{k+\\alpha-1}(1-p)^{n-k+\\alpha-1}</span>
-
-<span class="sd"> The mode for this posterior (also the maximum a posteriori) is:</span>
-
-<span class="sd"> .. math::</span>
-
-<span class="sd"> \\text{mode}(p) = \\frac{k+\\lambda-1}{n+2\\lambda-2}</span>
-
-<span class="sd"> Concretely, the prior may be flat (all rates are equally likely,</span>
-<span class="sd"> :math:`\\lambda=1`) or we may use Jeoffrey's prior</span>
-<span class="sd"> (:math:`\\lambda=0.5`), that is invariant through re-parameterisation.</span>
-<span class="sd"> Jeffrey's prior indicate that rates close to zero or one are more likely.</span>
-
-<span class="sd"> The mode above works if :math:`k+{\\alpha},n-k+{\\alpha} > 1`, which is</span>
-<span class="sd"> usually the case for a resonably well tunned system, with more than a few</span>
-<span class="sd"> samples for analysis. In the limit of the system performance, :math:`k`</span>
-<span class="sd"> may be 0, which will make the mode become zero.</span>
-
-<span class="sd"> For our purposes, it may be more suitable to represent :math:`n = k + l`,</span>
-<span class="sd"> with :math:`k`, the number of successes and :math:`l`, the number of</span>
-<span class="sd"> failures in the binomial experiment, and find this more suitable</span>
-<span class="sd"> representation:</span>
-
-<span class="sd"> .. math::</span>
-
-<span class="sd"> P(p|k,l) &= \\frac{1}{B(k+\\alpha, l+\\alpha)}p^{k+\\alpha-1}(1-p)^{l+\\alpha-1} \\\\</span>
-<span class="sd"> \\text{mode}(p) &= \\frac{k+\\lambda-1}{k+l+2\\lambda-2}</span>
-
-<span class="sd"> This can be mapped to most rates calculated in the context of binary</span>
-<span class="sd"> classification this way:</span>
-
-<span class="sd"> * Precision or Positive-Predictive Value (PPV): p = TP/(TP+FP), so k=TP, l=FP</span>
-<span class="sd"> * Recall, Sensitivity, or True Positive Rate: r = TP/(TP+FN), so k=TP, l=FN</span>
-<span class="sd"> * Specificity or True Negative Rage: s = TN/(TN+FP), so k=TN, l=FP</span>
-<span class="sd"> * F1-score: f1 = 2TP/(2TP+FP+FN), so k=2TP, l=FP+FN</span>
-<span class="sd"> * Accuracy: acc = TP+TN/(TP+TN+FP+FN), so k=TP+TN, l=FP+FN</span>
-<span class="sd"> * Jaccard: j = TP/(TP+FP+FN), so k=TP, l=FP+FN</span>
-
-<span class="sd"> Contrary to frequentist approaches, in which one can only</span>
-<span class="sd"> say that if the test were repeated an infinite number of times,</span>
-<span class="sd"> and one constructed a confidence interval each time, then X%</span>
-<span class="sd"> of the confidence intervals would contain the true rate, here</span>
-<span class="sd"> we can say that given our observed data, there is a X% probability</span>
-<span class="sd"> that the true value of :math:`k/n` falls within the provided</span>
-<span class="sd"> interval.</span>
-
-
-<span class="sd"> .. note::</span>
-
-<span class="sd"> For a disambiguation with Confidence Interval, read</span>
-<span class="sd"> https://en.wikipedia.org/wiki/Credible_interval.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ==========</span>
-
-<span class="sd"> successes : int</span>
-<span class="sd"> Number of successes observed on the experiment</span>
-
-<span class="sd"> failures : int</span>
-<span class="sd"> Number of failures observed on the experiment</span>
-
-<span class="sd"> lambda__ : :py:class:`float`, Optional</span>
-<span class="sd"> The parameterisation of the Beta prior to consider. Use</span>
-<span class="sd"> :math:`\\lambda=1` for a flat prior. Use :math:`\\lambda=0.5` for</span>
-<span class="sd"> Jeffrey's prior (the default).</span>
-
-<span class="sd"> coverage : :py:class:`float`, Optional</span>
-<span class="sd"> A floating-point number between 0 and 1.0 indicating the</span>
-<span class="sd"> coverage you're expecting. A value of 0.95 will ensure 95%</span>
-<span class="sd"> of the area under the probability density of the posterior</span>
-<span class="sd"> is covered by the returned equal-tailed interval.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> =======</span>
-
-<span class="sd"> mean : float</span>
-<span class="sd"> The mean of the posterior distribution</span>
-
-<span class="sd"> mode : float</span>
-<span class="sd"> The mode of the posterior distribution</span>
-
-<span class="sd"> lower, upper: float</span>
-<span class="sd"> The lower and upper bounds of the credible region</span>
-<span class="sd"> """</span>
- <span class="c1"># we return the equally-tailed range</span>
- <span class="n">right</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">coverage</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> <span class="c1"># half-width in each side</span>
- <span class="n">lower</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">betaincinv</span><span class="p">(</span>
- <span class="n">successes</span> <span class="o">+</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">failures</span> <span class="o">+</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">right</span>
- <span class="p">)</span>
- <span class="n">upper</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">betaincinv</span><span class="p">(</span>
- <span class="n">successes</span> <span class="o">+</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">failures</span> <span class="o">+</span> <span class="n">lambda_</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">right</span>
- <span class="p">)</span>
-
- <span class="c1"># evaluate mean and mode (https://en.wikipedia.org/wiki/Beta_distribution)</span>
- <span class="n">alpha</span> <span class="o">=</span> <span class="n">successes</span> <span class="o">+</span> <span class="n">lambda_</span>
- <span class="n">beta</span> <span class="o">=</span> <span class="n">failures</span> <span class="o">+</span> <span class="n">lambda_</span>
-
- <span class="n">E</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">/</span> <span class="p">(</span><span class="n">alpha</span> <span class="o">+</span> <span class="n">beta</span><span class="p">)</span>
-
- <span class="c1"># the mode of a beta distribution is a bit tricky</span>
- <span class="k">if</span> <span class="n">alpha</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">beta</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="p">(</span><span class="n">alpha</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">alpha</span> <span class="o">+</span> <span class="n">beta</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
- <span class="k">elif</span> <span class="n">alpha</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">beta</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="c1"># In the case of precision, if the threshold is close to 1.0, both TP</span>
- <span class="c1"># and FP can be zero, which may cause this condition to be reached, if</span>
- <span class="c1"># the prior is exactly 1 (flat prior). This is a weird situation,</span>
- <span class="c1"># because effectively we are trying to compute the posterior when the</span>
- <span class="c1"># total number of experiments is zero. So, only the prior counts - but</span>
- <span class="c1"># the prior is flat, so we should just pick a value. We choose the</span>
- <span class="c1"># middle of the range.</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="c1"># any value would do, we just pick this one</span>
- <span class="k">elif</span> <span class="n">alpha</span> <span class="o"><=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">beta</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="mf">0.0</span>
- <span class="k">elif</span> <span class="n">alpha</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">beta</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="mf">1.0</span>
- <span class="k">else</span><span class="p">:</span> <span class="c1"># elif alpha < 1 and beta < 1:</span>
- <span class="c1"># in the case of precision, if the threshold is close to 1.0, both TP</span>
- <span class="c1"># and FP can be zero, which may cause this condition to be reached, if</span>
- <span class="c1"># the prior is smaller than 1. This is a weird situation, because</span>
- <span class="c1"># effectively we are trying to compute the posterior when the total</span>
- <span class="c1"># number of experiments is zero. So, only the prior counts - but the</span>
- <span class="c1"># prior is bimodal, so we should just pick a value. We choose the</span>
- <span class="c1"># left of the range.</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="c1"># could also be 1.0 as the prior is bimodal</span>
-
- <span class="k">return</span> <span class="n">E</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span></div>
-
-
-<div class="viewcode-block" id="bayesian_measures"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.bayesian_measures">[docs]</a><span class="k">def</span> <span class="nf">bayesian_measures</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">tn</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">):</span>
-<span class="w"> </span><span class="sa">r</span><span class="sd">"""Calculates mean and mode from true/false positive and negative counts</span>
-<span class="sd"> with credible regions.</span>
-
-<span class="sd"> This function can return bayesian estimates of standard machine learning</span>
-<span class="sd"> measures from true and false positive counts of positives and negatives.</span>
-<span class="sd"> For a thorough look into these and alternate names for the returned values,</span>
-<span class="sd"> please check Wikipedia's entry on `Precision and Recall</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/Precision_and_recall>`_. See</span>
-<span class="sd"> :py:func:`beta_credible_region` for details on the calculation of returned</span>
-<span class="sd"> values.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> tp : int</span>
-<span class="sd"> True positive count, AKA "hit"</span>
-
-<span class="sd"> fp : int</span>
-<span class="sd"> False positive count, AKA "false alarm", or "Type I error"</span>
-
-<span class="sd"> tn : int</span>
-<span class="sd"> True negative count, AKA "correct rejection"</span>
-
-<span class="sd"> fn : int</span>
-<span class="sd"> False Negative count, AKA "miss", or "Type II error"</span>
-
-<span class="sd"> lambda_ : float</span>
-<span class="sd"> The parameterisation of the Beta prior to consider. Use</span>
-<span class="sd"> :math:`\lambda=1` for a flat prior. Use :math:`\lambda=0.5` for</span>
-<span class="sd"> Jeffrey's prior.</span>
-
-<span class="sd"> coverage : float</span>
-<span class="sd"> A floating-point number between 0 and 1.0 indicating the</span>
-<span class="sd"> coverage you're expecting. A value of 0.95 will ensure 95%</span>
-<span class="sd"> of the area under the probability density of the posterior</span>
-<span class="sd"> is covered by the returned equal-tailed interval.</span>
-
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> precision : (float, float, float, float)</span>
-<span class="sd"> P, AKA positive predictive value (PPV), mean, mode and credible</span>
-<span class="sd"> intervals (95% CI). It corresponds arithmetically</span>
-<span class="sd"> to ``tp/(tp+fp)``.</span>
-
-<span class="sd"> recall : (float, float, float, float)</span>
-<span class="sd"> R, AKA sensitivity, hit rate, or true positive rate (TPR), mean, mode</span>
-<span class="sd"> and credible intervals (95% CI). It corresponds arithmetically to</span>
-<span class="sd"> ``tp/(tp+fn)``.</span>
-
-<span class="sd"> specificity : (float, float, float, float)</span>
-<span class="sd"> S, AKA selectivity or true negative rate (TNR), mean, mode and credible</span>
-<span class="sd"> intervals (95% CI). It corresponds arithmetically to ``tn/(tn+fp)``.</span>
-
-<span class="sd"> accuracy : (float, float, float, float)</span>
-<span class="sd"> A, mean, mode and credible intervals (95% CI). See `Accuracy</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers>`_. is</span>
-<span class="sd"> the proportion of correct predictions (both true positives and true</span>
-<span class="sd"> negatives) among the total number of pixels examined. It corresponds</span>
-<span class="sd"> arithmetically to ``(tp+tn)/(tp+tn+fp+fn)``. This measure includes</span>
-<span class="sd"> both true-negatives and positives in the numerator, what makes it</span>
-<span class="sd"> sensitive to data or regions without annotations.</span>
-
-<span class="sd"> jaccard : (float, float, float, float)</span>
-<span class="sd"> J, mean, mode and credible intervals (95% CI). See `Jaccard Index or</span>
-<span class="sd"> Similarity <https://en.wikipedia.org/wiki/Jaccard_index>`_. It</span>
-<span class="sd"> corresponds arithmetically to ``tp/(tp+fp+fn)``. The Jaccard index</span>
-<span class="sd"> depends on a TP-only numerator, similarly to the F1 score. For regions</span>
-<span class="sd"> where there are no annotations, the Jaccard index will always be zero,</span>
-<span class="sd"> irrespective of the model output. Accuracy may be a better proxy if</span>
-<span class="sd"> one needs to consider the true abscence of annotations in a region as</span>
-<span class="sd"> part of the measure.</span>
-
-<span class="sd"> f1_score : (float, float, float, float)</span>
-<span class="sd"> F1, mean, mode and credible intervals (95% CI). See `F1-score</span>
-<span class="sd"> <https://en.wikipedia.org/wiki/F1_score>`_. It corresponds</span>
-<span class="sd"> arithmetically to ``2*P*R/(P+R)`` or ``2*tp/(2*tp+fp+fn)``. The F1 or</span>
-<span class="sd"> Dice score depends on a TP-only numerator, similarly to the Jaccard</span>
-<span class="sd"> index. For regions where there are no annotations, the F1-score will</span>
-<span class="sd"> always be zero, irrespective of the model output. Accuracy may be a</span>
-<span class="sd"> better proxy if one needs to consider the true abscence of annotations</span>
-<span class="sd"> in a region as part of the measure.</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="p">(</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># precision</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># recall</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># specificity</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="n">tp</span> <span class="o">+</span> <span class="n">tn</span><span class="p">,</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># accuracy</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># jaccard index</span>
- <span class="n">beta_credible_region</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">tp</span><span class="p">,</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">coverage</span><span class="p">),</span> <span class="c1"># f1-score</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="get_centered_maxf1"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.measure.html#ptbench.utils.measure.get_centered_maxf1">[docs]</a><span class="k">def</span> <span class="nf">get_centered_maxf1</span><span class="p">(</span><span class="n">f1_scores</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Return the centered max F1 score threshold when multiple threshold give</span>
-<span class="sd"> the same max F1 score.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> f1_scores : numpy.ndarray</span>
-<span class="sd"> 1D array of f1 scores</span>
-
-<span class="sd"> thresholds : numpy.ndarray</span>
-<span class="sd"> 1D array of thresholds</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> max F1 score: float</span>
-
-<span class="sd"> threshold: float</span>
-<span class="sd"> """</span>
- <span class="n">maxf1</span> <span class="o">=</span> <span class="n">f1_scores</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
- <span class="n">maxf1_indices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">f1_scores</span> <span class="o">==</span> <span class="n">maxf1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="c1"># If multiple thresholds give the same max F1 score</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">maxf1_indices</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">mean_maxf1_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">maxf1_indices</span><span class="p">)))</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">mean_maxf1_index</span> <span class="o">=</span> <span class="n">maxf1_indices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="k">return</span> <span class="n">maxf1</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">mean_maxf1_index</span><span class="p">]</span></div>
-</pre></div>
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- <h1>Source code for ptbench.utils.model_serialization</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright Facebook, Inc. and its affiliates. All Rights Reserved.</span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="c1"># Original code from: https://github.com/facebookresearch/maskrcnn-benchmark</span>
-
-<span class="kn">import</span> <span class="nn">logging</span>
-
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-
-
-<div class="viewcode-block" id="align_and_update_state_dicts"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.align_and_update_state_dicts">[docs]</a><span class="k">def</span> <span class="nf">align_and_update_state_dicts</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">,</span> <span class="n">loaded_state_dict</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""</span>
-<span class="sd"> Strategy: suppose that the models that we will create will have prefixes appended</span>
-<span class="sd"> to each of its keys, for example due to an extra level of nesting that the original</span>
-<span class="sd"> pre-trained weights from ImageNet won't contain. For example, model.state_dict()</span>
-<span class="sd"> might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains</span>
-<span class="sd"> res2.conv1.weight. We thus want to match both parameters together.</span>
-<span class="sd"> For that, we look for each model weight, look among all loaded keys if there is one</span>
-<span class="sd"> that is a suffix of the current weight name, and use it if that's the case.</span>
-<span class="sd"> If multiple matches exist, take the one with longest size</span>
-<span class="sd"> of the corresponding name. For example, for the same model as before, the pretrained</span>
-<span class="sd"> weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,</span>
-<span class="sd"> we want to match backbone[0].body.conv1.weight to conv1.weight, and</span>
-<span class="sd"> backbone[0].body.res2.conv1.weight to res2.conv1.weight.</span>
-<span class="sd"> """</span>
- <span class="n">current_keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">model_state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
- <span class="n">loaded_keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">loaded_state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
- <span class="c1"># get a matrix of string matches, where each (i, j) entry correspond to the size of the</span>
- <span class="c1"># loaded_key string, if it matches</span>
- <span class="n">match_matrix</span> <span class="o">=</span> <span class="p">[</span>
- <span class="nb">len</span><span class="p">(</span><span class="n">j</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="n">j</span><span class="p">)</span> <span class="k">else</span> <span class="mi">0</span>
- <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_keys</span>
- <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">loaded_keys</span>
- <span class="p">]</span>
- <span class="n">match_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">match_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
- <span class="nb">len</span><span class="p">(</span><span class="n">current_keys</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">loaded_keys</span><span class="p">)</span>
- <span class="p">)</span>
- <span class="n">max_match_size</span><span class="p">,</span> <span class="n">idxs</span> <span class="o">=</span> <span class="n">match_matrix</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
- <span class="c1"># remove indices that correspond to no-match</span>
- <span class="n">idxs</span><span class="p">[</span><span class="n">max_match_size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-
- <span class="c1"># used for logging</span>
- <span class="n">max_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">current_keys</span><span class="p">])</span> <span class="k">if</span> <span class="n">current_keys</span> <span class="k">else</span> <span class="mi">1</span>
- <span class="n">max_size_loaded</span> <span class="o">=</span> <span class="p">(</span>
- <span class="nb">max</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">loaded_keys</span><span class="p">])</span> <span class="k">if</span> <span class="n">loaded_keys</span> <span class="k">else</span> <span class="mi">1</span>
- <span class="p">)</span>
- <span class="n">log_str_template</span> <span class="o">=</span> <span class="s2">"{: <</span><span class="si">{}</span><span class="s2">} loaded from {: <</span><span class="si">{}</span><span class="s2">} of shape </span><span class="si">{}</span><span class="s2">"</span>
- <span class="k">for</span> <span class="n">idx_new</span><span class="p">,</span> <span class="n">idx_old</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">idxs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()):</span>
- <span class="k">if</span> <span class="n">idx_old</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
- <span class="k">continue</span>
- <span class="n">key</span> <span class="o">=</span> <span class="n">current_keys</span><span class="p">[</span><span class="n">idx_new</span><span class="p">]</span>
- <span class="n">key_old</span> <span class="o">=</span> <span class="n">loaded_keys</span><span class="p">[</span><span class="n">idx_old</span><span class="p">]</span>
- <span class="n">model_state_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">loaded_state_dict</span><span class="p">[</span><span class="n">key_old</span><span class="p">]</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
- <span class="n">log_str_template</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">key</span><span class="p">,</span>
- <span class="n">max_size</span><span class="p">,</span>
- <span class="n">key_old</span><span class="p">,</span>
- <span class="n">max_size_loaded</span><span class="p">,</span>
- <span class="nb">tuple</span><span class="p">(</span><span class="n">loaded_state_dict</span><span class="p">[</span><span class="n">key_old</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
- <span class="p">)</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="strip_prefix_if_present"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.strip_prefix_if_present">[docs]</a><span class="k">def</span> <span class="nf">strip_prefix_if_present</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">):</span>
- <span class="n">keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">state_dict</span>
- <span class="n">stripped_state_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">stripped_state_dict</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="s2">""</span><span class="p">)]</span> <span class="o">=</span> <span class="n">value</span>
- <span class="k">return</span> <span class="n">stripped_state_dict</span></div>
-
-
-<div class="viewcode-block" id="load_state_dict"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.load_state_dict">[docs]</a><span class="k">def</span> <span class="nf">load_state_dict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">loaded_state_dict</span><span class="p">):</span>
- <span class="n">model_state_dict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
- <span class="c1"># if the state_dict comes from a model that was wrapped in a</span>
- <span class="c1"># DataParallel or DistributedDataParallel during serialization,</span>
- <span class="c1"># remove the "module" prefix before performing the matching</span>
- <span class="n">loaded_state_dict</span> <span class="o">=</span> <span class="n">strip_prefix_if_present</span><span class="p">(</span>
- <span class="n">loaded_state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s2">"module."</span>
- <span class="p">)</span>
- <span class="n">align_and_update_state_dicts</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">,</span> <span class="n">loaded_state_dict</span><span class="p">)</span>
-
- <span class="c1"># use strict loading</span>
- <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">)</span></div>
-</pre></div>
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- <h1>Source code for ptbench.utils.model_zoo</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="c1"># Adapted from:</span>
-<span class="c1"># https://github.com/pytorch/pytorch/blob/master/torch/hub.py</span>
-<span class="c1"># https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/utils/checkpoint.py</span>
-
-<span class="kn">import</span> <span class="nn">hashlib</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">re</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">tempfile</span>
-
-<span class="kn">from</span> <span class="nn">urllib.parse</span> <span class="kn">import</span> <span class="n">urlparse</span>
-<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="kn">import</span> <span class="n">urlopen</span>
-
-<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="n">modelurls</span> <span class="o">=</span> <span class="p">{</span>
- <span class="s2">"vgg11"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg11-bbd30ac9.pth"</span><span class="p">,</span>
- <span class="s2">"vgg13"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg13-c768596a.pth"</span><span class="p">,</span>
- <span class="s2">"vgg16"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg16-397923af.pth"</span><span class="p">,</span>
- <span class="s2">"vgg19"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth"</span><span class="p">,</span>
- <span class="s2">"vgg11_bn"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg11_bn-6002323d.pth"</span><span class="p">,</span>
- <span class="s2">"vgg13_bn"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg13_bn-abd245e5.pth"</span><span class="p">,</span>
- <span class="s2">"vgg16_bn"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth"</span><span class="p">,</span>
- <span class="s2">"vgg19_bn"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/vgg19_bn-c79401a0.pth"</span><span class="p">,</span>
- <span class="s2">"resnet18"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/resnet18-5c106cde.pth"</span><span class="p">,</span>
- <span class="s2">"resnet34"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/resnet34-333f7ec4.pth"</span><span class="p">,</span>
- <span class="s2">"resnet50"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/resnet50-19c8e357.pth"</span><span class="p">,</span>
- <span class="s2">"resnet101"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth"</span><span class="p">,</span>
- <span class="s2">"resnet152"</span><span class="p">:</span> <span class="s2">"https://download.pytorch.org/models/resnet152-b121ed2d.pth"</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="sd">"""URLs of pre-trained models (backbones)"""</span>
-
-
-<div class="viewcode-block" id="download_url_to_file"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.model_zoo.html#ptbench.utils.model_zoo.download_url_to_file">[docs]</a><span class="k">def</span> <span class="nf">download_url_to_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span> <span class="n">hash_prefix</span><span class="p">,</span> <span class="n">progress</span><span class="p">):</span>
- <span class="n">file_size</span> <span class="o">=</span> <span class="kc">None</span>
- <span class="n">u</span> <span class="o">=</span> <span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
- <span class="n">meta</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
- <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="s2">"getheaders"</span><span class="p">):</span>
- <span class="n">content_length</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">getheaders</span><span class="p">(</span><span class="s2">"Content-Length"</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">content_length</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">get_all</span><span class="p">(</span><span class="s2">"Content-Length"</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">content_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">content_length</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">file_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">content_length</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
- <span class="n">f</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="n">delete</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">hash_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">sha256</span> <span class="o">=</span> <span class="n">hashlib</span><span class="o">.</span><span class="n">sha256</span><span class="p">()</span>
- <span class="k">with</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">total</span><span class="o">=</span><span class="n">file_size</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">progress</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
- <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
- <span class="n">buffer</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="mi">8192</span><span class="p">)</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
- <span class="k">break</span>
- <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">hash_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">sha256</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
- <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">buffer</span><span class="p">))</span>
-
- <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">hash_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">digest</span> <span class="o">=</span> <span class="n">sha256</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">digest</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">hash_prefix</span><span class="p">)]</span> <span class="o">!=</span> <span class="n">hash_prefix</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
- <span class="s1">'invalid hash value (expected "</span><span class="si">{}</span><span class="s1">", got "</span><span class="si">{}</span><span class="s1">")'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">hash_prefix</span><span class="p">,</span> <span class="n">digest</span>
- <span class="p">)</span>
- <span class="p">)</span>
- <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">dst</span><span class="p">)</span>
- <span class="k">finally</span><span class="p">:</span>
- <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">):</span>
- <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
-
-
-<span class="n">HASH_REGEX</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s2">"-([a-f0-9]*)\."</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="cache_url"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.model_zoo.html#ptbench.utils.model_zoo.cache_url">[docs]</a><span class="k">def</span> <span class="nf">cache_url</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">model_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w"> </span><span class="sa">r</span><span class="sd">"""Loads the Torch serialized object at the given URL.</span>
-
-<span class="sd"> If the object is already present in `model_dir`, it's deserialized and</span>
-<span class="sd"> returned. The filename part of the URL should follow the naming convention</span>
-<span class="sd"> ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more</span>
-<span class="sd"> digits of the SHA256 hash of the contents of the file. The hash is used to</span>
-<span class="sd"> ensure unique names and to verify the contents of the file.</span>
-<span class="sd"> The default value of `model_dir` is ``$TORCH_HOME/models`` where</span>
-<span class="sd"> ``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be</span>
-<span class="sd"> overridden with the ``$TORCH_MODEL_ZOO`` environment variable.</span>
-<span class="sd"> Args:</span>
-<span class="sd"> url (string): URL of the object to download</span>
-<span class="sd"> model_dir (string, optional): directory in which to save the object</span>
-<span class="sd"> progress (bool, optional): whether or not to display a progress bar to stderr</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">model_dir</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">torch_home</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">expanduser</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"TORCH_HOME"</span><span class="p">,</span> <span class="s2">"~/.torch"</span><span class="p">))</span>
- <span class="n">model_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span>
- <span class="s2">"TORCH_MODEL_ZOO"</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">torch_home</span><span class="p">,</span> <span class="s2">"models"</span><span class="p">)</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">model_dir</span><span class="p">):</span>
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">model_dir</span><span class="p">)</span>
- <span class="n">parts</span> <span class="o">=</span> <span class="n">urlparse</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
- <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">parts</span><span class="o">.</span><span class="n">path</span><span class="p">)</span>
-
- <span class="n">cached_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">cached_file</span><span class="p">):</span>
- <span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Downloading: "</span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s1">" to </span><span class="si">{</span><span class="n">cached_file</span><span class="si">}</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
- <span class="n">hash_prefix</span> <span class="o">=</span> <span class="n">HASH_REGEX</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">hash_prefix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">hash_prefix</span> <span class="o">=</span> <span class="n">hash_prefix</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
- <span class="n">download_url_to_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">cached_file</span><span class="p">,</span> <span class="n">hash_prefix</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="n">progress</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">cached_file</span></div>
-</pre></div>
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
-
-
- </div>
- <div class="bottom-of-page">
- <div class="left-details">
- <div class="copyright">
- Copyright © 2023, Idiap Research Institute
- </div>
- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-
- <a href="https://github.com/pradyunsg/furo">Furo</a>
-
- </div>
- <div class="right-details">
-
- </div>
- </div>
-
- </footer>
- </div>
- <aside class="toc-drawer no-toc">
-
-
-
- </aside>
- </div>
-</div><script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
- <script src="../../../_static/jquery.js"></script>
- <script src="../../../_static/underscore.js"></script>
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- <script src="../../../_static/scripts/furo.js"></script>
- <script src="../../../_static/clipboard.min.js"></script>
- <script src="../../../_static/copybutton.js"></script>
- <script src="../../../_static/tabs.js"></script>
- </body>
-</html>
\ No newline at end of file
diff --git a/sphinx/_modules/ptbench/utils/plot.html b/sphinx/_modules/ptbench/utils/plot.html
deleted file mode 100644
index ee4476e7f89d79bda910417894b47f2b78d6a2ff..0000000000000000000000000000000000000000
--- a/sphinx/_modules/ptbench/utils/plot.html
+++ /dev/null
@@ -1,695 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
- <head><meta charset="utf-8"/>
- <meta name="viewport" content="width=device-width,initial-scale=1"/>
- <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
-
- <!-- Generated with Sphinx 5.3.0 and Furo 2022.12.07 -->
- <title>ptbench.utils.plot - ptbench 1.0.0b0</title>
- <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
- <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?digest=91d0f0d1c444bdcb17a68e833c7a53903343c195" />
- <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" />
- <link rel="stylesheet" type="text/css" href="../../../_static/tabs.css" />
- <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?digest=30d1aed668e5c3a91c3e3bf6a60b675221979f0e" />
-
-
-
-
-<style>
- body {
- --color-code-background: #eeffcc;
- --color-code-foreground: black;
-
- }
- @media not print {
- body[data-theme="dark"] {
- --color-code-background: #272822;
- --color-code-foreground: #f8f8f2;
-
- }
- @media (prefers-color-scheme: dark) {
- body:not([data-theme="light"]) {
- --color-code-background: #272822;
- --color-code-foreground: #f8f8f2;
-
- }
- }
- }
-</style></head>
- <body>
-
- <script>
- document.body.dataset.theme = localStorage.getItem("theme") || "auto";
- </script>
-
-
-<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
- <symbol id="svg-toc" viewBox="0 0 24 24">
- <title>Contents</title>
- <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
- <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
- </svg>
- </symbol>
- <symbol id="svg-menu" viewBox="0 0 24 24">
- <title>Menu</title>
- <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
- stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
- <line x1="3" y1="12" x2="21" y2="12"></line>
- <line x1="3" y1="6" x2="21" y2="6"></line>
- <line x1="3" y1="18" x2="21" y2="18"></line>
- </svg>
- </symbol>
- <symbol id="svg-arrow-right" viewBox="0 0 24 24">
- <title>Expand</title>
- <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
- stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
- <polyline points="9 18 15 12 9 6"></polyline>
- </svg>
- </symbol>
- <symbol id="svg-sun" viewBox="0 0 24 24">
- <title>Light mode</title>
- <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
- stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
- <circle cx="12" cy="12" r="5"></circle>
- <line x1="12" y1="1" x2="12" y2="3"></line>
- <line x1="12" y1="21" x2="12" y2="23"></line>
- <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
- <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
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- <line x1="21" y1="12" x2="23" y2="12"></line>
- <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
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- </svg>
- </symbol>
- <symbol id="svg-moon" viewBox="0 0 24 24">
- <title>Dark mode</title>
- <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
- stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
- <path stroke="none" d="M0 0h24v24H0z" fill="none" />
- <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
- </svg>
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- <h1>Source code for ptbench.utils.plot</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">contextlib</span>
-
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">auc</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">precision_recall_curve</span> <span class="k">as</span> <span class="n">pr_curve</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span> <span class="k">as</span> <span class="n">r_curve</span>
-
-<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s2">"agg"</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
-<span class="k">def</span> <span class="nf">_precision_recall_canvas</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Generates a canvas to draw precision-recall curves.</span>
-
-<span class="sd"> Works like a context manager, yielding a figure and an axes set in which</span>
-<span class="sd"> the precision-recall curves should be added to. The figure already</span>
-<span class="sd"> contains F1-ISO lines and is preset to a 0-1 square region. Once the</span>
-<span class="sd"> context is finished, ``fig.tight_layout()`` is called.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> title : :py:class:`str`, Optional</span>
-<span class="sd"> Optional title to add to this plot</span>
-
-
-<span class="sd"> Yields</span>
-<span class="sd"> ------</span>
-
-<span class="sd"> figure : matplotlib.figure.Figure</span>
-<span class="sd"> The figure that should be finally returned to the user</span>
-
-<span class="sd"> axes : matplotlib.figure.Axes</span>
-<span class="sd"> An axis set where to precision-recall plots should be added to</span>
-<span class="sd"> """</span>
- <span class="n">fig</span><span class="p">,</span> <span class="n">axes1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
- <span class="c1"># Names and bounds</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Recall"</span><span class="p">)</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Precision"</span><span class="p">)</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
-
- <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
-
- <span class="n">axes1</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"gray"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
- <span class="n">axes2</span> <span class="o">=</span> <span class="n">axes1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
-
- <span class="c1"># Annotates plot with F1-score iso-lines</span>
- <span class="n">f_scores</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
- <span class="n">tick_locs</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">tick_labels</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">f_score</span> <span class="ow">in</span> <span class="n">f_scores</span><span class="p">:</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
- <span class="n">y</span> <span class="o">=</span> <span class="n">f_score</span> <span class="o">*</span> <span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">-</span> <span class="n">f_score</span><span class="p">)</span>
- <span class="p">(</span><span class="n">l</span><span class="p">,)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">y</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">y</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"green"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
- <span class="n">tick_locs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
- <span class="n">tick_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"</span><span class="si">%.1f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">f_score</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">"y"</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s2">"both"</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"iso-F"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"green"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_label_coords</span><span class="p">(</span><span class="mf">1.015</span><span class="p">,</span> <span class="mf">0.97</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">tick_locs</span><span class="p">)</span> <span class="c1"># notice these are invisible</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">axes2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">tick_labels</span><span class="p">):</span>
- <span class="n">k</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"green"</span><span class="p">)</span>
- <span class="n">k</span><span class="o">.</span><span class="n">set_alpha</span><span class="p">(</span><span class="mf">0.3</span><span class="p">)</span>
- <span class="n">k</span><span class="o">.</span><span class="n">set_size</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
-
- <span class="c1"># we should see some of axes 1 axes</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"right"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"top"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"left"</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">"data"</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.015</span><span class="p">))</span>
- <span class="n">axes1</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"bottom"</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">"data"</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.015</span><span class="p">))</span>
-
- <span class="c1"># we shouldn't see any of axes 2 axes</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"right"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"top"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"left"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"bottom"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
- <span class="c1"># yield execution, lets user draw precision-recall plots, and the legend</span>
- <span class="c1"># before tighteneing the layout</span>
- <span class="k">yield</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axes1</span>
-
- <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-
-
-<div class="viewcode-block" id="precision_recall_f1iso"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.plot.html#ptbench.utils.plot.precision_recall_f1iso">[docs]</a><span class="k">def</span> <span class="nf">precision_recall_f1iso</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Creates a precision-recall plot.</span>
-
-<span class="sd"> This function creates and returns a Matplotlib figure with a</span>
-<span class="sd"> precision-recall plot. The plot will be annotated with F1-score</span>
-<span class="sd"> iso-lines (in which the F1-score maintains the same value).</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> data : dict</span>
-<span class="sd"> A dictionary in which keys are strings defining plot labels and values</span>
-<span class="sd"> are dictionaries with two entries:</span>
-
-<span class="sd"> * ``df``: :py:class:`pandas.DataFrame`</span>
-
-<span class="sd"> A dataframe that is produced by our predictor engine containing</span>
-<span class="sd"> the following columns: ``filename``, ``likelihood``,</span>
-<span class="sd"> ``ground_truth``.</span>
-
-<span class="sd"> * ``threshold``: :py:class:`list`</span>
-
-<span class="sd"> A threshold for each set. Not used here.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> figure : matplotlib.figure.Figure</span>
-<span class="sd"> A matplotlib figure you can save or display (uses an ``agg`` backend)</span>
-<span class="sd"> """</span>
- <span class="n">lines</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"-"</span><span class="p">,</span> <span class="s2">"--"</span><span class="p">,</span> <span class="s2">"-."</span><span class="p">,</span> <span class="s2">":"</span><span class="p">]</span>
- <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span>
- <span class="s2">"#1f77b4"</span><span class="p">,</span>
- <span class="s2">"#ff7f0e"</span><span class="p">,</span>
- <span class="s2">"#2ca02c"</span><span class="p">,</span>
- <span class="s2">"#d62728"</span><span class="p">,</span>
- <span class="s2">"#9467bd"</span><span class="p">,</span>
- <span class="s2">"#8c564b"</span><span class="p">,</span>
- <span class="s2">"#e377c2"</span><span class="p">,</span>
- <span class="s2">"#7f7f7f"</span><span class="p">,</span>
- <span class="s2">"#bcbd22"</span><span class="p">,</span>
- <span class="s2">"#17becf"</span><span class="p">,</span>
- <span class="p">]</span>
- <span class="n">colorcycler</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
- <span class="n">linecycler</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="n">_precision_recall_canvas</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="k">as</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span><span class="p">):</span>
- <span class="n">legend</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">df</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="s2">"df"</span><span class="p">]</span>
-
- <span class="c1"># plots Recall/Precision curve</span>
- <span class="n">prec</span><span class="p">,</span> <span class="n">recall</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pr_curve</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">])</span>
- <span class="n">_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">recall</span><span class="p">,</span> <span class="n">prec</span><span class="p">)</span>
- <span class="n">label</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> (AUC=</span><span class="si">{</span><span class="n">_auc</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">)"</span>
- <span class="n">color</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">colorcycler</span><span class="p">)</span>
- <span class="n">style</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">linecycler</span><span class="p">)</span>
-
- <span class="p">(</span><span class="n">line</span><span class="p">,)</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">recall</span><span class="p">,</span> <span class="n">prec</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">style</span><span class="p">)</span>
- <span class="n">legend</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">line</span><span class="p">,</span> <span class="n">label</span><span class="p">))</span>
-
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
- <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">legend</span><span class="p">],</span>
- <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">legend</span><span class="p">],</span>
- <span class="n">loc</span><span class="o">=</span><span class="s2">"lower left"</span><span class="p">,</span>
- <span class="n">fancybox</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
- <span class="n">framealpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="k">return</span> <span class="n">fig</span></div>
-
-
-<div class="viewcode-block" id="roc_curve"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.plot.html#ptbench.utils.plot.roc_curve">[docs]</a><span class="k">def</span> <span class="nf">roc_curve</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Creates a ROC plot.</span>
-
-<span class="sd"> This function creates and returns a Matplotlib figure with a</span>
-<span class="sd"> ROC plot.</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> data : dict</span>
-<span class="sd"> A dictionary in which keys are strings defining plot labels and values</span>
-<span class="sd"> are dictionaries with two entries:</span>
-
-<span class="sd"> * ``df``: :py:class:`pandas.DataFrame`</span>
-
-<span class="sd"> A dataframe that is produced by our predictor engine containing</span>
-<span class="sd"> the following columns: ``filename``, ``likelihood``,</span>
-<span class="sd"> ``ground_truth``.</span>
-
-<span class="sd"> * ``threshold``: :py:class:`list`</span>
-
-<span class="sd"> A threshold for each set. Not used here.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> figure : matplotlib.figure.Figure</span>
-<span class="sd"> A matplotlib figure you can save or display (uses an ``agg`` backend)</span>
-<span class="sd"> """</span>
- <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
- <span class="c1"># Names and bounds</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"1 - specificity"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Sensitivity"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
-
- <span class="c1"># we should see some of axes 1 axes</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"right"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"top"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"left"</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">"data"</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.015</span><span class="p">))</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"bottom"</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s2">"data"</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.015</span><span class="p">))</span>
-
- <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
-
- <span class="n">axes</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"gray"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-
- <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-
- <span class="n">lines</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"-"</span><span class="p">,</span> <span class="s2">"--"</span><span class="p">,</span> <span class="s2">"-."</span><span class="p">,</span> <span class="s2">":"</span><span class="p">]</span>
- <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span>
- <span class="s2">"#1f77b4"</span><span class="p">,</span>
- <span class="s2">"#ff7f0e"</span><span class="p">,</span>
- <span class="s2">"#2ca02c"</span><span class="p">,</span>
- <span class="s2">"#d62728"</span><span class="p">,</span>
- <span class="s2">"#9467bd"</span><span class="p">,</span>
- <span class="s2">"#8c564b"</span><span class="p">,</span>
- <span class="s2">"#e377c2"</span><span class="p">,</span>
- <span class="s2">"#7f7f7f"</span><span class="p">,</span>
- <span class="s2">"#bcbd22"</span><span class="p">,</span>
- <span class="s2">"#17becf"</span><span class="p">,</span>
- <span class="p">]</span>
- <span class="n">colorcycler</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
- <span class="n">linecycler</span> <span class="o">=</span> <span class="n">cycle</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
-
- <span class="n">legend</span> <span class="o">=</span> <span class="p">[]</span>
-
- <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">df</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="s2">"df"</span><span class="p">]</span>
-
- <span class="c1"># plots roc curve</span>
- <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">r_curve</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">])</span>
- <span class="n">_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">)</span>
- <span class="n">label</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> (AUC=</span><span class="si">{</span><span class="n">_auc</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">)"</span>
- <span class="n">color</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">colorcycler</span><span class="p">)</span>
- <span class="n">style</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">linecycler</span><span class="p">)</span>
-
- <span class="p">(</span><span class="n">line</span><span class="p">,)</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">style</span><span class="p">)</span>
- <span class="n">legend</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">line</span><span class="p">,</span> <span class="n">label</span><span class="p">))</span>
-
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
- <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">legend</span><span class="p">],</span>
- <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">legend</span><span class="p">],</span>
- <span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">,</span>
- <span class="n">fancybox</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
- <span class="n">framealpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="k">return</span> <span class="n">fig</span></div>
-
-
-<div class="viewcode-block" id="relevance_analysis_plot"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.plot.html#ptbench.utils.plot.relevance_analysis_plot">[docs]</a><span class="k">def</span> <span class="nf">relevance_analysis_plot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Create an histogram plot to show the relative importance of features.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> data : :py:class:`list`</span>
-<span class="sd"> The list of values (one for each feature)</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> figure : matplotlib.figure.Figure</span>
-<span class="sd"> A matplotlib figure you can save or display (uses an ``agg`` backend)</span>
-<span class="sd"> """</span>
- <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-
- <span class="c1"># Names and bounds</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Features"</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Importance"</span><span class="p">)</span>
-
- <span class="c1"># we should see some of axes 1 axes</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"right"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s2">"top"</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">axes</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
-
- <span class="c1"># 818C2E = likely</span>
- <span class="c1"># F2921D = could be</span>
- <span class="c1"># 8C3503 = unlikely</span>
-
- <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span>
- <span class="s2">"Cardiomegaly"</span><span class="p">,</span>
- <span class="s2">"Emphysema"</span><span class="p">,</span>
- <span class="s2">"Pleural effusion"</span><span class="p">,</span>
- <span class="s2">"Hernia"</span><span class="p">,</span>
- <span class="s2">"Infiltration"</span><span class="p">,</span>
- <span class="s2">"Mass"</span><span class="p">,</span>
- <span class="s2">"Nodule"</span><span class="p">,</span>
- <span class="s2">"Atelectasis"</span><span class="p">,</span>
- <span class="s2">"Pneumothorax"</span><span class="p">,</span>
- <span class="s2">"Pleural thickening"</span><span class="p">,</span>
- <span class="s2">"Pneumonia"</span><span class="p">,</span>
- <span class="s2">"Fibrosis"</span><span class="p">,</span>
- <span class="s2">"Edema"</span><span class="p">,</span>
- <span class="s2">"Consolidation"</span><span class="p">,</span>
- <span class="p">]</span>
- <span class="n">bars</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"#8C3503"</span><span class="p">)</span>
-
- <span class="n">bars</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#818C2E"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#818C2E"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#818C2E"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#F2921D"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#F2921D"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#F2921D"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">11</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#F2921D"</span><span class="p">)</span>
- <span class="n">bars</span><span class="p">[</span><span class="mi">13</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s2">"#F2921D"</span><span class="p">)</span>
-
- <span class="k">for</span> <span class="n">tick</span> <span class="ow">in</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">():</span>
- <span class="n">tick</span><span class="o">.</span><span class="n">set_rotation</span><span class="p">(</span><span class="mi">90</span><span class="p">)</span>
-
- <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-
- <span class="k">return</span> <span class="n">fig</span></div>
-</pre></div>
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
-
-
- </div>
- <div class="bottom-of-page">
- <div class="left-details">
- <div class="copyright">
- Copyright © 2023, Idiap Research Institute
- </div>
- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-
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- <div class="right-details">
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- <aside class="toc-drawer no-toc">
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-</div><script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
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\ No newline at end of file
diff --git a/sphinx/_modules/ptbench/utils/rc.html b/sphinx/_modules/ptbench/utils/rc.html
deleted file mode 100644
index 32254fff029f481bf31416efc77855fc1a40e2ee..0000000000000000000000000000000000000000
--- a/sphinx/_modules/ptbench/utils/rc.html
+++ /dev/null
@@ -1,366 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en">
- <head><meta charset="utf-8"/>
- <meta name="viewport" content="width=device-width,initial-scale=1"/>
- <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
-
- <!-- Generated with Sphinx 5.3.0 and Furo 2022.12.07 -->
- <title>ptbench.utils.rc - ptbench 1.0.0b0</title>
- <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
- <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?digest=91d0f0d1c444bdcb17a68e833c7a53903343c195" />
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-
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-<style>
- body {
- --color-code-background: #eeffcc;
- --color-code-foreground: black;
-
- }
- @media not print {
- body[data-theme="dark"] {
- --color-code-background: #272822;
- --color-code-foreground: #f8f8f2;
-
- }
- @media (prefers-color-scheme: dark) {
- body:not([data-theme="light"]) {
- --color-code-background: #272822;
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-
- }
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-</style></head>
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-
- <script>
- document.body.dataset.theme = localStorage.getItem("theme") || "auto";
- </script>
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- <h1>Source code for ptbench.utils.rc</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">from</span> <span class="nn">clapper.rc</span> <span class="kn">import</span> <span class="n">UserDefaults</span>
-
-
-<div class="viewcode-block" id="load_rc"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.rc.html#ptbench.utils.rc.load_rc">[docs]</a><span class="k">def</span> <span class="nf">load_rc</span><span class="p">()</span> <span class="o">-></span> <span class="n">UserDefaults</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Returns global configuration variables."""</span>
- <span class="k">return</span> <span class="n">UserDefaults</span><span class="p">(</span><span class="s2">"ptbench.toml"</span><span class="p">)</span></div>
-</pre></div>
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- <article role="main">
- <h1>Source code for ptbench.utils.resources</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="sd">"""Tools for interacting with the running computer or GPU."""</span>
-
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">multiprocessing</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">queue</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">import</span> <span class="nn">subprocess</span>
-<span class="kn">import</span> <span class="nn">time</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">psutil</span>
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-<span class="n">_nvidia_smi</span> <span class="o">=</span> <span class="n">shutil</span><span class="o">.</span><span class="n">which</span><span class="p">(</span><span class="s2">"nvidia-smi"</span><span class="p">)</span>
-<span class="sd">"""Location of the nvidia-smi program, if one exists."""</span>
-
-
-<span class="n">GB</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="mi">30</span><span class="p">)</span>
-<span class="sd">"""The number of bytes in a gigabyte."""</span>
-
-
-<div class="viewcode-block" id="run_nvidia_smi"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.run_nvidia_smi">[docs]</a><span class="k">def</span> <span class="nf">run_nvidia_smi</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">rename</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns GPU information from query.</span>
-
-<span class="sd"> For a comprehensive list of options and help, execute ``nvidia-smi</span>
-<span class="sd"> --help-query-gpu`` on a host with a GPU</span>
-
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> query : list</span>
-<span class="sd"> A list of query strings as defined by ``nvidia-smi --help-query-gpu``</span>
-
-<span class="sd"> rename : :py:class:`list`, Optional</span>
-<span class="sd"> A list of keys to yield in the return value for each entry above. It</span>
-<span class="sd"> gives you the opportunity to rewrite some key names for convenience.</span>
-<span class="sd"> This list, if provided, must be of the same length as ``query``.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> data : :py:class:`tuple`, None</span>
-<span class="sd"> An ordered dictionary (organized as 2-tuples) containing the queried</span>
-<span class="sd"> parameters (``rename`` versions). If ``nvidia-smi`` is not available,</span>
-<span class="sd"> returns ``None``. Percentage information is left alone,</span>
-<span class="sd"> memory information is transformed to gigabytes (floating-point).</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">_nvidia_smi</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">rename</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">rename</span> <span class="o">=</span> <span class="n">query</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">rename</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
- <span class="c1"># Get GPU information based on GPU ID.</span>
- <span class="n">values</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">getoutput</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">%s</span><span class="s2"> --query-gpu=</span><span class="si">%s</span><span class="s2"> --format=csv,noheader --id=</span><span class="si">%s</span><span class="s2">"</span>
- <span class="o">%</span> <span class="p">(</span>
- <span class="n">_nvidia_smi</span><span class="p">,</span>
- <span class="s2">","</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">query</span><span class="p">),</span>
- <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"CUDA_VISIBLE_DEVICES"</span><span class="p">),</span>
- <span class="p">)</span>
- <span class="p">)</span>
- <span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">values</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">","</span><span class="p">)]</span>
- <span class="n">t_values</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">values</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">"%"</span><span class="p">):</span>
- <span class="n">t_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">k</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()))</span>
- <span class="k">elif</span> <span class="n">k</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">"MiB"</span><span class="p">):</span>
- <span class="n">t_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">k</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span> <span class="o">/</span> <span class="mi">1024</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">t_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="c1"># unchanged</span>
- <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rename</span><span class="p">,</span> <span class="n">t_values</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="gpu_constants"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.gpu_constants">[docs]</a><span class="k">def</span> <span class="nf">gpu_constants</span><span class="p">():</span>
-<span class="w"> </span><span class="sd">"""Returns GPU (static) information using nvidia-smi.</span>
-
-<span class="sd"> See :py:func:`run_nvidia_smi` for operational details.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> data : :py:class:`tuple`, None</span>
-<span class="sd"> If ``nvidia-smi`` is not available, returns ``None``, otherwise, we</span>
-<span class="sd"> return an ordered dictionary (organized as 2-tuples) containing the</span>
-<span class="sd"> following ``nvidia-smi`` query information:</span>
-
-<span class="sd"> * ``gpu_name``, as ``gpu_name`` (:py:class:`str`)</span>
-<span class="sd"> * ``driver_version``, as ``gpu_driver_version`` (:py:class:`str`)</span>
-<span class="sd"> * ``memory.total``, as ``gpu_memory_total`` (transformed to gigabytes,</span>
-<span class="sd"> :py:class:`float`)</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">run_nvidia_smi</span><span class="p">(</span>
- <span class="p">(</span><span class="s2">"gpu_name"</span><span class="p">,</span> <span class="s2">"driver_version"</span><span class="p">,</span> <span class="s2">"memory.total"</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"gpu_name"</span><span class="p">,</span> <span class="s2">"gpu_driver_version"</span><span class="p">,</span> <span class="s2">"gpu_memory_total"</span><span class="p">),</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="gpu_log"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.gpu_log">[docs]</a><span class="k">def</span> <span class="nf">gpu_log</span><span class="p">():</span>
-<span class="w"> </span><span class="sd">"""Returns GPU information about current non-static status using nvidia-</span>
-<span class="sd"> smi.</span>
-
-<span class="sd"> See :py:func:`run_nvidia_smi` for operational details.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> data : :py:class:`tuple`, None</span>
-<span class="sd"> If ``nvidia-smi`` is not available, returns ``None``, otherwise, we</span>
-<span class="sd"> return an ordered dictionary (organized as 2-tuples) containing the</span>
-<span class="sd"> following ``nvidia-smi`` query information:</span>
-
-<span class="sd"> * ``memory.used``, as ``gpu_memory_used`` (transformed to gigabytes,</span>
-<span class="sd"> :py:class:`float`)</span>
-<span class="sd"> * ``memory.free``, as ``gpu_memory_free`` (transformed to gigabytes,</span>
-<span class="sd"> :py:class:`float`)</span>
-<span class="sd"> * ``100*memory.used/memory.total``, as ``gpu_memory_percent``,</span>
-<span class="sd"> (:py:class:`float`, in percent)</span>
-<span class="sd"> * ``utilization.gpu``, as ``gpu_percent``,</span>
-<span class="sd"> (:py:class:`float`, in percent)</span>
-<span class="sd"> """</span>
- <span class="n">retval</span> <span class="o">=</span> <span class="n">run_nvidia_smi</span><span class="p">(</span>
- <span class="p">(</span>
- <span class="s2">"memory.total"</span><span class="p">,</span>
- <span class="s2">"memory.used"</span><span class="p">,</span>
- <span class="s2">"memory.free"</span><span class="p">,</span>
- <span class="s2">"utilization.gpu"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">(</span>
- <span class="s2">"gpu_memory_total"</span><span class="p">,</span>
- <span class="s2">"gpu_memory_used"</span><span class="p">,</span>
- <span class="s2">"gpu_memory_free"</span><span class="p">,</span>
- <span class="s2">"gpu_percent"</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">)</span>
-
- <span class="c1"># re-compose the output to generate expected values</span>
- <span class="k">return</span> <span class="p">(</span>
- <span class="n">retval</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="c1"># gpu_memory_used</span>
- <span class="n">retval</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="c1"># gpu_memory_free</span>
- <span class="p">(</span><span class="s2">"gpu_memory_percent"</span><span class="p">,</span> <span class="mi">100</span> <span class="o">*</span> <span class="p">(</span><span class="n">retval</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">retval</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">])),</span>
- <span class="n">retval</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="c1"># gpu_percent</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="cpu_constants"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.cpu_constants">[docs]</a><span class="k">def</span> <span class="nf">cpu_constants</span><span class="p">():</span>
-<span class="w"> </span><span class="sd">"""Returns static CPU information about the current system.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> data : tuple</span>
-<span class="sd"> An ordered dictionary (organized as 2-tuples) containing these entries:</span>
-
-<span class="sd"> 0. ``cpu_memory_total`` (:py:class:`float`): total memory available,</span>
-<span class="sd"> in gigabytes</span>
-<span class="sd"> 1. ``cpu_count`` (:py:class:`int`): number of logical CPUs available</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="p">(</span>
- <span class="p">(</span><span class="s2">"cpu_memory_total"</span><span class="p">,</span> <span class="n">psutil</span><span class="o">.</span><span class="n">virtual_memory</span><span class="p">()</span><span class="o">.</span><span class="n">total</span> <span class="o">/</span> <span class="n">GB</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"cpu_count"</span><span class="p">,</span> <span class="n">psutil</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">(</span><span class="n">logical</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
- <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="CPULogger"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.CPULogger">[docs]</a><span class="k">class</span> <span class="nc">CPULogger</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""Logs CPU information using :py:mod:`psutil`</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> pid : :py:class:`int`, Optional</span>
-<span class="sd"> Process identifier of the main process (parent process) to observe</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pid</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
- <span class="n">this</span> <span class="o">=</span> <span class="n">psutil</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span><span class="n">pid</span><span class="o">=</span><span class="n">pid</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span> <span class="o">=</span> <span class="p">[</span><span class="n">this</span><span class="p">]</span> <span class="o">+</span> <span class="n">this</span><span class="o">.</span><span class="n">children</span><span class="p">(</span><span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="c1"># touch cpu_percent() at least once for all processes in the cluster</span>
- <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">cpu_percent</span><span class="p">(</span><span class="n">interval</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">]</span>
-
-<div class="viewcode-block" id="CPULogger.log"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.CPULogger.log">[docs]</a> <span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns current process cluster information.</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> data : tuple</span>
-<span class="sd"> An ordered dictionary (organized as 2-tuples) containing these entries:</span>
-
-<span class="sd"> 0. ``cpu_memory_used`` (:py:class:`float`): total memory used from</span>
-<span class="sd"> the system, in gigabytes</span>
-<span class="sd"> 1. ``cpu_rss`` (:py:class:`float`): RAM currently used by</span>
-<span class="sd"> process and children, in gigabytes</span>
-<span class="sd"> 2. ``cpu_vms`` (:py:class:`float`): total memory (RAM + swap) currently</span>
-<span class="sd"> used by process and children, in gigabytes</span>
-<span class="sd"> 3. ``cpu_percent`` (:py:class:`float`): percentage of the total CPU</span>
-<span class="sd"> used by this process and children (recursively) since last call</span>
-<span class="sd"> (first time called should be ignored). This number depends on the</span>
-<span class="sd"> number of CPUs in the system and can be greater than 100%</span>
-<span class="sd"> 4. ``cpu_processes`` (:py:class:`int`): total number of processes</span>
-<span class="sd"> including self and children (recursively)</span>
-<span class="sd"> 5. ``cpu_open_files`` (:py:class:`int`): total number of open files by</span>
-<span class="sd"> self and children</span>
-<span class="sd"> """</span>
- <span class="c1"># check all cluster components and update process list</span>
- <span class="c1"># done so we can keep the cpu_percent() initialization</span>
- <span class="n">stored_children</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
- <span class="n">current_children</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">children</span><span class="p">(</span><span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
- <span class="n">keep_children</span> <span class="o">=</span> <span class="n">stored_children</span> <span class="o">-</span> <span class="n">current_children</span>
- <span class="n">new_children</span> <span class="o">=</span> <span class="n">current_children</span> <span class="o">-</span> <span class="n">stored_children</span>
- <span class="n">gone</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">new_children</span><span class="p">:</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="n">k</span><span class="o">.</span><span class="n">cpu_percent</span><span class="p">(</span><span class="n">interval</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
- <span class="k">except</span> <span class="p">(</span><span class="n">psutil</span><span class="o">.</span><span class="n">ZombieProcess</span><span class="p">,</span> <span class="n">psutil</span><span class="o">.</span><span class="n">NoSuchProcess</span><span class="p">):</span>
- <span class="c1"># child process is gone meanwhile</span>
- <span class="c1"># update the intermediate list for this time</span>
- <span class="n">gone</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
- <span class="n">new_children</span> <span class="o">=</span> <span class="n">new_children</span> <span class="o">-</span> <span class="n">gone</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span> <span class="o">=</span> <span class="p">(</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">keep_children</span><span class="p">)</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">new_children</span><span class="p">)</span>
- <span class="p">)</span>
-
- <span class="n">memory_info</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">cpu_percent</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">open_files</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">gone</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">:</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="n">memory_info</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="o">.</span><span class="n">memory_info</span><span class="p">())</span>
- <span class="n">cpu_percent</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="o">.</span><span class="n">cpu_percent</span><span class="p">(</span><span class="n">interval</span><span class="o">=</span><span class="kc">None</span><span class="p">))</span>
- <span class="n">open_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">k</span><span class="o">.</span><span class="n">open_files</span><span class="p">()))</span>
- <span class="k">except</span> <span class="p">(</span><span class="n">psutil</span><span class="o">.</span><span class="n">ZombieProcess</span><span class="p">,</span> <span class="n">psutil</span><span class="o">.</span><span class="n">NoSuchProcess</span><span class="p">):</span>
- <span class="c1"># child process is gone meanwhile, just ignore it</span>
- <span class="c1"># it is too late to update any intermediate list</span>
- <span class="c1"># at this point, but ensures to update counts later on</span>
- <span class="n">gone</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="p">(</span>
- <span class="p">(</span><span class="s2">"cpu_memory_used"</span><span class="p">,</span> <span class="n">psutil</span><span class="o">.</span><span class="n">virtual_memory</span><span class="p">()</span><span class="o">.</span><span class="n">used</span> <span class="o">/</span> <span class="n">GB</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"cpu_rss"</span><span class="p">,</span> <span class="nb">sum</span><span class="p">([</span><span class="n">k</span><span class="o">.</span><span class="n">rss</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">memory_info</span><span class="p">])</span> <span class="o">/</span> <span class="n">GB</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"cpu_vms"</span><span class="p">,</span> <span class="nb">sum</span><span class="p">([</span><span class="n">k</span><span class="o">.</span><span class="n">vms</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">memory_info</span><span class="p">])</span> <span class="o">/</span> <span class="n">GB</span><span class="p">),</span>
- <span class="p">(</span><span class="s2">"cpu_percent"</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">cpu_percent</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"cpu_processes"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cluster</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">gone</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"cpu_open_files"</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">open_files</span><span class="p">)),</span>
- <span class="p">)</span></div></div>
-
-
-<span class="k">class</span> <span class="nc">_InformationGatherer</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""A container to store monitoring information.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> has_gpu : bool</span>
-<span class="sd"> A flag indicating if we have a GPU installed on the platform or not</span>
-
-<span class="sd"> main_pid : int</span>
-<span class="sd"> The main process identifier to monitor</span>
-
-<span class="sd"> logger : logging.Logger</span>
-<span class="sd"> A logger to be used for logging messages</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">has_gpu</span><span class="p">,</span> <span class="n">main_pid</span><span class="p">,</span> <span class="n">logger</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">cpu_logger</span> <span class="o">=</span> <span class="n">CPULogger</span><span class="p">(</span><span class="n">main_pid</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cpu_logger</span><span class="o">.</span><span class="n">log</span><span class="p">()]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">cpu_keys_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">has_gpu</span> <span class="o">=</span> <span class="n">has_gpu</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">logger</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_gpu</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">keys</span> <span class="o">+=</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">gpu_log</span><span class="p">()]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">]</span>
-
- <span class="k">def</span> <span class="nf">acc</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Accumulates another measurement."""</span>
- <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cpu_logger</span><span class="o">.</span><span class="n">log</span><span class="p">()):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_gpu</span><span class="p">:</span>
- <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gpu_log</span><span class="p">()):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cpu_keys_len</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
- <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Returns the current data."""</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">"CPU/GPU logger was not able to collect any data"</span><span class="p">)</span>
- <span class="n">retval</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
- <span class="n">retval</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">values</span><span class="p">))</span>
- <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">retval</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_monitor_worker</span><span class="p">(</span><span class="n">interval</span><span class="p">,</span> <span class="n">has_gpu</span><span class="p">,</span> <span class="n">main_pid</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">queue</span><span class="p">,</span> <span class="n">logging_level</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""A monitoring worker that measures resources and returns lists.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ==========</span>
-
-<span class="sd"> interval : int, float</span>
-<span class="sd"> Number of seconds to wait between each measurement (maybe a floating</span>
-<span class="sd"> point number as accepted by :py:func:`time.sleep`)</span>
-
-<span class="sd"> has_gpu : bool</span>
-<span class="sd"> A flag indicating if we have a GPU installed on the platform or not</span>
-
-<span class="sd"> main_pid : int</span>
-<span class="sd"> The main process identifier to monitor</span>
-
-<span class="sd"> stop : :py:class:`multiprocessing.Event`</span>
-<span class="sd"> Indicates if we should continue running or stop</span>
-
-<span class="sd"> queue : :py:class:`queue.Queue`</span>
-<span class="sd"> A queue, to send monitoring information back to the spawner</span>
-
-<span class="sd"> logging_level: int</span>
-<span class="sd"> The logging level to use for logging from launched processes</span>
-<span class="sd"> """</span>
- <span class="n">logger</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">log_to_stderr</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging_level</span><span class="p">)</span>
- <span class="n">ra</span> <span class="o">=</span> <span class="n">_InformationGatherer</span><span class="p">(</span><span class="n">has_gpu</span><span class="p">,</span> <span class="n">main_pid</span><span class="p">,</span> <span class="n">logger</span><span class="p">)</span>
-
- <span class="k">while</span> <span class="ow">not</span> <span class="n">stop</span><span class="o">.</span><span class="n">is_set</span><span class="p">():</span>
- <span class="k">try</span><span class="p">:</span>
- <span class="n">ra</span><span class="o">.</span><span class="n">acc</span><span class="p">()</span> <span class="c1"># guarantees at least an entry will be available</span>
- <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
- <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
- <span class="s2">"Iterative CPU/GPU logging did not work properly "</span> <span class="s2">"this once"</span><span class="p">,</span>
- <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
- <span class="p">)</span>
- <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span> <span class="c1"># wait half a second, and try again!</span>
-
- <span class="n">queue</span><span class="o">.</span><span class="n">put</span><span class="p">(</span><span class="n">ra</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
-
-
-<div class="viewcode-block" id="ResourceMonitor"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.ResourceMonitor">[docs]</a><span class="k">class</span> <span class="nc">ResourceMonitor</span><span class="p">:</span>
-<span class="w"> </span><span class="sd">"""An external, non-blocking CPU/GPU resource monitor.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> interval : int, float</span>
-<span class="sd"> Number of seconds to wait between each measurement (maybe a floating</span>
-<span class="sd"> point number as accepted by :py:func:`time.sleep`)</span>
-
-<span class="sd"> has_gpu : bool</span>
-<span class="sd"> A flag indicating if we have a GPU installed on the platform or not</span>
-
-<span class="sd"> main_pid : int</span>
-<span class="sd"> The main process identifier to monitor</span>
-
-<span class="sd"> logging_level: int</span>
-<span class="sd"> The logging level to use for logging from launched processes</span>
-<span class="sd"> """</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interval</span><span class="p">,</span> <span class="n">has_gpu</span><span class="p">,</span> <span class="n">main_pid</span><span class="p">,</span> <span class="n">logging_level</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">has_gpu</span> <span class="o">=</span> <span class="n">has_gpu</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">main_pid</span> <span class="o">=</span> <span class="n">main_pid</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">event</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Event</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Queue</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logging_level</span> <span class="o">=</span> <span class="n">logging_level</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">monitor</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span>
- <span class="n">target</span><span class="o">=</span><span class="n">_monitor_worker</span><span class="p">,</span>
- <span class="n">name</span><span class="o">=</span><span class="s2">"ResourceMonitorProcess"</span><span class="p">,</span>
- <span class="n">args</span><span class="o">=</span><span class="p">(</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">interval</span><span class="p">,</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">has_gpu</span><span class="p">,</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">main_pid</span><span class="p">,</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">event</span><span class="p">,</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">logging_level</span><span class="p">,</span>
- <span class="p">),</span>
- <span class="p">)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="ResourceMonitor.monitored_keys"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.resources.html#ptbench.utils.resources.ResourceMonitor.monitored_keys">[docs]</a> <span class="nd">@staticmethod</span>
- <span class="k">def</span> <span class="nf">monitored_keys</span><span class="p">(</span><span class="n">has_gpu</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">_InformationGatherer</span><span class="p">(</span><span class="n">has_gpu</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">logger</span><span class="p">)</span><span class="o">.</span><span class="n">keys</span></div>
-
- <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Starts the monitoring process."""</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">monitor</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
- <span class="k">return</span> <span class="bp">self</span>
-
- <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">exc</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Stops the monitoring process and returns the summary of</span>
-<span class="sd"> observations."""</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">monitor</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">monitor</span><span class="o">.</span><span class="n">exitcode</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"CPU/GPU resource monitor process exited with code "</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">monitor</span><span class="o">.</span><span class="n">exitcode</span><span class="si">}</span><span class="s2">. Check logs for errors!"</span>
- <span class="p">)</span>
-
- <span class="k">try</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">timeout</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval</span><span class="p">)</span>
- <span class="k">except</span> <span class="n">queue</span><span class="o">.</span><span class="n">Empty</span><span class="p">:</span>
- <span class="n">logger</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
- <span class="sa">f</span><span class="s2">"CPU/GPU resource monitor did not provide anything when "</span>
- <span class="sa">f</span><span class="s2">"joined (even after a </span><span class="si">{</span><span class="mi">2</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">interval</span><span class="si">}</span><span class="s2">-second timeout - "</span>
- <span class="sa">f</span><span class="s2">"this is normally due to exceptions on the monitoring process. "</span>
- <span class="sa">f</span><span class="s2">"Check above for other exceptions."</span>
- <span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="kc">None</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># summarize the returned data by creating means</span>
- <span class="n">summary</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">values</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"cpu_processes"</span><span class="p">,</span> <span class="s2">"cpu_open_files"</span><span class="p">):</span>
- <span class="n">summary</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">values</span><span class="p">)))</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">summary</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">values</span><span class="p">)))</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">summary</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">k</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">summary</span><span class="p">)</span></div>
-</pre></div>
- </article>
- </div>
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- <div class="bottom-of-page">
- <div class="left-details">
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- </div>
- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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diff --git a/sphinx/_modules/ptbench/utils/summary.html b/sphinx/_modules/ptbench/utils/summary.html
deleted file mode 100644
index f8b1557f00cccf35d8d2dd0af9d3d8cd532da63c..0000000000000000000000000000000000000000
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-<html class="no-js" lang="en">
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- --color-code-foreground: black;
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- <h1>Source code for ptbench.utils.summary</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="c1"># Adapted from https://github.com/pytorch/pytorch/issues/2001#issuecomment-405675488</span>
-
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
-
-<span class="kn">from</span> <span class="nn">torch.nn.modules.module</span> <span class="kn">import</span> <span class="n">_addindent</span>
-
-
-<span class="c1"># ignore this space!</span>
-<span class="k">def</span> <span class="nf">_repr</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
- <span class="c1"># We treat the extra repr like the sub-module, one item per line</span>
- <span class="n">extra_lines</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">extra_repr</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">extra_repr</span><span class="p">()</span>
- <span class="c1"># empty string will be split into list ['']</span>
- <span class="k">if</span> <span class="n">extra_repr</span><span class="p">:</span>
- <span class="n">extra_lines</span> <span class="o">=</span> <span class="n">extra_repr</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
- <span class="n">child_lines</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">total_params</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">_modules</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">mod_str</span><span class="p">,</span> <span class="n">num_params</span> <span class="o">=</span> <span class="n">_repr</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
- <span class="n">mod_str</span> <span class="o">=</span> <span class="n">_addindent</span><span class="p">(</span><span class="n">mod_str</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
- <span class="n">child_lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"("</span> <span class="o">+</span> <span class="n">key</span> <span class="o">+</span> <span class="s2">"): "</span> <span class="o">+</span> <span class="n">mod_str</span><span class="p">)</span>
- <span class="n">total_params</span> <span class="o">+=</span> <span class="n">num_params</span>
- <span class="n">lines</span> <span class="o">=</span> <span class="n">extra_lines</span> <span class="o">+</span> <span class="n">child_lines</span>
-
- <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">_parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="s2">"dtype"</span><span class="p">):</span>
- <span class="n">total_params</span> <span class="o">+=</span> <span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
- <span class="n">main_str</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">_get_name</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"("</span>
- <span class="k">if</span> <span class="n">lines</span><span class="p">:</span>
- <span class="c1"># simple one-liner info, which most builtin Modules will use</span>
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">extra_lines</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">child_lines</span><span class="p">:</span>
- <span class="n">main_str</span> <span class="o">+=</span> <span class="n">extra_lines</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">main_str</span> <span class="o">+=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> "</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2"> "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span>
-
- <span class="n">main_str</span> <span class="o">+=</span> <span class="s2">")"</span>
- <span class="n">main_str</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">", </span><span class="si">{</span><span class="n">total_params</span><span class="si">:</span><span class="s2">,</span><span class="si">}</span><span class="s2"> params"</span>
- <span class="k">return</span> <span class="n">main_str</span><span class="p">,</span> <span class="n">total_params</span>
-
-
-<div class="viewcode-block" id="summary"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.summary.html#ptbench.utils.summary.summary">[docs]</a><span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Counts the number of parameters in each model layer.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> model : :py:class:`torch.nn.Module`</span>
-<span class="sd"> model to summarize</span>
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> repr : str</span>
-<span class="sd"> a multiline string representation of the network</span>
-
-<span class="sd"> nparam : int</span>
-<span class="sd"> number of parameters</span>
-<span class="sd"> """</span>
- <span class="k">return</span> <span class="n">_repr</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div>
-</pre></div>
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- <h1>Source code for ptbench.utils.table</h1><div class="highlight"><pre>
-<span></span><span class="c1"># SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch></span>
-<span class="c1">#</span>
-<span class="c1"># SPDX-License-Identifier: GPL-3.0-or-later</span>
-
-<span class="kn">import</span> <span class="nn">tabulate</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">auc</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">precision_recall_curve</span> <span class="k">as</span> <span class="n">pr_curve</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span> <span class="k">as</span> <span class="n">r_curve</span>
-
-<span class="kn">from</span> <span class="nn">..engine.evaluator</span> <span class="kn">import</span> <span class="n">posneg</span>
-<span class="kn">from</span> <span class="nn">..utils.measure</span> <span class="kn">import</span> <span class="n">base_measures</span><span class="p">,</span> <span class="n">bayesian_measures</span>
-
-
-<div class="viewcode-block" id="performance_table"><a class="viewcode-back" href="../../../api/utils/ptbench.utils.table.html#ptbench.utils.table.performance_table">[docs]</a><span class="k">def</span> <span class="nf">performance_table</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span>
-<span class="w"> </span><span class="sd">"""Tables result comparison in a given format.</span>
-
-<span class="sd"> Parameters</span>
-<span class="sd"> ----------</span>
-
-<span class="sd"> data : dict</span>
-<span class="sd"> A dictionary in which keys are strings defining plot labels and values</span>
-<span class="sd"> are dictionaries with two entries:</span>
-
-<span class="sd"> * ``df``: :py:class:`pandas.DataFrame`</span>
-
-<span class="sd"> A dataframe that is produced by our predictor engine containing</span>
-<span class="sd"> the following columns: ``filename``, ``likelihood``,</span>
-<span class="sd"> ``ground_truth``.</span>
-
-<span class="sd"> * ``threshold``: :py:class:`list`</span>
-
-<span class="sd"> A threshold to compute measures.</span>
-
-
-<span class="sd"> fmt : str</span>
-<span class="sd"> One of the formats supported by tabulate.</span>
-
-
-<span class="sd"> Returns</span>
-<span class="sd"> -------</span>
-
-<span class="sd"> table : str</span>
-<span class="sd"> A table in a specific format</span>
-<span class="sd"> """</span>
- <span class="n">headers</span> <span class="o">=</span> <span class="p">[</span>
- <span class="s2">"Dataset"</span><span class="p">,</span>
- <span class="s2">"T"</span><span class="p">,</span>
- <span class="s2">"F1 (95% CI)"</span><span class="p">,</span>
- <span class="s2">"Prec (95% CI)"</span><span class="p">,</span>
- <span class="s2">"Recall/Sen (95% CI)"</span><span class="p">,</span>
- <span class="s2">"Spec (95% CI)"</span><span class="p">,</span>
- <span class="s2">"Acc (95% CI)"</span><span class="p">,</span>
- <span class="s2">"AUC (PRC)"</span><span class="p">,</span>
- <span class="s2">"AUC (ROC)"</span><span class="p">,</span>
- <span class="p">]</span>
-
- <span class="n">table</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
- <span class="n">entry</span> <span class="o">=</span> <span class="p">[</span>
- <span class="n">k</span><span class="p">,</span>
- <span class="n">v</span><span class="p">[</span><span class="s2">"threshold"</span><span class="p">],</span>
- <span class="p">]</span>
-
- <span class="n">df</span> <span class="o">=</span> <span class="n">v</span><span class="p">[</span><span class="s2">"df"</span><span class="p">]</span>
-
- <span class="n">gt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
- <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"likelihood"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
- <span class="n">threshold</span> <span class="o">=</span> <span class="n">v</span><span class="p">[</span><span class="s2">"threshold"</span><span class="p">]</span>
-
- <span class="n">tp_tensor</span><span class="p">,</span> <span class="n">fp_tensor</span><span class="p">,</span> <span class="n">tn_tensor</span><span class="p">,</span> <span class="n">fn_tensor</span> <span class="o">=</span> <span class="n">posneg</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">gt</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
-
- <span class="c1"># calc measures from scalars</span>
- <span class="n">tp_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tp_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">fp_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fp_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">tn_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tn_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
- <span class="n">fn_count</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fn_tensor</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-
- <span class="n">base_m</span> <span class="o">=</span> <span class="n">base_measures</span><span class="p">(</span>
- <span class="n">tp_count</span><span class="p">,</span>
- <span class="n">fp_count</span><span class="p">,</span>
- <span class="n">tn_count</span><span class="p">,</span>
- <span class="n">fn_count</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="n">bayes_m</span> <span class="o">=</span> <span class="n">bayesian_measures</span><span class="p">(</span>
- <span class="n">tp_count</span><span class="p">,</span>
- <span class="n">fp_count</span><span class="p">,</span>
- <span class="n">tn_count</span><span class="p">,</span>
- <span class="n">fn_count</span><span class="p">,</span>
- <span class="n">lambda_</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
- <span class="n">coverage</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
- <span class="p">)</span>
-
- <span class="c1"># statistics based on the "assigned" threshold (a priori, less biased)</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">{:.2f}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">, </span><span class="si">{:.2f}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">base_m</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="p">)</span> <span class="c1"># f1</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">{:.2f}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">, </span><span class="si">{:.2f}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">base_m</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="p">)</span> <span class="c1"># precision</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">{:.2f}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">, </span><span class="si">{:.2f}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">base_m</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="p">)</span> <span class="c1"># recall/sensitivity</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">{:.2f}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">, </span><span class="si">{:.2f}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">base_m</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="p">)</span> <span class="c1"># specificity</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="s2">"</span><span class="si">{:.2f}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">, </span><span class="si">{:.2f}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
- <span class="n">base_m</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">bayes_m</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
- <span class="p">)</span>
- <span class="p">)</span> <span class="c1"># accuracy</span>
-
- <span class="n">prec</span><span class="p">,</span> <span class="n">recall</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">pr_curve</span><span class="p">(</span><span class="n">gt</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
- <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">r_curve</span><span class="p">(</span><span class="n">gt</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
-
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">(</span><span class="n">recall</span><span class="p">,</span> <span class="n">prec</span><span class="p">))</span>
- <span class="n">entry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">))</span>
-
- <span class="n">table</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">tabulate</span><span class="o">.</span><span class="n">tabulate</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">headers</span><span class="p">,</span> <span class="n">tablefmt</span><span class="o">=</span><span class="n">fmt</span><span class="p">,</span> <span class="n">floatfmt</span><span class="o">=</span><span class="s2">".3f"</span><span class="p">)</span></div>
-</pre></div>
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
-
-
- </div>
- <div class="bottom-of-page">
- <div class="left-details">
- <div class="copyright">
- Copyright © 2023, Idiap Research Institute
- </div>
- Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-
- <a href="https://github.com/pradyunsg/furo">Furo</a>
-
- </div>
- <div class="right-details">
-
- </div>
- </div>
-
- </footer>
- </div>
- <aside class="toc-drawer no-toc">
-
-
-
- </aside>
- </div>
-</div><script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
- <script src="../../../_static/jquery.js"></script>
- <script src="../../../_static/underscore.js"></script>
- <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
- <script src="../../../_static/doctools.js"></script>
- <script src="../../../_static/sphinx_highlight.js"></script>
- <script src="../../../_static/scripts/furo.js"></script>
- <script src="../../../_static/clipboard.min.js"></script>
- <script src="../../../_static/copybutton.js"></script>
- <script src="../../../_static/tabs.js"></script>
- </body>
-</html>
\ No newline at end of file
diff --git a/sphinx/_sources/api.rst.txt b/sphinx/_sources/api.rst.txt
deleted file mode 100644
index 7314356253fefa05fc6de69fadefccbd61471aca..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api.rst.txt
+++ /dev/null
@@ -1,115 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.api:
-
-============
- Python API
-============
-
-This section includes information for using the Python API of
-``ptbench``.
-
-
-.. _ptbench.api.data:
-
-Data Methods
-------------
-
-Auxiliary classes and methods to define raw dataset iterators.
-
-.. autosummary::
- :toctree: api/data
-
- ptbench.data.sample
- ptbench.data.dataset
- ptbench.data.utils
- ptbench.data.loader
- ptbench.data.transforms
- ptbench.configs.datasets
-
-
-.. _ptbench.api.data.raw:
-
-Raw Dataset Access
-------------------
-
-Direct data-access through iterators.
-
-.. autosummary::
- :toctree: api/data/raw
-
- ptbench.data.hivtb_RS
- ptbench.data.tbpoc
- ptbench.data.montgomery_RS
- ptbench.data.padchest
- ptbench.data.hivtb
- ptbench.data.indian_RS
- ptbench.data.shenzhen_RS
- ptbench.data.tbpoc_RS
- ptbench.data.shenzhen
- ptbench.data.montgomery
- ptbench.data.indian
- ptbench.data.nih_cxr14_re
- ptbench.data.padchest_RS
-
-
-.. _ptbench.api.models:
-
-Models
-------
-
-CNN and other models implemented.
-
-.. autosummary::
- :toctree: api/models
-
- ptbench.models.alexnet
- ptbench.models.densenet
- ptbench.models.densenet_rs
- ptbench.models.logistic_regression
- ptbench.models.normalizer
- ptbench.models.pasa
- ptbench.models.signs_to_tb
-
-
-.. _ptbench.api.engines:
-
-Command engines
----------------
-
-Functions to actuate on the data.
-
-.. autosummary::
- :toctree: api/engine
-
- ptbench.engine.trainer
- ptbench.engine.predictor
- ptbench.engine.evaluator
-
-
-.. _ptbench.api.utils:
-
-Various utilities
------------------
-
-Reusable auxiliary functions.
-
-.. autosummary::
- :toctree: api/utils
-
- ptbench.utils.checkpointer
- ptbench.utils.download
- ptbench.utils.grad_cams
- ptbench.utils.measure
- ptbench.utils.model_serialization
- ptbench.utils.model_zoo
- ptbench.utils.plot
- ptbench.utils.rc
- ptbench.utils.resources
- ptbench.utils.summary
- ptbench.utils.table
-
-
-.. include:: links.rst
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.rst.txt
deleted file mode 100644
index d1d0bd8ad49228aac975cc5e3c979eb14ad30cff..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.hivtb.fold\_0
-======================================
-
-.. automodule:: ptbench.configs.datasets.hivtb.fold_0
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/hivtb/fold_0.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.rst.txt
deleted file mode 100644
index 7250478cd88941d0ff4488166ccd55ef206cf6d8..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.hivtb.fold\_0\_rgb
-===========================================
-
-.. automodule:: ptbench.configs.datasets.hivtb.fold_0_rgb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/hivtb/fold_0_rgb.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.rst.txt
deleted file mode 100644
index 55aface44eacb392083c799c2257fc8102cb97bd..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.hivtb\_RS.fold\_0
-==========================================
-
-.. automodule:: ptbench.configs.datasets.hivtb_RS.fold_0
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/hivtb_RS/fold_0.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.default.rst.txt
deleted file mode 100644
index bed4841a403dbe59a714e0f5eaceff8309c96175..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian.default
-=======================================
-
-.. automodule:: ptbench.configs.datasets.indian.default
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian/default.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0.rst.txt
deleted file mode 100644
index bc1252b7e9cf15cdd80f06d265c11ff4e17ad5b2..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian.fold\_0
-=======================================
-
-.. automodule:: ptbench.configs.datasets.indian.fold_0
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian/fold_0.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.rst.txt
deleted file mode 100644
index 2a0d229967a53a318a66d63d7d5f44d8e78a1824..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian.fold\_0\_rgb
-============================================
-
-.. automodule:: ptbench.configs.datasets.indian.fold_0_rgb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian/fold_0_rgb.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.rgb.rst.txt
deleted file mode 100644
index 4d8e82babcc864b6426977c5bdf057a1f9dc5522..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian.rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian.rgb
-===================================
-
-.. automodule:: ptbench.configs.datasets.indian.rgb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian/rgb.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.default.rst.txt
deleted file mode 100644
index 3fd70bd6cdb79d47f2c71721cec0dfef9d291e92..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian\_RS.default
-===========================================
-
-.. automodule:: ptbench.configs.datasets.indian_RS.default
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian_RS/default.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.rst.txt
deleted file mode 100644
index ad8ca65bcf3fc1e8b607264f86e2cfa6343d4b2f..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.indian\_RS.fold\_0
-===========================================
-
-.. automodule:: ptbench.configs.datasets.indian_RS.fold_0
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/indian_RS/fold_0.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.default.rst.txt
deleted file mode 100644
index d8f14803a5f13b5e712e6d4dacb5802508497cd9..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.mc\_ch.default
-=======================================
-
-.. automodule:: ptbench.configs.datasets.mc_ch.default
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/mc_ch/default.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.rst.txt
deleted file mode 100644
index a197193f312213274e99f46c4a0e57c5aa95b536..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.mc\_ch.fold\_0
-=======================================
-
-.. automodule:: ptbench.configs.datasets.mc_ch.fold_0
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. literalinclude:: ../../../../src/ptbench/configs/datasets/mc_ch/fold_0.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.rst.txt
deleted file mode 100644
index aa82a092277b201a803c0514bc00e617980f0116..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.mc\_ch.fold\_0\_rgb
-============================================
-
-.. automodule:: ptbench.configs.datasets.mc_ch.fold_0_rgb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-.. literalinclude:: ../../../../src/ptbench/configs/datasets/mc_ch/fold_0_rgb.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.rst.txt
deleted file mode 100644
index 12401d0cade32abd43ab6105653895ad6521dd10..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.mc\_ch.rgb
-===================================
-
-.. automodule:: ptbench.configs.datasets.mc_ch.rgb
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-.. literalinclude:: ../../../../src/ptbench/configs/datasets/mc_ch/rgb.py
- :start-at: import
\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.rst.txt
deleted file mode 100644
index 68cefd2d04cd4c33af1cdf9b2e2510f4538abd66..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.mc\_ch\_RS.default
-===========================================
-
-.. automodule:: ptbench.configs.datasets.mc_ch_RS.default
-
-
-
-
-
-
-
-
-
-
-
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-
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-
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-.. literalinclude:: ../../../../src/ptbench/configs/datasets/mc_ch_RS/default.py
- :start-at: import
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.rst.txt
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-ptbench.configs.datasets.mc\_ch\_RS.fold\_0
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-ptbench.configs.datasets.mc\_ch\_in.default
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-ptbench.configs.datasets.mc\_ch\_in.fold\_0
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-ptbench.configs.datasets.mc\_ch\_in.fold\_0\_rgb
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-ptbench.configs.datasets.mc\_ch\_in.rgb
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.rst.txt
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-ptbench.configs.datasets.mc\_ch\_in\_RS.default
-===============================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.rst.txt
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-ptbench.configs.datasets.mc\_ch\_in\_RS.fold\_0
-===============================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.rst.txt
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-ptbench.configs.datasets.mc\_ch\_in\_pc.default
-===============================================
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-ptbench.configs.datasets.mc\_ch\_in\_pc.rgb
-===========================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.mc\_ch\_in\_pc\_RS.default
-===================================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery.default.rst.txt
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-ptbench.configs.datasets.montgomery.default
-===========================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.montgomery.fold\_0
-===========================================
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-ptbench.configs.datasets.montgomery.fold\_0\_rgb
-================================================
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-ptbench.configs.datasets.montgomery.rgb
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.montgomery\_RS.default
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.rst.txt
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-ptbench.configs.datasets.montgomery\_RS.fold\_0
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.nih\_cxr14\_re.cardiomegaly
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+++ /dev/null
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-ptbench.configs.datasets.nih\_cxr14\_re.default
-===============================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.nih\_cxr14\_re\_pc.idiap
-=================================================
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.padchest.cardiomegaly\_idiap
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+++ /dev/null
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+++ /dev/null
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-ptbench.configs.datasets.padchest.no\_tb\_idiap
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+++ /dev/null
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-ptbench.configs.datasets.padchest.tb\_idiap
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+++ /dev/null
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.rst.txt
deleted file mode 100644
index 0de09b928e320ea627e2eecb101f891d908bcb69..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.padchest\_RS.tb\_idiap
-===============================================
-
-.. automodule:: ptbench.configs.datasets.padchest_RS.tb_idiap
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.default.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.shenzhen.default
-=========================================
-
-.. automodule:: ptbench.configs.datasets.shenzhen.default
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.rst.txt
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.shenzhen.fold\_0
-=========================================
-
-.. automodule:: ptbench.configs.datasets.shenzhen.fold_0
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.shenzhen.fold\_0\_rgb
-==============================================
-
-.. automodule:: ptbench.configs.datasets.shenzhen.fold_0_rgb
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.rst.txt
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+++ /dev/null
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-ptbench.configs.datasets.shenzhen.rgb
-=====================================
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-.. automodule:: ptbench.configs.datasets.shenzhen.rgb
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.shenzhen\_RS.default
-=============================================
-
-.. automodule:: ptbench.configs.datasets.shenzhen_RS.default
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.shenzhen\_RS.fold\_0
-=============================================
-
-.. automodule:: ptbench.configs.datasets.shenzhen_RS.fold_0
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.tbpoc.fold\_0
-======================================
-
-.. automodule:: ptbench.configs.datasets.tbpoc.fold_0
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diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.tbpoc.fold\_0\_rgb
-===========================================
-
-.. automodule:: ptbench.configs.datasets.tbpoc.fold_0_rgb
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.rst.txt b/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.rst.txt
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--- a/sphinx/_sources/api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.datasets.tbpoc\_RS.fold\_0
-==========================================
-
-.. automodule:: ptbench.configs.datasets.tbpoc_RS.fold_0
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\ No newline at end of file
diff --git a/sphinx/_sources/api/configs/models/ptbench.configs.models.alexnet.rst.txt b/sphinx/_sources/api/configs/models/ptbench.configs.models.alexnet.rst.txt
deleted file mode 100644
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--- a/sphinx/_sources/api/configs/models/ptbench.configs.models.alexnet.rst.txt
+++ /dev/null
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-ptbench.configs.models.alexnet
-==============================
-
-.. automodule:: ptbench.configs.models.alexnet
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.models.alexnet\_pretrained
-==========================================
-
-.. automodule:: ptbench.configs.models.alexnet_pretrained
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diff --git a/sphinx/_sources/api/configs/models/ptbench.configs.models.densenet.rst.txt b/sphinx/_sources/api/configs/models/ptbench.configs.models.densenet.rst.txt
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.models.densenet
-===============================
-
-.. automodule:: ptbench.configs.models.densenet
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.models.densenet\_pretrained
-===========================================
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-.. automodule:: ptbench.configs.models.densenet_pretrained
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diff --git a/sphinx/_sources/api/configs/models/ptbench.configs.models.logistic_regression.rst.txt b/sphinx/_sources/api/configs/models/ptbench.configs.models.logistic_regression.rst.txt
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+++ /dev/null
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-ptbench.configs.models.logistic\_regression
-===========================================
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-.. automodule:: ptbench.configs.models.logistic_regression
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+++ /dev/null
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-ptbench.configs.models.pasa
-===========================
-
-.. automodule:: ptbench.configs.models.pasa
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.models.signs\_to\_tb
-====================================
-
-.. automodule:: ptbench.configs.models.signs_to_tb
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diff --git a/sphinx/_sources/api/configs/models/ptbench.configs.models_datasets.densenet_rs.rst.txt b/sphinx/_sources/api/configs/models/ptbench.configs.models_datasets.densenet_rs.rst.txt
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+++ /dev/null
@@ -1,26 +0,0 @@
-ptbench.configs.models\_datasets.densenet\_rs
-=============================================
-
-.. automodule:: ptbench.configs.models_datasets.densenet_rs
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--- a/sphinx/_sources/api/data/ptbench.configs.datasets.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-ptbench.configs.datasets
-========================
-
-.. automodule:: ptbench.configs.datasets
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- RANDOM_ROTATION
-
-
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- .. rubric:: Functions
-
- .. autosummary::
-
- get_positive_weights
- get_samples_weights
- make_dataset
- make_subset
-
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diff --git a/sphinx/_sources/api/data/ptbench.data.dataset.rst.txt b/sphinx/_sources/api/data/ptbench.data.dataset.rst.txt
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--- a/sphinx/_sources/api/data/ptbench.data.dataset.rst.txt
+++ /dev/null
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-ptbench.data.dataset
-====================
-
-.. automodule:: ptbench.data.dataset
-
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- .. rubric:: Classes
-
- .. autosummary::
-
- CSVDataset
- JSONDataset
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diff --git a/sphinx/_sources/api/data/ptbench.data.loader.rst.txt b/sphinx/_sources/api/data/ptbench.data.loader.rst.txt
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--- a/sphinx/_sources/api/data/ptbench.data.loader.rst.txt
+++ /dev/null
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-ptbench.data.loader
-===================
-
-.. automodule:: ptbench.data.loader
-
-
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-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- load_pil
- load_pil_baw
- load_pil_rgb
- make_delayed
-
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diff --git a/sphinx/_sources/api/data/ptbench.data.sample.rst.txt b/sphinx/_sources/api/data/ptbench.data.sample.rst.txt
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--- a/sphinx/_sources/api/data/ptbench.data.sample.rst.txt
+++ /dev/null
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-ptbench.data.sample
-===================
-
-.. automodule:: ptbench.data.sample
-
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- .. rubric:: Classes
-
- .. autosummary::
-
- DelayedSample
- Sample
-
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diff --git a/sphinx/_sources/api/data/ptbench.data.transforms.rst.txt b/sphinx/_sources/api/data/ptbench.data.transforms.rst.txt
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--- a/sphinx/_sources/api/data/ptbench.data.transforms.rst.txt
+++ /dev/null
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-ptbench.data.transforms
-=======================
-
-.. automodule:: ptbench.data.transforms
-
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- .. rubric:: Classes
-
- .. autosummary::
-
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- RemoveBlackBorders
- SingleAutoLevel16to8
-
-
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-
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diff --git a/sphinx/_sources/api/data/ptbench.data.utils.rst.txt b/sphinx/_sources/api/data/ptbench.data.utils.rst.txt
deleted file mode 100644
index 048d0d07b06aa869290739693fbc4e98b30608e9..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/ptbench.data.utils.rst.txt
+++ /dev/null
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-ptbench.data.utils
-==================
-
-.. automodule:: ptbench.data.utils
-
-
-
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- .. rubric:: Classes
-
- .. autosummary::
-
- SampleListDataset
-
-
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diff --git a/sphinx/_sources/api/data/raw/ptbench.data.hivtb.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.hivtb.rst.txt
deleted file mode 100644
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--- a/sphinx/_sources/api/data/raw/ptbench.data.hivtb.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.hivtb
-==================
-
-.. automodule:: ptbench.data.hivtb
-
-
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- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
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-
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-
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diff --git a/sphinx/_sources/api/data/raw/ptbench.data.hivtb_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.hivtb_RS.rst.txt
deleted file mode 100644
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--- a/sphinx/_sources/api/data/raw/ptbench.data.hivtb_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.hivtb\_RS
-======================
-
-.. automodule:: ptbench.data.hivtb_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
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-
-
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-
-
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-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.indian.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.indian.rst.txt
deleted file mode 100644
index 37bd3e391421b74dc8c40f6525b00a20b80926b6..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.indian.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.indian
-===================
-
-.. automodule:: ptbench.data.indian
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.indian_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.indian_RS.rst.txt
deleted file mode 100644
index 297c3445a7af352b87cfa3544a4d8f214d78116e..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.indian_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.indian\_RS
-=======================
-
-.. automodule:: ptbench.data.indian_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.montgomery.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.montgomery.rst.txt
deleted file mode 100644
index ab64eb16bda165616c67b27caa330abca2b536e3..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.montgomery.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.montgomery
-=======================
-
-.. automodule:: ptbench.data.montgomery
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.montgomery_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.montgomery_RS.rst.txt
deleted file mode 100644
index 715b3d465e6ebaa568d1b5c8542d830f68a3a843..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.montgomery_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.montgomery\_RS
-===========================
-
-.. automodule:: ptbench.data.montgomery_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.nih_cxr14_re.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.nih_cxr14_re.rst.txt
deleted file mode 100644
index 0c11eb2a38ed8963d107e665ab3d17f629c032fb..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.nih_cxr14_re.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.nih\_cxr14\_re
-===========================
-
-.. automodule:: ptbench.data.nih_cxr14_re
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.padchest.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.padchest.rst.txt
deleted file mode 100644
index b1b36a9dd9ea77d1d25549bcae034f169d54b847..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.padchest.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.padchest
-=====================
-
-.. automodule:: ptbench.data.padchest
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.padchest_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.padchest_RS.rst.txt
deleted file mode 100644
index 54471bb33e9350f904e0321ab9628e007b97ddb0..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.padchest_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.padchest\_RS
-=========================
-
-.. automodule:: ptbench.data.padchest_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.shenzhen.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.shenzhen.rst.txt
deleted file mode 100644
index e0dc9e2d87d3fce196719ccf6b93eea2ab31b721..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.shenzhen.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.shenzhen
-=====================
-
-.. automodule:: ptbench.data.shenzhen
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.shenzhen_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.shenzhen_RS.rst.txt
deleted file mode 100644
index 388a6167cd8199901f43e0e405ff1e1c41e51f93..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.shenzhen_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.shenzhen\_RS
-=========================
-
-.. automodule:: ptbench.data.shenzhen_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.tbpoc.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.tbpoc.rst.txt
deleted file mode 100644
index 20bc77e8d1237b50c48aec1a7a323db157bd7a09..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.tbpoc.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.tbpoc
-==================
-
-.. automodule:: ptbench.data.tbpoc
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/data/raw/ptbench.data.tbpoc_RS.rst.txt b/sphinx/_sources/api/data/raw/ptbench.data.tbpoc_RS.rst.txt
deleted file mode 100644
index 3877089121df31de2846717ba3382d0932ad71d5..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/data/raw/ptbench.data.tbpoc_RS.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.data.tbpoc\_RS
-======================
-
-.. automodule:: ptbench.data.tbpoc_RS
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- dataset
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/engine/ptbench.engine.evaluator.rst.txt b/sphinx/_sources/api/engine/ptbench.engine.evaluator.rst.txt
deleted file mode 100644
index f2539251a234839f0a8f0aea1d763e892fdf1c8f..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/engine/ptbench.engine.evaluator.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-ptbench.engine.evaluator
-========================
-
-.. automodule:: ptbench.engine.evaluator
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- eer_threshold
- posneg
- run
- sample_measures_for_threshold
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/engine/ptbench.engine.predictor.rst.txt b/sphinx/_sources/api/engine/ptbench.engine.predictor.rst.txt
deleted file mode 100644
index dea82e30a333f34480a6560227aa6bf280a732fe..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/engine/ptbench.engine.predictor.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.engine.predictor
-========================
-
-.. automodule:: ptbench.engine.predictor
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- run
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/engine/ptbench.engine.trainer.rst.txt b/sphinx/_sources/api/engine/ptbench.engine.trainer.rst.txt
deleted file mode 100644
index 4e5f04572e9755ecd76be12774957676f3fee3f0..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/engine/ptbench.engine.trainer.rst.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-ptbench.engine.trainer
-======================
-
-.. automodule:: ptbench.engine.trainer
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- check_exist_logfile
- check_gpu
- checkpointer_process
- create_logfile_fields
- run
- save_model_summary
- static_information_to_csv
- torch_evaluation
- train_epoch
- validate_epoch
- write_log_info
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.alexnet.rst.txt b/sphinx/_sources/api/models/ptbench.models.alexnet.rst.txt
deleted file mode 100644
index cf291c701e84bffe84f0cbb128b4c96a1a65057f..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.alexnet.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.alexnet
-======================
-
-.. automodule:: ptbench.models.alexnet
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_alexnet
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Alexnet
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.densenet.rst.txt b/sphinx/_sources/api/models/ptbench.models.densenet.rst.txt
deleted file mode 100644
index 9d32ac9c70e2db009bc90b03b3d2582fddfe4373..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.densenet.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.densenet
-=======================
-
-.. automodule:: ptbench.models.densenet
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_densenet
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Densenet
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.densenet_rs.rst.txt b/sphinx/_sources/api/models/ptbench.models.densenet_rs.rst.txt
deleted file mode 100644
index 8e45bccadfb3b2e761ecb0d3e2db958a031b138d..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.densenet_rs.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.densenet\_rs
-===========================
-
-.. automodule:: ptbench.models.densenet_rs
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_densenetrs
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- DensenetRS
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.logistic_regression.rst.txt b/sphinx/_sources/api/models/ptbench.models.logistic_regression.rst.txt
deleted file mode 100644
index 1f4dd5df1b409ee2846d97f31bc16d1f4e75e72e..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.logistic_regression.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.logistic\_regression
-===================================
-
-.. automodule:: ptbench.models.logistic_regression
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_logistic_regression
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- LogisticRegression
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.normalizer.rst.txt b/sphinx/_sources/api/models/ptbench.models.normalizer.rst.txt
deleted file mode 100644
index e2aef15bede6ed09fcc2cf0619b9506b18a0a112..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.normalizer.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.models.normalizer
-=========================
-
-.. automodule:: ptbench.models.normalizer
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- TorchVisionNormalizer
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.pasa.rst.txt b/sphinx/_sources/api/models/ptbench.models.pasa.rst.txt
deleted file mode 100644
index c912b94e2b724f0c0dd065e627bbe54d89bfe3d9..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.pasa.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.pasa
-===================
-
-.. automodule:: ptbench.models.pasa
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_pasa
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- PASA
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/models/ptbench.models.signs_to_tb.rst.txt b/sphinx/_sources/api/models/ptbench.models.signs_to_tb.rst.txt
deleted file mode 100644
index 16eec5b96a3cb6a1d76a3dcd4b456bcbbda62f4e..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/models/ptbench.models.signs_to_tb.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-ptbench.models.signs\_to\_tb
-============================
-
-.. automodule:: ptbench.models.signs_to_tb
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- build_signs_to_tb
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- SignsToTB
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.checkpointer.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.checkpointer.rst.txt
deleted file mode 100644
index 9522160818cd268d5527d634dd4c7115a419b376..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.checkpointer.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.utils.checkpointer
-==========================
-
-.. automodule:: ptbench.utils.checkpointer
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Checkpointer
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.download.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.download.rst.txt
deleted file mode 100644
index ff2ed71545c621ea1a822e644ef825f8b365c9a9..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.download.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.utils.download
-======================
-
-.. automodule:: ptbench.utils.download
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- download_to_tempfile
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.grad_cams.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.grad_cams.rst.txt
deleted file mode 100644
index c85bb760f696b9729b8fbbf11f7ad5cd13d2cb81..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.grad_cams.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-ptbench.utils.grad\_cams
-========================
-
-.. automodule:: ptbench.utils.grad_cams
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- BaseWrapper
- GradCAM
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.measure.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.measure.rst.txt
deleted file mode 100644
index 5bcc40d5d0306186d378491cd9d697709df5c312..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.measure.rst.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-ptbench.utils.measure
-=====================
-
-.. automodule:: ptbench.utils.measure
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- base_measures
- bayesian_measures
- beta_credible_region
- get_centered_maxf1
- tricky_division
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- SmoothedValue
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.model_serialization.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.model_serialization.rst.txt
deleted file mode 100644
index fb3566f715e44291eb758542f20a590999ebad9c..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.model_serialization.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-ptbench.utils.model\_serialization
-==================================
-
-.. automodule:: ptbench.utils.model_serialization
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- align_and_update_state_dicts
- load_state_dict
- strip_prefix_if_present
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.model_zoo.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.model_zoo.rst.txt
deleted file mode 100644
index ce3dd27781e84118aea9d21345af174fd44e2418..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.model_zoo.rst.txt
+++ /dev/null
@@ -1,36 +0,0 @@
-ptbench.utils.model\_zoo
-========================
-
-.. automodule:: ptbench.utils.model_zoo
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- modelurls
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- cache_url
- download_url_to_file
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.plot.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.plot.rst.txt
deleted file mode 100644
index 02f20946d3978bc7b64e4503ee5e1fdc94ccf7c9..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.plot.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-ptbench.utils.plot
-==================
-
-.. automodule:: ptbench.utils.plot
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- precision_recall_f1iso
- relevance_analysis_plot
- roc_curve
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.rc.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.rc.rst.txt
deleted file mode 100644
index edb7b41261370a8989b3b6b46cdad0dcf33f8e39..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.rc.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.utils.rc
-================
-
-.. automodule:: ptbench.utils.rc
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- load_rc
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.resources.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.resources.rst.txt
deleted file mode 100644
index 3504fcefb62e7b7a947ad8a3e9618e4afc8e57b6..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.resources.rst.txt
+++ /dev/null
@@ -1,45 +0,0 @@
-ptbench.utils.resources
-=======================
-
-.. automodule:: ptbench.utils.resources
-
-
-
- .. rubric:: Module Attributes
-
- .. autosummary::
-
- GB
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- cpu_constants
- gpu_constants
- gpu_log
- run_nvidia_smi
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- CPULogger
- ResourceMonitor
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.summary.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.summary.rst.txt
deleted file mode 100644
index be48907bdecabb47345f36200f4f3bb5fab6b713..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.summary.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.utils.summary
-=====================
-
-.. automodule:: ptbench.utils.summary
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- summary
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/api/utils/ptbench.utils.table.rst.txt b/sphinx/_sources/api/utils/ptbench.utils.table.rst.txt
deleted file mode 100644
index 1c2b2a38ac6e365d9864164075ef5bd5d52e02f4..0000000000000000000000000000000000000000
--- a/sphinx/_sources/api/utils/ptbench.utils.table.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-ptbench.utils.table
-===================
-
-.. automodule:: ptbench.utils.table
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- performance_table
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sphinx/_sources/cli.rst.txt b/sphinx/_sources/cli.rst.txt
deleted file mode 100644
index 950e3cdc6182d330d5bdd40586a51eae71560e8a..0000000000000000000000000000000000000000
--- a/sphinx/_sources/cli.rst.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-.. Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.cli:
-
-========================
- Command-line Interface
-========================
-
-This section contains an overview of command-line applications shipped with
-this package.
-
-
-.. click:: ptbench.scripts.cli:cli
- :prog: ptbench
- :nested: full
-
-
-.. include:: links.rst
diff --git a/sphinx/_sources/config.rst.txt b/sphinx/_sources/config.rst.txt
deleted file mode 100644
index af8fa995a3247f5bd735627b9d59e6de436025c4..0000000000000000000000000000000000000000
--- a/sphinx/_sources/config.rst.txt
+++ /dev/null
@@ -1,111 +0,0 @@
-.. Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.config:
-
-Preset Configurations
----------------------
-
-This module contains preset configurations for baseline CNN architectures and
-datasets.
-
-
-Models
-======
-
-.. autosummary::
- :toctree: api/configs/models
- :template: config.rst
-
- ptbench.configs.models.alexnet
- ptbench.configs.models.alexnet_pretrained
- ptbench.configs.models.densenet
- ptbench.configs.models.densenet_pretrained
- ptbench.configs.models.logistic_regression
- ptbench.configs.models.pasa
- ptbench.configs.models.signs_to_tb
- ptbench.configs.models_datasets.densenet_rs
-
-
-.. _ptbench.configs.datasets:
-
-Datasets
-========
-
-Datasets include iterative accessors to raw data
-(:ref:`ptbench.setup.datasets`) including data pre-processing and augmentation,
-if applicable. Use these datasets for training and evaluating your models.
-
-.. autosummary::
- :toctree: api/configs/datasets
- :template: config.rst
-
- ptbench.configs.datasets.indian.default
- ptbench.configs.datasets.indian.rgb
- ptbench.configs.datasets.indian_RS.default
- ptbench.configs.datasets.mc_ch.default
- ptbench.configs.datasets.mc_ch.rgb
- ptbench.configs.datasets.mc_ch_RS.default
- ptbench.configs.datasets.mc_ch_in.default
- ptbench.configs.datasets.mc_ch_in.rgb
- ptbench.configs.datasets.mc_ch_in_RS.default
- ptbench.configs.datasets.mc_ch_in_pc.default
- ptbench.configs.datasets.mc_ch_in_pc.rgb
- ptbench.configs.datasets.mc_ch_in_pc_RS.default
- ptbench.configs.datasets.montgomery.default
- ptbench.configs.datasets.montgomery.rgb
- ptbench.configs.datasets.montgomery_RS.default
- ptbench.configs.datasets.nih_cxr14_re.cardiomegaly
- ptbench.configs.datasets.nih_cxr14_re.default
- ptbench.configs.datasets.nih_cxr14_re_pc.idiap
- ptbench.configs.datasets.padchest.cardiomegaly_idiap
- ptbench.configs.datasets.padchest.idiap
- ptbench.configs.datasets.padchest.no_tb_idiap
- ptbench.configs.datasets.padchest.tb_idiap
- ptbench.configs.datasets.padchest.tb_idiap_rgb
- ptbench.configs.datasets.padchest_RS.tb_idiap
- ptbench.configs.datasets.shenzhen.default
- ptbench.configs.datasets.shenzhen.rgb
- ptbench.configs.datasets.shenzhen_RS.default
-
-
-.. _ptbench.configs.datasets.folds:
-
-Cross-Validation Datasets
-=========================
-
-We support cross-validation with precise preset folds. In this section, you
-will find the configuration for the first fold (fold-0) for all supported
-datasets. Nine other folds are available for every configuration (from 1 to
-9), making up 10 folds per supported dataset.
-
-
-.. autosummary::
- :toctree: api/configs/datasets
- :template: config.rst
-
- ptbench.configs.datasets.hivtb.fold_0
- ptbench.configs.datasets.hivtb.fold_0_rgb
- ptbench.configs.datasets.hivtb_RS.fold_0
- ptbench.configs.datasets.indian.fold_0
- ptbench.configs.datasets.indian.fold_0_rgb
- ptbench.configs.datasets.indian_RS.fold_0
- ptbench.configs.datasets.mc_ch.fold_0
- ptbench.configs.datasets.mc_ch.fold_0_rgb
- ptbench.configs.datasets.mc_ch_RS.fold_0
- ptbench.configs.datasets.mc_ch_in.fold_0
- ptbench.configs.datasets.mc_ch_in.fold_0_rgb
- ptbench.configs.datasets.mc_ch_in_RS.fold_0
- ptbench.configs.datasets.montgomery.fold_0
- ptbench.configs.datasets.montgomery.fold_0_rgb
- ptbench.configs.datasets.montgomery_RS.fold_0
- ptbench.configs.datasets.shenzhen.fold_0
- ptbench.configs.datasets.shenzhen.fold_0_rgb
- ptbench.configs.datasets.shenzhen_RS.fold_0
- ptbench.configs.datasets.tbpoc.fold_0
- ptbench.configs.datasets.tbpoc.fold_0_rgb
- ptbench.configs.datasets.tbpoc_RS.fold_0
-
-
-.. include:: links.rst
diff --git a/sphinx/_sources/index.rst.txt b/sphinx/_sources/index.rst.txt
deleted file mode 100644
index 964e4f35d4916227ca78b731b1d87acc3d1d89d3..0000000000000000000000000000000000000000
--- a/sphinx/_sources/index.rst.txt
+++ /dev/null
@@ -1,68 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench:
-
-=========================================================
- Active Pulmonary Tuberculosis Detection On Chest X-Rays
-=========================================================
-
-.. todolist::
-
-Benchmarks of convolutional neural network (CNN) architectures applied to
-Pulmonary Tuberculosis (TB) detection on chest X-rays (CXR).
-
-Please use the BibTeX reference below to cite this work:
-
-.. code:: bibtex
-
- @INPROCEEDINGS{raposo_union_2022,
- author = {Raposo, Geoffrey and Trajman, Anete and Anjos, Andr{\'{e}}},
- month = 11,
- title = {Pulmonary Tuberculosis Screening from Radiological Signs on Chest X-Ray Images Using Deep Models},
- booktitle = {Union World Conference on Lung Health},
- year = {2022},
- date = {2022-11-01},
- organization = {The Union},
- }
-
- @TECHREPORT{Raposo_Idiap-Com-01-2021,
- author = {Raposo, Geoffrey},
- keywords = {deep learning, generalization, Interpretability, transfer learning, Tuberculosis Detection},
- projects = {Idiap},
- month = {7},
- title = {Active tuberculosis detection from frontal chest X-ray images},
- type = {Idiap-Com},
- number = {Idiap-Com-01-2021},
- year = {2021},
- institution = {Idiap},
- url = {https://gitlab.idiap.ch/biosignal/software/ptbench},
- pdf = {https://publidiap.idiap.ch/downloads/reports/2021/Raposo_Idiap-Com-01-2021.pdf}
- }
-
-
-User Guide
-----------
-
-.. toctree::
- :maxdepth: 2
-
- install
- usage/index
- results/index
- references
- cli
- config
- api
-
-
-Indices and tables
-------------------
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
-
-.. include:: links.rst
diff --git a/sphinx/_sources/install.rst.txt b/sphinx/_sources/install.rst.txt
deleted file mode 100644
index 0ec7ad7d7d9dfbefb6b3106ddcfc164d41d7f435..0000000000000000000000000000000000000000
--- a/sphinx/_sources/install.rst.txt
+++ /dev/null
@@ -1,228 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.install:
-
-==============
- Installation
-==============
-
-We support two installation modes, through pip_, or mamba_ (conda).
-
-
-.. tab:: pip
-
- Stable, from PyPI:
-
- .. code:: sh
-
- pip install ptbench
-
- Latest beta, from GitLab package registry:
-
- .. code:: sh
-
- pip install --pre --index-url https://gitlab.idiap.ch/api/v4/groups/software/-/packages/pypi/simple --extra-index-url https://pypi.org/simple ptbench
-
- .. tip::
-
- To avoid long command-lines you may configure pip to define the indexes and
- package search priorities as you like.
-
-
-.. tab:: mamba/conda
-
- Stable:
-
- .. code:: sh
-
- mamba install -c https://www.idiap.ch/software/biosignal/conda -c conda-forge ptbench
-
- Latest beta:
-
- .. code:: sh
-
- mamba install -c https://www.idiap.ch/software/biosignal/conda/label/beta -c conda-forge ptbench
-
-
-.. _ptbench.setup:
-
-Setup
------
-
-A configuration file may be useful to setup global options that should be often
-reused. The location of the configuration file depends on the value of the
-environment variable ``$XDG_CONFIG_HOME``, but defaults to
-``~/.config/ptbench.toml``. You may edit this file using your preferred
-editor.
-
-Here is an example configuration file that may be useful as a starting point:
-
-.. code:: toml
-
- [datadir]
- indian = "/Users/myself/dbs/tbxpredict"
- montgomery = "/Users/myself/dbs/montgomery-xrayset"
- shenzhen = "/Users/myself/dbs/shenzhen"
- nih_cxr14_re = "/Users/myself/dbs/nih-cxr14-re"
-
- [nih_cxr14_re]
- idiap_folder_structure = false # set to `true` if at Idiap
-
-
-.. tip::
-
- To get a list of valid data directories that can be configured, execute:
-
- .. code:: sh
-
- ptbench dataset list
-
-
- You must procure and download datasets by yourself. The raw data is not
- included in this package as we are not authorised to redistribute it.
-
- To check whether the downloaded version is consistent with the structure
- that is expected by this package, run:
-
- .. code:: sh
-
- ptbench dataset check montgomery
-
-
-.. _ptbench.setup.datasets:
-
-Supported Datasets
-==================
-
-Here is a list of currently supported datasets in this package, alongside
-notable properties. Each dataset name is linked to the location where
-raw data can be downloaded. The list of images in each split is available
-in the source code.
-
-
-.. _ptbench.setup.datasets.tb:
-
-Tuberculosis datasets
-~~~~~~~~~~~~~~~~~~~~~
-
-The following datasets contain only the tuberculosis final diagnosis (0 or 1).
-In addition to the splits presented in the following table, 10 folds
-(for cross-validation) randomly generated are available for these datasets.
-
-.. list-table::
-
- * - Dataset
- - Reference
- - H x W
- - Samples
- - Training
- - Validation
- - Test
- * - Montgomery_
- - [MONTGOMERY-SHENZHEN-2014]_
- - 4020 x 4892
- - 138
- - 88
- - 22
- - 28
- * - Shenzhen_
- - [MONTGOMERY-SHENZHEN-2014]_
- - Varying
- - 662
- - 422
- - 107
- - 133
- * - Indian_
- - [INDIAN-2013]_
- - Varying
- - 155
- - 83
- - 20
- - 52
-
-
-.. _ptbench.setup.datasets.tb+signs:
-
-Tuberculosis + radiological findings dataset
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following dataset contains both the tuberculosis final diagnosis (0 or 1)
-and radiological findings.
-
-.. list-table::
-
- * - Dataset
- - Reference
- - H x W
- - Samples
- - Train
- - Test
- * - PadChest_
- - [PADCHEST-2019]_
- - Varying
- - 160'861
- - 160'861
- - 0
-
-
-.. _ptbench.setup.datasets.signs:
-
-Radiological findings datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following dataset contains only the radiological findings without any
-information about tuberculosis.
-
-.. note::
-
- NIH CXR14 labels for training and validation sets are the relabeled
- versions done by the author of the CheXNeXt study [CHEXNEXT-2018]_.
-
-.. list-table::
-
- * - Dataset
- - Reference
- - H x W
- - Samples
- - Training
- - Validation
- - Test
- * - NIH_CXR14_re_
- - [NIH-CXR14-2017]_
- - 1024 x 1024
- - 109'041
- - 98'637
- - 6'350
- - 4'054
-
-
-.. _ptbench.setup.datasets.hiv-tb:
-
-HIV-Tuberculosis datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following datasets contain only the tuberculosis final diagnosis (0 or 1)
-and come from HIV infected patients. 10 folds (for cross-validation) randomly
-generated are available for these datasets.
-
-Please contact the authors of these datasets to have access to the data.
-
-.. list-table::
-
- * - Dataset
- - Reference
- - H x W
- - Samples
- * - TB POC
- - [TB-POC-2018]_
- - 2048 x 2500
- - 407
- * - HIV TB
- - [HIV-TB-2019]_
- - 2048 x 2500
- - 243
-
-
-.. include:: links.rst
diff --git a/sphinx/_sources/references.rst.txt b/sphinx/_sources/references.rst.txt
deleted file mode 100644
index 4897ee5bebf8a8937fa247d2f4fc8a199279ff56..0000000000000000000000000000000000000000
--- a/sphinx/_sources/references.rst.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-
-.. coding=utf-8
-
-============
- References
-============
-
-.. [MONTGOMERY-SHENZHEN-2014] *Jaeger S, Candemir S, Antani S, Wáng YX, Lu PX,
- Thoma G.*, **Two public chest X-ray datasets for computer-aided screening of
- pulmonary diseases.**, Quant Imaging Med Surg. 2014;4(6):475â€477.
- https://dx.doi.org/10.3978%2Fj.issn.2223-4292.2014.11.20
-
-.. [INDIAN-2013] https://sourceforge.net/projects/tbxpredict/
-
-.. [PASA-2019] *Pasa, F., Golkov, V., Pfeiffer, F. et al.*,
- **Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis
- Screening and Visualization.** Sci Rep 9, 6268 (2019).
- https://doi.org/10.1038/s41598-019-42557-4
-
-.. [SIMARD-2003] *P. Y. Simard, D. Steinkraus and J. C. Platt*,
- **Best practices for convolutional neural networks applied to visual
- document analysis**, Seventh International Conference on Document Analysis
- and Recognition, 2003. Proceedings., Edinburgh, UK, 2003, pp. 958-963.
- https://doi.org/10.1109/ICDAR.2003.1227801
-
-.. [CHEXNEXT-2018] *Rajpurkar Pranav, Jeremy Irvin, Robyn L. Ball, Kaylie Zhu,
- Brandon Yang, Hershel Mehta, Tony Duan, et al.*, **Deep Learning for Chest
- Radiograph Diagnosis: A Retrospective Comparison of the CheXNeXt Algorithm
- to Practicing Radiologists**. PLOS Medicine 15, náµ’ 11 (20 november 2018):
- e1002686. https://doi.org/10.1371/journal.pmed.1002686
-
-.. [NIH-CXR14-2017] *Xiaosong Wang et al.*, **ChestX-Ray8: Hospital-Scale
- Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification
- and Localization of Common Thorax Diseases.** 2017 IEEE Conference on
- Computer Vision and Pattern Recognition (CVPR). 2017 IEEE Conference on
- Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE,
- July 2017, pp. 3462–3471. doi: 10.1109/CVPR.2017.369.
- http://ieeexplore.ieee.org/document/8099852/
-
-.. [PADCHEST-2019] *Aurelia Bustos et al.*, **PadChest: A large chest x-ray
- image dataset with multi-label annotated reports** Medical Image Analysis,
- Volume 66, 2020, 101797, ISSN 1361-8415. doi: 10.1016/j.media.2020.101797.
- https://www.sciencedirect.com/science/article/abs/pii/S1361841520301614
-
-.. [GOUTTE-2005] *C. Goutte and E. Gaussier*, **A probabilistic interpretation
- of precision, recall and F-score, with implication for evaluation**,
- European conference on Advances in Information Retrieval Research, 2005.
- https://doi.org/10.1007/978-3-540-31865-1_25
-
-.. [TB-POC-2018] *Griesel, Rulan and Stewart, Annemie and van der Plas, Helen
- and Sikhondze, Welile and Rangaka, Molebogeng X and Nicol, Mark P and
- Kengne, Andre P and Mendelson, Marc and Maartens, Gary*, **Optimizing
- Tuberculosis Diagnosis in Human Immunodeficiency Virus–Infected Inpatients
- Meeting the Criteria of Seriously Ill in the World Health Organization
- Algorithm**, Clinical Infectious Diseases, 2017.
- https://doi.org/10.1093/cid/cix988
-
-.. [HIV-TB-2019] *Van Hoving, D. J. et al.*, **Brief report: real-world
- performance and interobserver agreement of urine lipoarabinomannan in
- diagnosing HIV-Associated tuberculosis in an emergency center.**,
- J. Acquir. Immune Defic. Syndr. 1999 81, e10–e14 (2019).
diff --git a/sphinx/_sources/results/index.rst.txt b/sphinx/_sources/results/index.rst.txt
deleted file mode 100644
index 29ad8a10c4c54f3b8164fd04acab529f30e0ad29..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/index.rst.txt
+++ /dev/null
@@ -1,390 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results:
-
-=========
- Results
-=========
-
-This section summarizes results that can be obtained with this package.
-
-Models optimization
--------------------
-
-In the link below, you will find information about the optimization of each
-model we used.
-
-.. toctree::
- :maxdepth: 2
-
- optimization/pasa
- optimization/densenet
- optimization/logreg
- optimization/signstotb
-
-
-Models training runtime and memory footprint
---------------------------------------------
-
-In the link below, you will find information about the training runtime and the
-memory footprint of each model we used.
-
-.. toctree::
- :maxdepth: 2
-
- runtime
-
-
-AUROC Scores
-------------
-
-* Benchmark results for models: Pasa, DenseNet, SignsToTB
-* Each dataset is split in a training, a validation and a testing subset
-* Datasets names are abbreviated as follows: Montgomery (MC), Shenzhen (CH),
- Indian (IN)
-* Models are only trained on the training subset
-* During the training session, we keep checkpoints for the best performing
- networks based on the validation set. The best performing network during
- training is used for evaluation.
-* Model resource configuration links are linked to the originating
- configuration files used to obtain these results.
-
-K-folding
-^^^^^^^^^
-
-Stratified k-folding has been used (10 folds) to generate these results.
-
-.. tip::
-
- To generate the following results, you first need to predict TB on each
- fold, then use the :ref:`aggregpred command <ptbench.cli>` to aggregate the
- predictions together, and finally evaluate this new file using the
- :ref:`compare command <ptbench.cli>`.
-
-Pasa and DenseNet-121 (random initialization)
-"""""""""""""""""""""""""""""""""""""""""""""
-
-Thresholds used:
-
-* Pasa trained on MC, test on MC, mean threshold: 0.5057
-* Pasa trained on MC-CH, test on MC-CH, mean threshold: 0.4966
-* Pasa trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4135
-* Densenet trained on MC, test on MC, mean threshold: 0.5183
-* Densenet trained on MC-CH, test on MC-CH, mean threshold: 0.2555
-* Densenet trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4037
-
-.. list-table::
-
- * - AUC
- - MC test
- - CH test
- - IN test
- * - Pasa (train: MC)
- - 0.890
- - 0.576
- - 0.642
- * - Pasa (train: MC+CH)
- - 0.870
- - 0.893
- - 0.669
- * - Pasa (train: MC+CH+IN)
- - 0.881
- - 0.898
- - 0.848
- * - DenseNet-121 (train: MC)
- - 0.822
- - 0.607
- - 0.625
- * - DenseNet-121 (train: MC+CH)
- - 0.883
- - 0.905
- - 0.672
- * - DenseNet-121 (train: MC+CH+IN)
- - 0.860
- - 0.917
- - 0.850
-
-.. list-table::
-
- * - .. figure:: img/compare_pasa_mc_kfold_500.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for Pasa model trained on normalized-kfold MC
-
- :py:mod:`Pasa <ptbench.configs.models.pasa>`: Pasa trained on normalized-kfold MC
- - .. figure:: img/compare_pasa_mc_ch_kfold_500.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for Pasa model trained on normalized-kfold MC-CH
-
- :py:mod:`Pasa <ptbench.configs.models.pasa>`: Pasa trained on normalized-kfold MC-CH
- - .. figure:: img/compare_pasa_mc_ch_in_kfold_500.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for Pasa model trained on normalized-kfold MC-CH-IN
-
- :py:mod:`Pasa <ptbench.configs.models.pasa>`: Pasa trained on normalized-kfold MC-CH-IN
- * - .. figure:: img/compare_densenet_mc_kfold_2000.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC
- - .. figure:: img/compare_densenet_mc_ch_kfold_2000.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC-CH
- - .. figure:: img/compare_densenet_mc_ch_in_kfold_2000.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN
-
-DenseNet-121 (pretrained on ImageNet)
-"""""""""""""""""""""""""""""""""""""
-
-Thresholds used:
-
-* DenseNet (pretrained on ImageNet) trained on MC, test on MC, mean threshold: 0.3581
-* DenseNet (pretrained on ImageNet) trained on MC-CH, test on MC-CH, mean threshold: 0.3319
-* DenseNet (pretrained on ImageNet) trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4048
-
-.. list-table::
-
- * - AUC
- - MC test
- - CH test
- - IN test
- * - DenseNet-121 (train: MC)
- - 0.910
- - 0.814
- - 0.817
- * - DenseNet-121 (train: MC+CH)
- - 0.948
- - 0.946
- - 0.816
- * - DenseNet-121 (train: MC+CH+IN)
- - 0.925
- - 0.944
- - 0.911
-
-.. list-table::
-
- * - .. figure:: img/compare_densenetpreIN_mc_kfold_600.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>` DenseNet trained on normalized-kfold MC
- - .. figure:: img/compare_densenetpreIN_mc_ch_kfold_600.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>` DenseNet trained on normalized-kfold MC-CH
- - .. figure:: img/compare_densenetpreIN_mc_ch_ch_kfold_600.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>` DenseNet trained on normalized-kfold MC-CH-IN
-
-Logistic Regression Classifier
-""""""""""""""""""""""""""""""
-
-Thresholds used:
-
-* LogReg trained on MC, test on MC, mean threshold: 0.534
-* LogReg trained on MC-CH, test on MC-CH, mean threshold: 0.2838
-* LogReg trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.2371
-
-.. list-table::
-
- * - AUC
- - MC test
- - CH test
- - IN test
- * - Indirect (train: MC)
- - 0.966
- - 0.867
- - 0.926
- * - Indirect (train: MC+CH)
- - 0.961
- - 0.901
- - 0.928
- * - Indirect (train: MC+CH+IN)
- - 0.951
- - 0.895
- - 0.920
-
-.. list-table::
-
- * - .. figure:: img/compare_logreg_mc_kfold_150.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for LogReg model trained on normalized-kfold MC
-
- :py:mod:`LogReg <ptbench.configs.models.logistic_regression>`: LogReg trained on normalized-kfold MC
- - .. figure:: img/compare_logreg_mc_ch_kfold_100.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for LogReg model trained on normalized-kfold MC-CH
-
- :py:mod:`LogReg <ptbench.configs.models.logistic_regression>`: LogReg trained on normalized-kfold MC-CH
- - .. figure:: img/compare_logreg_mc_ch_in_kfold_100.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for LogReg model trained on normalized-kfold MC-CH-IN
-
- :py:mod:`LogReg <ptbench.configs.models.logistic_regression>`: LogReg trained on normalized-kfold MC-CH-IN
-
-DenseNet-121 (pretrained on ImageNet and NIH CXR14)
-"""""""""""""""""""""""""""""""""""""""""""""""""""
-
-Thresholds used:
-
-* DenseNetPre trained on MC, test on MC, mean threshold: 0.4126
-* DenseNetPre trained on MC-CH, test on MC-CH, mean threshold: 0.3711
-* DenseNetPre trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4255
-
-.. list-table::
-
- * - AUC
- - MC test
- - CH test
- - IN test
- * - DenseNet-121 (train: MC)
- - 0.966
- - 0.917
- - 0.901
- * - DenseNet-121 (train: MC+CH)
- - 0.984
- - 0.979
- - 0.869
- * - DenseNet-121 (train: MC+CH+IN)
- - 0.965
- - 0.978
- - 0.931
-
-.. list-table::
-
- * - .. figure:: img/compare_densenetpre_mc_kfold_300.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC (pretrained on NIH)
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC (pretrained on NIH)
- - .. figure:: img/compare_densenetpre_mc_ch_kfold_300.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH (pretrained on NIH)
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC-CH (pretrained on NIH)
- - .. figure:: img/compare_densenetpre_mc_ch_in_kfold_300.jpg
- :align: center
- :scale: 50%
- :alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN (pretrained on NIH)
-
- :py:mod:`DenseNet <ptbench.configs.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN (pretrained on NIH)
-
-
-Global sensitivity analysis (relevance)
----------------------------------------
-
-Model used to generate the following figures: LogReg trained on MC-CH-IN fold 0 for 100 epochs.
-
-.. tip::
-
- Use the ``--relevance-analysis`` argument of the :ref:`predict command
- <ptbench.cli>` to generate the following plots.
-
-* Green color: likely TB
-* Orange color: Could be TB
-* Dark red color: Unlikely TB
-
-As CH is the largest dataset, its relevance analysis is computed on more images
-and is supposed to be more stable. Similarly, train sets are larger.
-We notice the systematic importance of Nodule, Pleural Thickening, Fibrosis,
-Mass, Consolidation and Pleural Effusion.
-
-.. list-table::
-
- * - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_mc_train.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on train MC
-
- Relevance analysis on train MC
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_mc_validation.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on validation MC
-
- Relevance analysis on validation MC
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_mc_test.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on test MC
-
- Relevance analysis on test MC
- * - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_ch_train.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on train CH
-
- Relevance analysis on train CH
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_ch_validation.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on validation CH
-
- Relevance analysis on validation CH
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_ch_test.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on test CH
-
- Relevance analysis on test CH
- * - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_in_train.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on train IN
-
- Relevance analysis on train IN
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_in_validation.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on validation IN
-
- Relevance analysis on validation IN
- - .. figure:: img/relevance_analysis/logreg_mc_ch_in_f0_100_in_test.jpg
- :align: center
- :scale: 50%
- :alt: Relevance analysis on test IN
-
- Relevance analysis on test IN
-
-
-Ablation study
---------------
-
-Here, we removed the data of each sign, one after the other, from the dataset
-for both model training and prediction. LogReg trained on MC-CH-IN fold 0 for
-100 epochs has been used to generate the following plot.
-
-Predictive capabilities of our logistic regression model after removing the
-data for each radiological sign (d0-d13 correspond, in this order, to
-cardiomegaly, emphysema, effusion, hernia, infiltration, mass, nodule,
-atelectasis, pneumothorax, pleural thickening, pneumonia, fibrosis, edema, and
-consolidation).
-
-- .. figure:: img/rad_sign_drop.png
- :width: 400px
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/results/optimization/densenet.rst.txt b/sphinx/_sources/results/optimization/densenet.rst.txt
deleted file mode 100644
index d6a4c1a87898e9992760d306be76dc9d860c1673..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/optimization/densenet.rst.txt
+++ /dev/null
@@ -1,136 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results.optimization.densenet:
-
-=============================
- Densenet model optimization
-=============================
-
-.. note::
-
- The Densenet121 model contains 7'216'513 parameters.
-
-Training on TB datasets from scratch
-------------------------------------
-
-To select the optimal learning rate and batch size for the training on the
-TB datasets from scratch (densenet not pretrained),
-we did a grid search with the following parameters.
-
-- learning rate of 1e-4, 5e-5 and 1e-5
-- batch size of 4 and 8
-
-We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.
-
-**The minimum validation loss we found is 0.3168 by using a learning rate of
-5e-5 and a batch size of 8**.
-
-
-Minimum validation loss grid search
-===================================
-
-This table indicates the minimum validation loss obtained for each combination
-of learning rate and batch size.
-
-.. list-table::
-
- * - Learning rate
- - Batch size of 4
- - Batch size of 8
- * - 1e-4 (training for 600 epochs)
- - 0.3658
- - 0.3676
- * - 5e-5 (training for 150 epochs)
- - 0.3490
- - **0.3168**
- * - 1e-5 (training for 1000 epochs)
- - 0.3791
- - 0.3831
-
-
-Thresholds selection
-====================
-
-The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.
-
-- Threshold for Densenet trained on MC: 0.599
-- Threshold for Densenet trained on MC-CH: 0.519
-- Threshold for Densenet trained on MC-CH-IN: 0.472
-
-
-Pre-training on NIH CXR14
--------------------------
-
-We used the pretrained Densenet121 model provided by PyTorch. For the
-pretraining on the NIH CXR14 dataset, the hyperparameters from the CheXNeXt
-study were used: batch size of 8, learning rate 1e-4 and the default Adam
-optimizer parameters: beta_1=0.9, beta_2=0.999, epsilon = 1e-8.
-
-
-Fine-tuning on TB datasets
---------------------------
-
-To select the optimal learning rate and batch size for the fine-tuning (after
-the pre-training on NIH CXR14), we did a grid search with the following
-parameters.
-
-- learning rate of 1e-4, 1e-5, 5e-6, 1e-6
-- batch size of 4, 8 and 16
-
-We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.
-
-**The minimum validation loss we found is 0.1511 by using a learning rate of
-1e-4 and a batch size of 8**.
-
-
-Minimum validation loss grid search
-===================================
-
-This table indicates the minimum validation loss obtained for each combination
-of learning rate and batch size.
-
-.. list-table::
-
- * - Learning rate
- - Batch size of 4
- - Batch size of 8
- - Batch size of 16
- * - 1e-4 (training for 300 epochs)
- - 0.2053
- - **0.1511**
- - 0.2372
- * - 1e-5 (training for 500 epochs)
- - 0.1832
- - 0.1931
- - 0.2326
- * - 5e-6 (training for 300 epochs)
- - 0.1932
- - 0.2234
- - 0.2298
- * - 1e-6 (training for 600 epochs)
- - 0.2086
- - 0.2139
- - 0.2138
-
-
-Thresholds selection
-====================
-
-The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.
-
-- Threshold for Densenet trained on MC: 0.688
-- Threshold for Densenet trained on MC-CH: 0.386
-- Threshold for Densenet trained on MC-CH-IN: 0.432
-
-
-Other hyperparameters
-=====================
-
-The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.
diff --git a/sphinx/_sources/results/optimization/logreg.rst.txt b/sphinx/_sources/results/optimization/logreg.rst.txt
deleted file mode 100644
index cc8f8574bddbfdeb32eb14ad4ced8e3d457789a6..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/optimization/logreg.rst.txt
+++ /dev/null
@@ -1,78 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results.optimization.logreg:
-
-===========================
- LogReg model optimization
-===========================
-
-.. note::
-
- The Logistic Regression model contains 15 parameters.
-
-
-LogReg is a logistic regression model created to predict TB presence based on
-the fourteen radiological signs predicted by the DensenetRS model. To train
-this model, we created new features for the Montgomery, Shenzhen and Indian
-dataset by predicting the presence of radiological signs on each of them with
-DensenetRS. Those new datasets versions can be identified by the _RS
-(for Radiological Signs) in their name.
-
-To select the optimal learning rate and the optimal number of neurons for the
-LogReg model, we did a grid search with the following parameters.
-
-- learning rate from 1e-1 to 1e-4
-- batch size of 4, 8 and 16
-
-We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.
-
-**The minimum validation loss we found is 0.3835 by using a learning rate of
-1e-2 and a batch size of 4**.
-
-
-Minimum validation loss grid search
------------------------------------
-
-.. list-table::
-
- * - Learning rate
- - Batch size of 4
- - Batch size of 8
- - Batch size of 16
- * - 1e-1 (training for 50 epochs)
- - 0.3932
- - 0.4013
- - 0.4229
- * - 1e-2 (training for 100 epochs)
- - **0.3835**
- - 0.3998
- - 0.4126
- * - 1e-3 (training for 200 epochs)
- - 0.3875
- - 0.4075
- - 0.4188
- * - 1e-4 (training for 800 epochs)
- - 0.3942
- - 0.4059
- - 0.4123
-
-
-Thresholds selection
---------------------
-
-The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.
-
-- Threshold for LogReg trained on MC: 0.568
-- Threshold for LogReg trained on MC-CH: 0.372
-- Threshold for LogReg trained on MC-CH-IN: 0.430
-
-
-Other hyperparameters
-=====================
-
-The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.
diff --git a/sphinx/_sources/results/optimization/pasa.rst.txt b/sphinx/_sources/results/optimization/pasa.rst.txt
deleted file mode 100644
index 901d99f63cb84eecca18b8ab687600b24b97979d..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/optimization/pasa.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results.optimization.pasa:
-
-=========================
- Pasa model optimization
-=========================
-
-.. note::
-
- The Pasa model contains 201'905 parameters.
-
-
-Model hyperparameters from the original study were used: batch size of 4,
-learning rate 8e-5 and the default Adam optimizer parameters: beta_1=0.9,
-beta_2=0.999, epsilon = 1e-8. The Pasa model has not been pretrained.
-
-
-Thresholds selection
---------------------
-
-The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.
-
-- Threshold for Pasa trained on MC: 0.577
-- Threshold for Pasa trained on MC-CH: 0.417
-- Threshold for Pasa trained on MC-CH-IN: 0.235
diff --git a/sphinx/_sources/results/optimization/signstotb.rst.txt b/sphinx/_sources/results/optimization/signstotb.rst.txt
deleted file mode 100644
index a7b100341ad2089743a66de37d713945ec5544a0..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/optimization/signstotb.rst.txt
+++ /dev/null
@@ -1,74 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results.optimization.signstotb:
-
-==============================
- SignsToTB model optimization
-==============================
-
-.. note::
-
- The SignsToTB model contains 161 parameters.
-
-
-SignsToTB is a shallow model created to predict TB presence based on the
-fourteen radiological signs predicted by the DensenetRS model. To train this
-model, we created new features for the Montgomery, Shenzhen and Indian dataset
-by predicting the presence of radiological signs on each of them with
-DensenetRS. Those new datasets versions can be identified by the _RS (for
-Radiological Signs) in their name.
-
-To select the optimal learning rate and the optimal number of neurons for the
-SignsToTB model, we did a grid search with the following parameters.
-
-- 2, 5, 10 and 14 neurons
-- learning rate of 1e-2, 1e-3, 1e-4 and 1e-5
-- batch size of 4
-- 1'000 epochs
-
-We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.
-
-**The minimum validation loss we found is 0.307 by using a learning rate of
-1e-2 and 10 neurons.**
-
-
-Minimum validation loss grid search
------------------------------------
-
-.. list-table::
-
- * - Learning rate
- - 2 neurons
- - 5 neurons
- - 10 neurons
- - 14 neurons
- * - 1e-2
- - 0.310
- - 0.314
- - **0.307**
- - 0.317
- * - 1e-3
- - 0.336
- - 0.315
- - 0.314
- - 0.317
- * - 1e-4
- - 0.341
- - 0.309
- - 0.321
- - 0.313
- * - 1e-5
- - 0.326
- - 0.357
- - 0.337
- - 0.323
-
-
-Other hyperparameters
-=====================
-
-The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.
diff --git a/sphinx/_sources/results/runtime.rst.txt b/sphinx/_sources/results/runtime.rst.txt
deleted file mode 100644
index 7f079c959bfe068ea5f6e896f5876fa04fd46ff0..0000000000000000000000000000000000000000
--- a/sphinx/_sources/results/runtime.rst.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.results.runtime:
-
-==============================================
- Models training runtime and memory footprint
-==============================================
-
-The Pasa and the Densenet models were trained on a machine equipped with an 11
-GB GeForce GTX 1080 Ti GPU, an 8-core processor, 48 GB of RAM and Debian 10.
-The Logistic Regression model was trained on a Macbook Pro with an 8-core
-processor, 32 GB of RAM and macOS Big Sur.
-
-Pasa
-----
-
-- Training on MC: 2'000 epochs in 2.5 hours, ~2 GB of CPU memory, ~0.75 GB of
- GPU memory
-- Training on MC-CH: 2'000 epochs in 17 hours, ~2 GB of CPU memory, ~0.75 GB of
- GPU memory
-- Training on MC-CH-IN: 2'000 epochs in 16.5 hours, ~2 GB of CPU memory, ~0.75
- GB of GPU memory
-
-Densenet pretraining
---------------------
-
-- Training on NIH CXR14: 10 epochs in 12 hours, ~7.2 GB of CPU memory, ~6.4 GB
- of GPU memory
-
-Densenet fine-tuning
---------------------
-
-- Training on MC: 300 epochs in 0.5 hours, ~2 GB of CPU memory, ~6.4 GB of GPU
- memory
-- Training on MC-CH: 300 epochs in 2.5 hours, ~2 GB of CPU memory, ~6.4 GB of
- GPU memory
-- Training on MC-CH-IN: 300 epochs in 3.5 hours, ~2 GB of CPU memory, ~6.4 GB
- of GPU memory
-
-Logistic Regression
--------------------
-
-- Training on MC: 100 epochs in a few seconds, ~17 GB of CPU memory
-- Training on MC-CH: 100 epochs in a few seconds, ~17 GB of CPU memory
-- Training on MC-CH-IN: 100 epochs in a few seconds, ~17 GB of CPU memory
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/usage/aggregpred.rst.txt b/sphinx/_sources/usage/aggregpred.rst.txt
deleted file mode 100644
index bcec1eef503e8017e0e242bf9695b4296e9c402c..0000000000000000000000000000000000000000
--- a/sphinx/_sources/usage/aggregpred.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-.. Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.usage.aggregpred:
-
-=======================================================
- Aggregate multiple prediction files into a single one
-=======================================================
-
-This guide explains how to aggregate multiple prediction files into a single
-one. It can be used when doing cross-validation to aggregate the predictions of
-k different models before evaluating the aggregated predictions. We input
-multiple prediction files (CSV files) and output a single one.
-
-Use the sub-command :ref:`aggregpred <ptbench.cli>` aggregate your prediction
-files together:
-
-.. code:: sh
-
- ptbench aggregpred -vv path/to/fold0/predictions.csv path/to/fold1/predictions.csv --output-folder=aggregpred
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/usage/evaluation.rst.txt b/sphinx/_sources/usage/evaluation.rst.txt
deleted file mode 100644
index d8b15040228d0781707073f9e538dd0949608246..0000000000000000000000000000000000000000
--- a/sphinx/_sources/usage/evaluation.rst.txt
+++ /dev/null
@@ -1,77 +0,0 @@
-.. Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.usage.evaluation:
-
-==========================
- Inference and Evaluation
-==========================
-
-This guides explains how to run inference or a complete evaluation using
-command-line tools. Inference produces probability of TB presence for input
-images, while evaluation will analyze such output against existing annotations
-and produce performance figures.
-
-
-Inference
----------
-
-In inference (or prediction) mode, we input data, the trained model, and output
-a CSV file containing the prediction outputs for every input image.
-
-To run inference, use the sub-command :ref:`predict <ptbench.cli>` to run
-prediction on an existing dataset:
-
-.. code:: sh
-
- ptbench predict -vv <model> -w <path/to/model.pth> <dataset>
-
-
-Replace ``<model>`` and ``<dataset>`` by the appropriate :ref:`configuration
-files <ptbench.config>`. Replace ``<path/to/model.pth>`` to a path leading to
-the pre-trained model.
-
-.. tip::
-
- An option to generate grad-CAMs is available for the :py:mod:`DensenetRS
- <ptbench.configs.models_datasets.densenet_rs>` model. To activate it, use
- the ``--grad-cams`` argument.
-
-.. tip::
-
- An option to generate a relevance analysis plot is available. To activate
- it, use the ``--relevance-analysis`` argument.
-
-
-Evaluation
-----------
-
-In evaluation, we input a dataset and predictions to generate performance
-summaries that help analysis of a trained model. Evaluation is done using the
-:ref:`evaluate command <ptbench.cli>` followed by the model and the annotated
-dataset configuration, and the path to the pretrained weights via the
-``--weight`` argument.
-
-Use ``ptbench evaluate --help`` for more information.
-
-E.g. run evaluation on predictions from the Montgomery set, do the following:
-
-.. code:: sh
-
- ptbench evaluate -vv montgomery -p /predictions/folder -o /eval/results/folder
-
-
-Comparing Systems
------------------
-
-To compare multiple systems together and generate combined plots and tables,
-use the :ref:`compare command <ptbench.cli>`. Use ``--help`` for a quick
-guide.
-
-.. code:: sh
-
- ptbench compare -vv A A/metrics.csv B B/metrics.csv --output-figure=plot.pdf --output-table=table.txt --threshold=0.5
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/usage/index.rst.txt b/sphinx/_sources/usage/index.rst.txt
deleted file mode 100644
index 34316bab8bea2d08637b90a7dd6895b2d94da10a..0000000000000000000000000000000000000000
--- a/sphinx/_sources/usage/index.rst.txt
+++ /dev/null
@@ -1,91 +0,0 @@
-.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.usage:
-
-=======
- Usage
-=======
-
-This package supports a fully reproducible research experimentation cycle for
-tuberculosis detection with support for the following activities.
-
-.. figure:: img/direct_vs_indirect.png
-
-
-.. _ptbench.usage.direct-detection:
-
-Direct detection
-----------------
-
-* Training: Images are fed to a Convolutional Neural Network (CNN),
- that is trained to detect the presence of tuberculosis
- automatically, via error back propagation. The objective of this phase is to
- produce a CNN model.
-* Inference (prediction): The CNN is used to generate TB predictions.
-* Evaluation: Predications are used to evaluate CNN performance against
- provided annotations, and to generate measure files and score tables. Optimal
- thresholds are also calculated.
-* Comparison: Use predictions results to compare performance of multiple
- systems.
-
-
-.. _ptbench.usage.indirect-detection:
-
-Indirect detection
-------------------
-
-* Training (step 1): Images are fed to a Convolutional Neural Network (CNN),
- that is trained to detect the presence of radiological signs
- automatically, via error back propagation. The objective of this phase is to
- produce a CNN model.
-* Inference (prediction): The CNN is used to generate radiological signs
- predictions.
-* Conversion of the radiological signs predictions into a new dataset.
-* Training (step 2): Radiological signs are fed to a shallow network, that is
- trained to detect the presence of tuberculosis automatically, via error back
- propagation. The objective of this phase is to produce a shallow model.
-* Inference (prediction): The shallow model is used to generate TB predictions.
-* Evaluation: Predications are used to evaluate CNN performance against
- provided annotations, and to generate measure files and score tables.
-* Comparison: Use predictions results to compare performance of multiple
- systems.
-
-We provide :ref:`command-line interfaces (CLI) <ptbench.cli>` that implement
-each of the phases above. This interface is configurable using :ref:`clapper's
-extensible configuration framework <clapper.config>`. In essence, each
-command-line option may be provided as a variable with the same name in a
-Python file. Each file may combine any number of variables that are pertinent
-to an application.
-
-.. tip::
-
- For reproducibility, we recommend you stick to configuration files when
- parameterizing our CLI. Notice some of the options in the CLI interface
- (e.g. ``--dataset``) cannot be passed via the actual command-line as it
- may require complex Python types that cannot be synthetized in a single
- input parameter.
-
-We provide a number of :ref:`preset configuration files <ptbench.config>` that
-can be used in one or more of the activities described in this section. Our
-command-line framework allows you to refer to these preset configuration files
-using special names (a.k.a. "resources"), that procure and load these for you
-automatically.
-
-
-.. _ptbench.usage.commands:
-
-Commands
---------
-
-.. toctree::
- :maxdepth: 2
-
- training
- evaluation
- predtojson
- aggregpred
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/usage/predtojson.rst.txt b/sphinx/_sources/usage/predtojson.rst.txt
deleted file mode 100644
index 81e31d2fa8422f52e739146ccf8aa951469f6806..0000000000000000000000000000000000000000
--- a/sphinx/_sources/usage/predtojson.rst.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-.. Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.usage.predtojson:
-
-========================================
- Converting predictions to JSON dataset
-========================================
-
-This guide explains how to convert radiological signs predictions from a model
-into a JSON dataset. It can be used to create new versions of TB datasets with
-the predicted radiological signs to be able to use a shallow model. We input
-predictions (CSV files) and output a ``dataset.json`` file.
-
-Use the sub-command :ref:`predtojson <ptbench.cli>` to create your JSON dataset
-file:
-
-.. code:: sh
-
- ptbench predtojson -vv train train/predictions.csv test test/predictions.csv --output-folder=pred_to_json
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_sources/usage/training.rst.txt b/sphinx/_sources/usage/training.rst.txt
deleted file mode 100644
index 67b3918cee7640bdee8f0277ec5390142163c21e..0000000000000000000000000000000000000000
--- a/sphinx/_sources/usage/training.rst.txt
+++ /dev/null
@@ -1,72 +0,0 @@
-.. Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-..
-.. SPDX-License-Identifier: GPL-3.0-or-later
-
-.. _ptbench.usage.training:
-
-==========
- Training
-==========
-
-Convolutional Neural Network (CNN)
-----------------------------------
-
-To train a new CNN, use the command-line interface (CLI) application ``ptbench
-train``, available on your prompt. To use this CLI, you must define the input
-dataset that will be used to train the CNN, as well as the type of model that
-will be trained. You may issue ``ptbench train --help`` for a help message
-containing more detailed instructions.
-
-.. tip::
-
- We strongly advice training with a GPU (using ``--device="cuda:0"``).
- Depending on the available GPU memory you might have to adjust your batch
- size (``--batch``).
-
-
-Examples
-========
-
-To train Pasa CNN on the Montgomery dataset:
-
-.. code:: sh
-
- ptbench train -vv pasa montgomery --batch-size=4 --epochs=150
-
-To train DensenetRS CNN on the NIH CXR14 dataset:
-
-.. code:: sh
-
- ptbench train -vv nih_cxr14 densenet_rs --batch-size=8 --epochs=10
-
-
-Logistic regressor or shallow network
--------------------------------------
-
-To train a logistic regressor or a shallow network, use the command-line
-interface (CLI) application ``ptbench train``, available on your prompt. To use
-this CLI, you must define the input dataset that will be used to train the
-model, as well as the type of model that will be trained.
-You may issue ``ptbench train --help`` for a help message containing more
-detailed instructions.
-
-Examples
-========
-
-To train a logistic regressor using predictions from DensenetForRS on the
-Montgomery dataset:
-
-.. code:: sh
-
- ptbench train -vv logistic_regression montgomery_rs --batch-size=4 --epochs=20
-
-
-To train Signs_to_TB using predictions from DensenetForRS on the Shenzhen
-dataset:
-
-.. code:: sh
-
- ptbench train -vv signs_to_tb shenzhen_rs --batch-size=4 --epochs=20
-
-
-.. include:: ../links.rst
diff --git a/sphinx/_static/_sphinx_javascript_frameworks_compat.js b/sphinx/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469dc29fac0cbf16d10355e3313897cb3752..0000000000000000000000000000000000000000
--- a/sphinx/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
- if (!x) {
- return x
- }
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-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
- if (typeof s === 'undefined')
- s = document.location.search;
- var parts = s.substr(s.indexOf('?') + 1).split('&');
- var result = {};
- for (var i = 0; i < parts.length; i++) {
- var tmp = parts[i].split('=', 2);
- var key = jQuery.urldecode(tmp[0]);
- var value = jQuery.urldecode(tmp[1]);
- if (key in result)
- result[key].push(value);
- else
- result[key] = [value];
- }
- return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
- function highlight(node, addItems) {
- if (node.nodeType === 3) {
- var val = node.nodeValue;
- var pos = val.toLowerCase().indexOf(text);
- if (pos >= 0 &&
- !jQuery(node.parentNode).hasClass(className) &&
- !jQuery(node.parentNode).hasClass("nohighlight")) {
- var span;
- var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.className = className;
- }
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
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- highlight(this, addItems);
- });
- }
- }
- var addItems = [];
- var result = this.each(function() {
- highlight(this, addItems);
- });
- for (var i = 0; i < addItems.length; ++i) {
- jQuery(addItems[i].parent).before(addItems[i].target);
- }
- return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
- /(webkit)[ \/]([\w.]+)/.exec(ua) ||
- /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
- /(msie) ([\w.]+)/.exec(ua) ||
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- return {
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- };
- };
- jQuery.browser = {};
- jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
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diff --git a/sphinx/_static/basic.css b/sphinx/_static/basic.css
deleted file mode 100644
index 4e9a9f1faca4a263f4cb13711058d9456d38dc60..0000000000000000000000000000000000000000
--- a/sphinx/_static/basic.css
+++ /dev/null
@@ -1,900 +0,0 @@
-/*
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- * ~~~~~~~~~
- *
- * Sphinx stylesheet -- basic theme.
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deleted file mode 100644
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deleted file mode 100644
index 40ac331086b8957a58ce1dbc752ad065b1d9dcc0..0000000000000000000000000000000000000000
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- },
- 'es' : {
- 'copy': 'Copiar',
- 'copy_to_clipboard': 'Copiar al portapapeles',
- 'copy_success': '¡Copiado!',
- 'copy_failure': 'Error al copiar',
- },
- 'de' : {
- 'copy': 'Kopieren',
- 'copy_to_clipboard': 'In die Zwischenablage kopieren',
- 'copy_success': 'Kopiert!',
- 'copy_failure': 'Fehler beim Kopieren',
- },
- 'fr' : {
- 'copy': 'Copier',
- 'copy_to_clipboard': 'Copié dans le presse-papier',
- 'copy_success': 'Copié !',
- 'copy_failure': 'Échec de la copie',
- },
- 'ru': {
- 'copy': 'Скопировать',
- 'copy_to_clipboard': 'Скопировать в буфер',
- 'copy_success': 'Скопировано!',
- 'copy_failure': 'Ðе удалоÑÑŒ Ñкопировать',
- },
- 'zh-CN': {
- 'copy': 'å¤åˆ¶',
- 'copy_to_clipboard': 'å¤åˆ¶åˆ°å‰ªè´´æ¿',
- 'copy_success': 'å¤åˆ¶æˆåŠŸ!',
- 'copy_failure': 'å¤åˆ¶å¤±è´¥',
- },
- 'it' : {
- 'copy': 'Copiare',
- 'copy_to_clipboard': 'Copiato negli appunti',
- 'copy_success': 'Copiato!',
- 'copy_failure': 'Errore durante la copia',
- }
-}
-
-let locale = 'en'
-if( document.documentElement.lang !== undefined
- && messages[document.documentElement.lang] !== undefined ) {
- locale = document.documentElement.lang
-}
-
-let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
-if (doc_url_root == '#') {
- doc_url_root = '';
-}
-
-/**
- * SVG files for our copy buttons
- */
-let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
- <title>${messages[locale]['copy_success']}</title>
- <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
- <path d="M5 12l5 5l10 -10" />
-</svg>`
-
-// If the user specified their own SVG use that, otherwise use the default
-let iconCopy = ``;
-if (!iconCopy) {
- iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
- <title>${messages[locale]['copy_to_clipboard']}</title>
- <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
- <rect x="8" y="8" width="12" height="12" rx="2" />
- <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>`
-}
-
-/**
- * Set up copy/paste for code blocks
- */
-
-const runWhenDOMLoaded = cb => {
- if (document.readyState != 'loading') {
- cb()
- } else if (document.addEventListener) {
- document.addEventListener('DOMContentLoaded', cb)
- } else {
- document.attachEvent('onreadystatechange', function() {
- if (document.readyState == 'complete') cb()
- })
- }
-}
-
-const codeCellId = index => `codecell${index}`
-
-// Clears selected text since ClipboardJS will select the text when copying
-const clearSelection = () => {
- if (window.getSelection) {
- window.getSelection().removeAllRanges()
- } else if (document.selection) {
- document.selection.empty()
- }
-}
-
-// Changes tooltip text for two seconds, then changes it back
-const temporarilyChangeTooltip = (el, oldText, newText) => {
- el.setAttribute('data-tooltip', newText)
- el.classList.add('success')
- setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
- setTimeout(() => el.classList.remove('success'), 2000)
-}
-
-// Changes the copy button icon for two seconds, then changes it back
-const temporarilyChangeIcon = (el) => {
- el.innerHTML = iconCheck;
- setTimeout(() => {el.innerHTML = iconCopy}, 2000)
-}
-
-const addCopyButtonToCodeCells = () => {
- // If ClipboardJS hasn't loaded, wait a bit and try again. This
- // happens because we load ClipboardJS asynchronously.
- if (window.ClipboardJS === undefined) {
- setTimeout(addCopyButtonToCodeCells, 250)
- return
- }
-
- // Add copybuttons to all of our code cells
- const codeCells = document.querySelectorAll('div.highlight pre')
- codeCells.forEach((codeCell, index) => {
- const id = codeCellId(index)
- codeCell.setAttribute('id', id)
-
- const clipboardButton = id =>
- `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
- ${iconCopy}
- </button>`
- codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
- })
-
-function escapeRegExp(string) {
- return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
- var regexp;
- var match;
-
- // Do we check for line continuation characters and "HERE-documents"?
- var useLineCont = !!lineContinuationChar
- var useHereDoc = !!hereDocDelim
-
- // create regexp to capture prompt and remaining line
- if (isRegexp) {
- regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
- } else {
- regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
- }
-
- const outputLines = [];
- var promptFound = false;
- var gotLineCont = false;
- var gotHereDoc = false;
- const lineGotPrompt = [];
- for (const line of textContent.split('\n')) {
- match = line.match(regexp)
- if (match || gotLineCont || gotHereDoc) {
- promptFound = regexp.test(line)
- lineGotPrompt.push(promptFound)
- if (removePrompts && promptFound) {
- outputLines.push(match[2])
- } else {
- outputLines.push(line)
- }
- gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
- if (line.includes(hereDocDelim) & useHereDoc)
- gotHereDoc = !gotHereDoc
- } else if (!onlyCopyPromptLines) {
- outputLines.push(line)
- } else if (copyEmptyLines && line.trim() === '') {
- outputLines.push(line)
- }
- }
-
- // If no lines with the prompt were found then just use original lines
- if (lineGotPrompt.some(v => v === true)) {
- textContent = outputLines.join('\n');
- }
-
- // Remove a trailing newline to avoid auto-running when pasting
- if (textContent.endsWith("\n")) {
- textContent = textContent.slice(0, -1)
- }
- return textContent
-}
-
-
-var copyTargetText = (trigger) => {
- var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
- return formatCopyText(target.innerText, '', false, true, true, true, '', '')
-}
-
- // Initialize with a callback so we can modify the text before copy
- const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
-
- // Update UI with error/success messages
- clipboard.on('success', event => {
- clearSelection()
- temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
- temporarilyChangeIcon(event.trigger)
- })
-
- clipboard.on('error', event => {
- temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
- })
-}
-
-runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/sphinx/_static/copybutton_funcs.js b/sphinx/_static/copybutton_funcs.js
deleted file mode 100644
index b9168c55654b79a2207aa0ef08016ddec4cf4cdd..0000000000000000000000000000000000000000
--- a/sphinx/_static/copybutton_funcs.js
+++ /dev/null
@@ -1,58 +0,0 @@
-function escapeRegExp(string) {
- return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
- var regexp;
- var match;
-
- // Do we check for line continuation characters and "HERE-documents"?
- var useLineCont = !!lineContinuationChar
- var useHereDoc = !!hereDocDelim
-
- // create regexp to capture prompt and remaining line
- if (isRegexp) {
- regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
- } else {
- regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
- }
-
- const outputLines = [];
- var promptFound = false;
- var gotLineCont = false;
- var gotHereDoc = false;
- const lineGotPrompt = [];
- for (const line of textContent.split('\n')) {
- match = line.match(regexp)
- if (match || gotLineCont || gotHereDoc) {
- promptFound = regexp.test(line)
- lineGotPrompt.push(promptFound)
- if (removePrompts && promptFound) {
- outputLines.push(match[2])
- } else {
- outputLines.push(line)
- }
- gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
- if (line.includes(hereDocDelim) & useHereDoc)
- gotHereDoc = !gotHereDoc
- } else if (!onlyCopyPromptLines) {
- outputLines.push(line)
- } else if (copyEmptyLines && line.trim() === '') {
- outputLines.push(line)
- }
- }
-
- // If no lines with the prompt were found then just use original lines
- if (lineGotPrompt.some(v => v === true)) {
- textContent = outputLines.join('\n');
- }
-
- // Remove a trailing newline to avoid auto-running when pasting
- if (textContent.endsWith("\n")) {
- textContent = textContent.slice(0, -1)
- }
- return textContent
-}
diff --git a/sphinx/_static/debug.css b/sphinx/_static/debug.css
deleted file mode 100644
index 74d4aec33e5ccd45b77c871a005d96f9f8ae1dc4..0000000000000000000000000000000000000000
--- a/sphinx/_static/debug.css
+++ /dev/null
@@ -1,69 +0,0 @@
-/*
- This CSS file should be overridden by the theme authors. It's
- meant for debugging and developing the skeleton that this theme provides.
-*/
-body {
- font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif,
- "Apple Color Emoji", "Segoe UI Emoji";
- background: lavender;
-}
-.sb-announcement {
- background: rgb(131, 131, 131);
-}
-.sb-announcement__inner {
- background: black;
- color: white;
-}
-.sb-header {
- background: lightskyblue;
-}
-.sb-header__inner {
- background: royalblue;
- color: white;
-}
-.sb-header-secondary {
- background: lightcyan;
-}
-.sb-header-secondary__inner {
- background: cornflowerblue;
- color: white;
-}
-.sb-sidebar-primary {
- background: lightgreen;
-}
-.sb-main {
- background: blanchedalmond;
-}
-.sb-main__inner {
- background: antiquewhite;
-}
-.sb-header-article {
- background: lightsteelblue;
-}
-.sb-article-container {
- background: snow;
-}
-.sb-article-main {
- background: white;
-}
-.sb-footer-article {
- background: lightpink;
-}
-.sb-sidebar-secondary {
- background: lightgoldenrodyellow;
-}
-.sb-footer-content {
- background: plum;
-}
-.sb-footer-content__inner {
- background: palevioletred;
-}
-.sb-footer {
- background: pink;
-}
-.sb-footer__inner {
- background: salmon;
-}
-.sb-article {
- background: white;
-}
diff --git a/sphinx/_static/doctools.js b/sphinx/_static/doctools.js
deleted file mode 100644
index 527b876ca636d9a23d0cb3cc73ff6355b94fd7da..0000000000000000000000000000000000000000
--- a/sphinx/_static/doctools.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
- "TEXTAREA",
- "INPUT",
- "SELECT",
- "BUTTON",
-]);
-
-const _ready = (callback) => {
- if (document.readyState !== "loading") {
- callback();
- } else {
- document.addEventListener("DOMContentLoaded", callback);
- }
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const Documentation = {
- init: () => {
- Documentation.initDomainIndexTable();
- Documentation.initOnKeyListeners();
- },
-
- /**
- * i18n support
- */
- TRANSLATIONS: {},
- PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
- LOCALE: "unknown",
-
- // gettext and ngettext don't access this so that the functions
- // can safely bound to a different name (_ = Documentation.gettext)
- gettext: (string) => {
- const translated = Documentation.TRANSLATIONS[string];
- switch (typeof translated) {
- case "undefined":
- return string; // no translation
- case "string":
- return translated; // translation exists
- default:
- return translated[0]; // (singular, plural) translation tuple exists
- }
- },
-
- ngettext: (singular, plural, n) => {
- const translated = Documentation.TRANSLATIONS[singular];
- if (typeof translated !== "undefined")
- return translated[Documentation.PLURAL_EXPR(n)];
- return n === 1 ? singular : plural;
- },
-
- addTranslations: (catalog) => {
- Object.assign(Documentation.TRANSLATIONS, catalog.messages);
- Documentation.PLURAL_EXPR = new Function(
- "n",
- `return (${catalog.plural_expr})`
- );
- Documentation.LOCALE = catalog.locale;
- },
-
- /**
- * helper function to focus on search bar
- */
- focusSearchBar: () => {
- document.querySelectorAll("input[name=q]")[0]?.focus();
- },
-
- /**
- * Initialise the domain index toggle buttons
- */
- initDomainIndexTable: () => {
- const toggler = (el) => {
- const idNumber = el.id.substr(7);
- const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
- if (el.src.substr(-9) === "minus.png") {
- el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
- toggledRows.forEach((el) => (el.style.display = "none"));
- } else {
- el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
- toggledRows.forEach((el) => (el.style.display = ""));
- }
- };
-
- const togglerElements = document.querySelectorAll("img.toggler");
- togglerElements.forEach((el) =>
- el.addEventListener("click", (event) => toggler(event.currentTarget))
- );
- togglerElements.forEach((el) => (el.style.display = ""));
- if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
- },
-
- initOnKeyListeners: () => {
- // only install a listener if it is really needed
- if (
- !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
- !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
- )
- return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.altKey || event.ctrlKey || event.metaKey) return;
-
- if (!event.shiftKey) {
- switch (event.key) {
- case "ArrowLeft":
- if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
- const prevLink = document.querySelector('link[rel="prev"]');
- if (prevLink && prevLink.href) {
- window.location.href = prevLink.href;
- event.preventDefault();
- }
- break;
- case "ArrowRight":
- if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
- const nextLink = document.querySelector('link[rel="next"]');
- if (nextLink && nextLink.href) {
- window.location.href = nextLink.href;
- event.preventDefault();
- }
- break;
- }
- }
-
- // some keyboard layouts may need Shift to get /
- switch (event.key) {
- case "/":
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
- Documentation.focusSearchBar();
- event.preventDefault();
- }
- });
- },
-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/sphinx/_static/documentation_options.js b/sphinx/_static/documentation_options.js
deleted file mode 100644
index 0a62387fce2d4ee1dde6985604040f8b07ff9916..0000000000000000000000000000000000000000
--- a/sphinx/_static/documentation_options.js
+++ /dev/null
@@ -1,14 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
- URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '1.0.0b0',
- LANGUAGE: 'en',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false,
- SHOW_SEARCH_SUMMARY: true,
- ENABLE_SEARCH_SHORTCUTS: true,
-};
\ No newline at end of file
diff --git a/sphinx/_static/file.png b/sphinx/_static/file.png
deleted file mode 100644
index a858a410e4faa62ce324d814e4b816fff83a6fb3..0000000000000000000000000000000000000000
Binary files a/sphinx/_static/file.png and /dev/null differ
diff --git a/sphinx/_static/jquery-3.6.0.js b/sphinx/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b73e792ef288e785c22393a5df9dded4b..0000000000000000000000000000000000000000
--- a/sphinx/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
- "use strict";
-
- if ( typeof module === "object" && typeof module.exports === "object" ) {
-
- // For CommonJS and CommonJS-like environments where a proper `window`
- // is present, execute the factory and get jQuery.
- // For environments that do not have a `window` with a `document`
- // (such as Node.js), expose a factory as module.exports.
- // This accentuates the need for the creation of a real `window`.
- // e.g. var jQuery = require("jquery")(window);
- // See ticket #14549 for more info.
- module.exports = global.document ?
- factory( global, true ) :
- function( w ) {
- if ( !w.document ) {
- throw new Error( "jQuery requires a window with a document" );
- }
- return factory( w );
- };
- } else {
- factory( global );
- }
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
- return arr.flat.call( array );
-} : function( array ) {
- return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
- // Support: Chrome <=57, Firefox <=52
- // In some browsers, typeof returns "function" for HTML <object> elements
- // (i.e., `typeof document.createElement( "object" ) === "function"`).
- // We don't want to classify *any* DOM node as a function.
- // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
- // Plus for old WebKit, typeof returns "function" for HTML collections
- // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
- return typeof obj === "function" && typeof obj.nodeType !== "number" &&
- typeof obj.item !== "function";
- };
-
-
-var isWindow = function isWindow( obj ) {
- return obj != null && obj === obj.window;
- };
-
-
-var document = window.document;
-
-
-
- var preservedScriptAttributes = {
- type: true,
- src: true,
- nonce: true,
- noModule: true
- };
-
- function DOMEval( code, node, doc ) {
- doc = doc || document;
-
- var i, val,
- script = doc.createElement( "script" );
-
- script.text = code;
- if ( node ) {
- for ( i in preservedScriptAttributes ) {
-
- // Support: Firefox 64+, Edge 18+
- // Some browsers don't support the "nonce" property on scripts.
- // On the other hand, just using `getAttribute` is not enough as
- // the `nonce` attribute is reset to an empty string whenever it
- // becomes browsing-context connected.
- // See https://github.com/whatwg/html/issues/2369
- // See https://html.spec.whatwg.org/#nonce-attributes
- // The `node.getAttribute` check was added for the sake of
- // `jQuery.globalEval` so that it can fake a nonce-containing node
- // via an object.
- val = node[ i ] || node.getAttribute && node.getAttribute( i );
- if ( val ) {
- script.setAttribute( i, val );
- }
- }
- }
- doc.head.appendChild( script ).parentNode.removeChild( script );
- }
-
-
-function toType( obj ) {
- if ( obj == null ) {
- return obj + "";
- }
-
- // Support: Android <=2.3 only (functionish RegExp)
- return typeof obj === "object" || typeof obj === "function" ?
- class2type[ toString.call( obj ) ] || "object" :
- typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
- version = "3.6.0",
-
- // Define a local copy of jQuery
- jQuery = function( selector, context ) {
-
- // The jQuery object is actually just the init constructor 'enhanced'
- // Need init if jQuery is called (just allow error to be thrown if not included)
- return new jQuery.fn.init( selector, context );
- };
-
-jQuery.fn = jQuery.prototype = {
-
- // The current version of jQuery being used
- jquery: version,
-
- constructor: jQuery,
-
- // The default length of a jQuery object is 0
- length: 0,
-
- toArray: function() {
- return slice.call( this );
- },
-
- // Get the Nth element in the matched element set OR
- // Get the whole matched element set as a clean array
- get: function( num ) {
-
- // Return all the elements in a clean array
- if ( num == null ) {
- return slice.call( this );
- }
-
- // Return just the one element from the set
- return num < 0 ? this[ num + this.length ] : this[ num ];
- },
-
- // Take an array of elements and push it onto the stack
- // (returning the new matched element set)
- pushStack: function( elems ) {
-
- // Build a new jQuery matched element set
- var ret = jQuery.merge( this.constructor(), elems );
-
- // Add the old object onto the stack (as a reference)
- ret.prevObject = this;
-
- // Return the newly-formed element set
- return ret;
- },
-
- // Execute a callback for every element in the matched set.
- each: function( callback ) {
- return jQuery.each( this, callback );
- },
-
- map: function( callback ) {
- return this.pushStack( jQuery.map( this, function( elem, i ) {
- return callback.call( elem, i, elem );
- } ) );
- },
-
- slice: function() {
- return this.pushStack( slice.apply( this, arguments ) );
- },
-
- first: function() {
- return this.eq( 0 );
- },
-
- last: function() {
- return this.eq( -1 );
- },
-
- even: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return ( i + 1 ) % 2;
- } ) );
- },
-
- odd: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return i % 2;
- } ) );
- },
-
- eq: function( i ) {
- var len = this.length,
- j = +i + ( i < 0 ? len : 0 );
- return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
- },
-
- end: function() {
- return this.prevObject || this.constructor();
- },
-
- // For internal use only.
- // Behaves like an Array's method, not like a jQuery method.
- push: push,
- sort: arr.sort,
- splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
- var options, name, src, copy, copyIsArray, clone,
- target = arguments[ 0 ] || {},
- i = 1,
- length = arguments.length,
- deep = false;
-
- // Handle a deep copy situation
- if ( typeof target === "boolean" ) {
- deep = target;
-
- // Skip the boolean and the target
- target = arguments[ i ] || {};
- i++;
- }
-
- // Handle case when target is a string or something (possible in deep copy)
- if ( typeof target !== "object" && !isFunction( target ) ) {
- target = {};
- }
-
- // Extend jQuery itself if only one argument is passed
- if ( i === length ) {
- target = this;
- i--;
- }
-
- for ( ; i < length; i++ ) {
-
- // Only deal with non-null/undefined values
- if ( ( options = arguments[ i ] ) != null ) {
-
- // Extend the base object
- for ( name in options ) {
- copy = options[ name ];
-
- // Prevent Object.prototype pollution
- // Prevent never-ending loop
- if ( name === "__proto__" || target === copy ) {
- continue;
- }
-
- // Recurse if we're merging plain objects or arrays
- if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
- ( copyIsArray = Array.isArray( copy ) ) ) ) {
- src = target[ name ];
-
- // Ensure proper type for the source value
- if ( copyIsArray && !Array.isArray( src ) ) {
- clone = [];
- } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
- clone = {};
- } else {
- clone = src;
- }
- copyIsArray = false;
-
- // Never move original objects, clone them
- target[ name ] = jQuery.extend( deep, clone, copy );
-
- // Don't bring in undefined values
- } else if ( copy !== undefined ) {
- target[ name ] = copy;
- }
- }
- }
- }
-
- // Return the modified object
- return target;
-};
-
-jQuery.extend( {
-
- // Unique for each copy of jQuery on the page
- expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
- // Assume jQuery is ready without the ready module
- isReady: true,
-
- error: function( msg ) {
- throw new Error( msg );
- },
-
- noop: function() {},
-
- isPlainObject: function( obj ) {
- var proto, Ctor;
-
- // Detect obvious negatives
- // Use toString instead of jQuery.type to catch host objects
- if ( !obj || toString.call( obj ) !== "[object Object]" ) {
- return false;
- }
-
- proto = getProto( obj );
-
- // Objects with no prototype (e.g., `Object.create( null )`) are plain
- if ( !proto ) {
- return true;
- }
-
- // Objects with prototype are plain iff they were constructed by a global Object function
- Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
- return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
- },
-
- isEmptyObject: function( obj ) {
- var name;
-
- for ( name in obj ) {
- return false;
- }
- return true;
- },
-
- // Evaluates a script in a provided context; falls back to the global one
- // if not specified.
- globalEval: function( code, options, doc ) {
- DOMEval( code, { nonce: options && options.nonce }, doc );
- },
-
- each: function( obj, callback ) {
- var length, i = 0;
-
- if ( isArrayLike( obj ) ) {
- length = obj.length;
- for ( ; i < length; i++ ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- } else {
- for ( i in obj ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- }
-
- return obj;
- },
-
- // results is for internal usage only
- makeArray: function( arr, results ) {
- var ret = results || [];
-
- if ( arr != null ) {
- if ( isArrayLike( Object( arr ) ) ) {
- jQuery.merge( ret,
- typeof arr === "string" ?
- [ arr ] : arr
- );
- } else {
- push.call( ret, arr );
- }
- }
-
- return ret;
- },
-
- inArray: function( elem, arr, i ) {
- return arr == null ? -1 : indexOf.call( arr, elem, i );
- },
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- merge: function( first, second ) {
- var len = +second.length,
- j = 0,
- i = first.length;
-
- for ( ; j < len; j++ ) {
- first[ i++ ] = second[ j ];
- }
-
- first.length = i;
-
- return first;
- },
-
- grep: function( elems, callback, invert ) {
- var callbackInverse,
- matches = [],
- i = 0,
- length = elems.length,
- callbackExpect = !invert;
-
- // Go through the array, only saving the items
- // that pass the validator function
- for ( ; i < length; i++ ) {
- callbackInverse = !callback( elems[ i ], i );
- if ( callbackInverse !== callbackExpect ) {
- matches.push( elems[ i ] );
- }
- }
-
- return matches;
- },
-
- // arg is for internal usage only
- map: function( elems, callback, arg ) {
- var length, value,
- i = 0,
- ret = [];
-
- // Go through the array, translating each of the items to their new values
- if ( isArrayLike( elems ) ) {
- length = elems.length;
- for ( ; i < length; i++ ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
-
- // Go through every key on the object,
- } else {
- for ( i in elems ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
- }
-
- // Flatten any nested arrays
- return flat( ret );
- },
-
- // A global GUID counter for objects
- guid: 1,
-
- // jQuery.support is not used in Core but other projects attach their
- // properties to it so it needs to exist.
- support: support
-} );
-
-if ( typeof Symbol === "function" ) {
- jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
- function( _i, name ) {
- class2type[ "[object " + name + "]" ] = name.toLowerCase();
- } );
-
-function isArrayLike( obj ) {
-
- // Support: real iOS 8.2 only (not reproducible in simulator)
- // `in` check used to prevent JIT error (gh-2145)
- // hasOwn isn't used here due to false negatives
- // regarding Nodelist length in IE
- var length = !!obj && "length" in obj && obj.length,
- type = toType( obj );
-
- if ( isFunction( obj ) || isWindow( obj ) ) {
- return false;
- }
-
- return type === "array" || length === 0 ||
- typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
- support,
- Expr,
- getText,
- isXML,
- tokenize,
- compile,
- select,
- outermostContext,
- sortInput,
- hasDuplicate,
-
- // Local document vars
- setDocument,
- document,
- docElem,
- documentIsHTML,
- rbuggyQSA,
- rbuggyMatches,
- matches,
- contains,
-
- // Instance-specific data
- expando = "sizzle" + 1 * new Date(),
- preferredDoc = window.document,
- dirruns = 0,
- done = 0,
- classCache = createCache(),
- tokenCache = createCache(),
- compilerCache = createCache(),
- nonnativeSelectorCache = createCache(),
- sortOrder = function( a, b ) {
- if ( a === b ) {
- hasDuplicate = true;
- }
- return 0;
- },
-
- // Instance methods
- hasOwn = ( {} ).hasOwnProperty,
- arr = [],
- pop = arr.pop,
- pushNative = arr.push,
- push = arr.push,
- slice = arr.slice,
-
- // Use a stripped-down indexOf as it's faster than native
- // https://jsperf.com/thor-indexof-vs-for/5
- indexOf = function( list, elem ) {
- var i = 0,
- len = list.length;
- for ( ; i < len; i++ ) {
- if ( list[ i ] === elem ) {
- return i;
- }
- }
- return -1;
- },
-
- booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
- "ismap|loop|multiple|open|readonly|required|scoped",
-
- // Regular expressions
-
- // http://www.w3.org/TR/css3-selectors/#whitespace
- whitespace = "[\\x20\\t\\r\\n\\f]",
-
- // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
- identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
- "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
- // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
- attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
- // Operator (capture 2)
- "*([*^$|!~]?=)" + whitespace +
-
- // "Attribute values must be CSS identifiers [capture 5]
- // or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
- whitespace + "*\\]",
-
- pseudos = ":(" + identifier + ")(?:\\((" +
-
- // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
- // 1. quoted (capture 3; capture 4 or capture 5)
- "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
- // 2. simple (capture 6)
- "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
- // 3. anything else (capture 2)
- ".*" +
- ")\\)|)",
-
- // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
- rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
- whitespace + "+$", "g" ),
-
- rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
- "*" ),
- rdescend = new RegExp( whitespace + "|>" ),
-
- rpseudo = new RegExp( pseudos ),
- ridentifier = new RegExp( "^" + identifier + "$" ),
-
- matchExpr = {
- "ID": new RegExp( "^#(" + identifier + ")" ),
- "CLASS": new RegExp( "^\\.(" + identifier + ")" ),
- "TAG": new RegExp( "^(" + identifier + "|[*])" ),
- "ATTR": new RegExp( "^" + attributes ),
- "PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
- whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
- whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
- "bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
- // For use in libraries implementing .is()
- // We use this for POS matching in `select`
- "needsContext": new RegExp( "^" + whitespace +
- "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
- "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
- },
-
- rhtml = /HTML$/i,
- rinputs = /^(?:input|select|textarea|button)$/i,
- rheader = /^h\d$/i,
-
- rnative = /^[^{]+\{\s*\[native \w/,
-
- // Easily-parseable/retrievable ID or TAG or CLASS selectors
- rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
- rsibling = /[+~]/,
-
- // CSS escapes
- // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
- funescape = function( escape, nonHex ) {
- var high = "0x" + escape.slice( 1 ) - 0x10000;
-
- return nonHex ?
-
- // Strip the backslash prefix from a non-hex escape sequence
- nonHex :
-
- // Replace a hexadecimal escape sequence with the encoded Unicode code point
- // Support: IE <=11+
- // For values outside the Basic Multilingual Plane (BMP), manually construct a
- // surrogate pair
- high < 0 ?
- String.fromCharCode( high + 0x10000 ) :
- String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
- },
-
- // CSS string/identifier serialization
- // https://drafts.csswg.org/cssom/#common-serializing-idioms
- rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
- fcssescape = function( ch, asCodePoint ) {
- if ( asCodePoint ) {
-
- // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
- if ( ch === "\0" ) {
- return "\uFFFD";
- }
-
- // Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" +
- ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
- }
-
- // Other potentially-special ASCII characters get backslash-escaped
- return "\\" + ch;
- },
-
- // Used for iframes
- // See setDocument()
- // Removing the function wrapper causes a "Permission Denied"
- // error in IE
- unloadHandler = function() {
- setDocument();
- },
-
- inDisabledFieldset = addCombinator(
- function( elem ) {
- return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
- },
- { dir: "parentNode", next: "legend" }
- );
-
-// Optimize for push.apply( _, NodeList )
-try {
- push.apply(
- ( arr = slice.call( preferredDoc.childNodes ) ),
- preferredDoc.childNodes
- );
-
- // Support: Android<4.0
- // Detect silently failing push.apply
- // eslint-disable-next-line no-unused-expressions
- arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
- push = { apply: arr.length ?
-
- // Leverage slice if possible
- function( target, els ) {
- pushNative.apply( target, slice.call( els ) );
- } :
-
- // Support: IE<9
- // Otherwise append directly
- function( target, els ) {
- var j = target.length,
- i = 0;
-
- // Can't trust NodeList.length
- while ( ( target[ j++ ] = els[ i++ ] ) ) {}
- target.length = j - 1;
- }
- };
-}
-
-function Sizzle( selector, context, results, seed ) {
- var m, i, elem, nid, match, groups, newSelector,
- newContext = context && context.ownerDocument,
-
- // nodeType defaults to 9, since context defaults to document
- nodeType = context ? context.nodeType : 9;
-
- results = results || [];
-
- // Return early from calls with invalid selector or context
- if ( typeof selector !== "string" || !selector ||
- nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
- return results;
- }
-
- // Try to shortcut find operations (as opposed to filters) in HTML documents
- if ( !seed ) {
- setDocument( context );
- context = context || document;
-
- if ( documentIsHTML ) {
-
- // If the selector is sufficiently simple, try using a "get*By*" DOM method
- // (excepting DocumentFragment context, where the methods don't exist)
- if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
- // ID selector
- if ( ( m = match[ 1 ] ) ) {
-
- // Document context
- if ( nodeType === 9 ) {
- if ( ( elem = context.getElementById( m ) ) ) {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( elem.id === m ) {
- results.push( elem );
- return results;
- }
- } else {
- return results;
- }
-
- // Element context
- } else {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( newContext && ( elem = newContext.getElementById( m ) ) &&
- contains( context, elem ) &&
- elem.id === m ) {
-
- results.push( elem );
- return results;
- }
- }
-
- // Type selector
- } else if ( match[ 2 ] ) {
- push.apply( results, context.getElementsByTagName( selector ) );
- return results;
-
- // Class selector
- } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
- context.getElementsByClassName ) {
-
- push.apply( results, context.getElementsByClassName( m ) );
- return results;
- }
- }
-
- // Take advantage of querySelectorAll
- if ( support.qsa &&
- !nonnativeSelectorCache[ selector + " " ] &&
- ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
- // Support: IE 8 only
- // Exclude object elements
- ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
- newSelector = selector;
- newContext = context;
-
- // qSA considers elements outside a scoping root when evaluating child or
- // descendant combinators, which is not what we want.
- // In such cases, we work around the behavior by prefixing every selector in the
- // list with an ID selector referencing the scope context.
- // The technique has to be used as well when a leading combinator is used
- // as such selectors are not recognized by querySelectorAll.
- // Thanks to Andrew Dupont for this technique.
- if ( nodeType === 1 &&
- ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
-
- // We can use :scope instead of the ID hack if the browser
- // supports it & if we're not changing the context.
- if ( newContext !== context || !support.scope ) {
-
- // Capture the context ID, setting it first if necessary
- if ( ( nid = context.getAttribute( "id" ) ) ) {
- nid = nid.replace( rcssescape, fcssescape );
- } else {
- context.setAttribute( "id", ( nid = expando ) );
- }
- }
-
- // Prefix every selector in the list
- groups = tokenize( selector );
- i = groups.length;
- while ( i-- ) {
- groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
- toSelector( groups[ i ] );
- }
- newSelector = groups.join( "," );
- }
-
- try {
- push.apply( results,
- newContext.querySelectorAll( newSelector )
- );
- return results;
- } catch ( qsaError ) {
- nonnativeSelectorCache( selector, true );
- } finally {
- if ( nid === expando ) {
- context.removeAttribute( "id" );
- }
- }
- }
- }
- }
-
- // All others
- return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- * deleting the oldest entry
- */
-function createCache() {
- var keys = [];
-
- function cache( key, value ) {
-
- // Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
- if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
- // Only keep the most recent entries
- delete cache[ keys.shift() ];
- }
- return ( cache[ key + " " ] = value );
- }
- return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
- fn[ expando ] = true;
- return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
- var el = document.createElement( "fieldset" );
-
- try {
- return !!fn( el );
- } catch ( e ) {
- return false;
- } finally {
-
- // Remove from its parent by default
- if ( el.parentNode ) {
- el.parentNode.removeChild( el );
- }
-
- // release memory in IE
- el = null;
- }
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
- var arr = attrs.split( "|" ),
- i = arr.length;
-
- while ( i-- ) {
- Expr.attrHandle[ arr[ i ] ] = handler;
- }
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
- var cur = b && a,
- diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
- a.sourceIndex - b.sourceIndex;
-
- // Use IE sourceIndex if available on both nodes
- if ( diff ) {
- return diff;
- }
-
- // Check if b follows a
- if ( cur ) {
- while ( ( cur = cur.nextSibling ) ) {
- if ( cur === b ) {
- return -1;
- }
- }
- }
-
- return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return ( name === "input" || name === "button" ) && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
- // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
- return function( elem ) {
-
- // Only certain elements can match :enabled or :disabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
- if ( "form" in elem ) {
-
- // Check for inherited disabledness on relevant non-disabled elements:
- // * listed form-associated elements in a disabled fieldset
- // https://html.spec.whatwg.org/multipage/forms.html#category-listed
- // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
- // * option elements in a disabled optgroup
- // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
- // All such elements have a "form" property.
- if ( elem.parentNode && elem.disabled === false ) {
-
- // Option elements defer to a parent optgroup if present
- if ( "label" in elem ) {
- if ( "label" in elem.parentNode ) {
- return elem.parentNode.disabled === disabled;
- } else {
- return elem.disabled === disabled;
- }
- }
-
- // Support: IE 6 - 11
- // Use the isDisabled shortcut property to check for disabled fieldset ancestors
- return elem.isDisabled === disabled ||
-
- // Where there is no isDisabled, check manually
- /* jshint -W018 */
- elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
- }
-
- return elem.disabled === disabled;
-
- // Try to winnow out elements that can't be disabled before trusting the disabled property.
- // Some victims get caught in our net (label, legend, menu, track), but it shouldn't
- // even exist on them, let alone have a boolean value.
- } else if ( "label" in elem ) {
- return elem.disabled === disabled;
- }
-
- // Remaining elements are neither :enabled nor :disabled
- return false;
- };
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
- return markFunction( function( argument ) {
- argument = +argument;
- return markFunction( function( seed, matches ) {
- var j,
- matchIndexes = fn( [], seed.length, argument ),
- i = matchIndexes.length;
-
- // Match elements found at the specified indexes
- while ( i-- ) {
- if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
- seed[ j ] = !( matches[ j ] = seed[ j ] );
- }
- }
- } );
- } );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
- return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
- var namespace = elem && elem.namespaceURI,
- docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
- // Support: IE <=8
- // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
- // https://bugs.jquery.com/ticket/4833
- return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
- var hasCompare, subWindow,
- doc = node ? node.ownerDocument || node : preferredDoc;
-
- // Return early if doc is invalid or already selected
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
- return document;
- }
-
- // Update global variables
- document = doc;
- docElem = document.documentElement;
- documentIsHTML = !isXML( document );
-
- // Support: IE 9 - 11+, Edge 12 - 18+
- // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( preferredDoc != document &&
- ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
- // Support: IE 11, Edge
- if ( subWindow.addEventListener ) {
- subWindow.addEventListener( "unload", unloadHandler, false );
-
- // Support: IE 9 - 10 only
- } else if ( subWindow.attachEvent ) {
- subWindow.attachEvent( "onunload", unloadHandler );
- }
- }
-
- // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
- // Safari 4 - 5 only, Opera <=11.6 - 12.x only
- // IE/Edge & older browsers don't support the :scope pseudo-class.
- // Support: Safari 6.0 only
- // Safari 6.0 supports :scope but it's an alias of :root there.
- support.scope = assert( function( el ) {
- docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
- return typeof el.querySelectorAll !== "undefined" &&
- !el.querySelectorAll( ":scope fieldset div" ).length;
- } );
-
- /* Attributes
- ---------------------------------------------------------------------- */
-
- // Support: IE<8
- // Verify that getAttribute really returns attributes and not properties
- // (excepting IE8 booleans)
- support.attributes = assert( function( el ) {
- el.className = "i";
- return !el.getAttribute( "className" );
- } );
-
- /* getElement(s)By*
- ---------------------------------------------------------------------- */
-
- // Check if getElementsByTagName("*") returns only elements
- support.getElementsByTagName = assert( function( el ) {
- el.appendChild( document.createComment( "" ) );
- return !el.getElementsByTagName( "*" ).length;
- } );
-
- // Support: IE<9
- support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
- // Support: IE<10
- // Check if getElementById returns elements by name
- // The broken getElementById methods don't pick up programmatically-set names,
- // so use a roundabout getElementsByName test
- support.getById = assert( function( el ) {
- docElem.appendChild( el ).id = expando;
- return !document.getElementsByName || !document.getElementsByName( expando ).length;
- } );
-
- // ID filter and find
- if ( support.getById ) {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- return elem.getAttribute( "id" ) === attrId;
- };
- };
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var elem = context.getElementById( id );
- return elem ? [ elem ] : [];
- }
- };
- } else {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode( "id" );
- return node && node.value === attrId;
- };
- };
-
- // Support: IE 6 - 7 only
- // getElementById is not reliable as a find shortcut
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var node, i, elems,
- elem = context.getElementById( id );
-
- if ( elem ) {
-
- // Verify the id attribute
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
-
- // Fall back on getElementsByName
- elems = context.getElementsByName( id );
- i = 0;
- while ( ( elem = elems[ i++ ] ) ) {
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
- }
- }
-
- return [];
- }
- };
- }
-
- // Tag
- Expr.find[ "TAG" ] = support.getElementsByTagName ?
- function( tag, context ) {
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- return context.getElementsByTagName( tag );
-
- // DocumentFragment nodes don't have gEBTN
- } else if ( support.qsa ) {
- return context.querySelectorAll( tag );
- }
- } :
-
- function( tag, context ) {
- var elem,
- tmp = [],
- i = 0,
-
- // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
- results = context.getElementsByTagName( tag );
-
- // Filter out possible comments
- if ( tag === "*" ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem.nodeType === 1 ) {
- tmp.push( elem );
- }
- }
-
- return tmp;
- }
- return results;
- };
-
- // Class
- Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
- if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
- return context.getElementsByClassName( className );
- }
- };
-
- /* QSA/matchesSelector
- ---------------------------------------------------------------------- */
-
- // QSA and matchesSelector support
-
- // matchesSelector(:active) reports false when true (IE9/Opera 11.5)
- rbuggyMatches = [];
-
- // qSa(:focus) reports false when true (Chrome 21)
- // We allow this because of a bug in IE8/9 that throws an error
- // whenever `document.activeElement` is accessed on an iframe
- // So, we allow :focus to pass through QSA all the time to avoid the IE error
- // See https://bugs.jquery.com/ticket/13378
- rbuggyQSA = [];
-
- if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
- // Build QSA regex
- // Regex strategy adopted from Diego Perini
- assert( function( el ) {
-
- var input;
-
- // Select is set to empty string on purpose
- // This is to test IE's treatment of not explicitly
- // setting a boolean content attribute,
- // since its presence should be enough
- // https://bugs.jquery.com/ticket/12359
- docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
- "<select id='" + expando + "-\r\\' msallowcapture=''>" +
- "<option selected=''></option></select>";
-
- // Support: IE8, Opera 11-12.16
- // Nothing should be selected when empty strings follow ^= or $= or *=
- // The test attribute must be unknown in Opera but "safe" for WinRT
- // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
- rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
- }
-
- // Support: IE8
- // Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll( "[selected]" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
- }
-
- // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
- if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
- rbuggyQSA.push( "~=" );
- }
-
- // Support: IE 11+, Edge 15 - 18+
- // IE 11/Edge don't find elements on a `[name='']` query in some cases.
- // Adding a temporary attribute to the document before the selection works
- // around the issue.
- // Interestingly, IE 10 & older don't seem to have the issue.
- input = document.createElement( "input" );
- input.setAttribute( "name", "" );
- el.appendChild( input );
- if ( !el.querySelectorAll( "[name='']" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
- whitespace + "*(?:''|\"\")" );
- }
-
- // Webkit/Opera - :checked should return selected option elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- // IE8 throws error here and will not see later tests
- if ( !el.querySelectorAll( ":checked" ).length ) {
- rbuggyQSA.push( ":checked" );
- }
-
- // Support: Safari 8+, iOS 8+
- // https://bugs.webkit.org/show_bug.cgi?id=136851
- // In-page `selector#id sibling-combinator selector` fails
- if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
- rbuggyQSA.push( ".#.+[+~]" );
- }
-
- // Support: Firefox <=3.6 - 5 only
- // Old Firefox doesn't throw on a badly-escaped identifier.
- el.querySelectorAll( "\\\f" );
- rbuggyQSA.push( "[\\r\\n\\f]" );
- } );
-
- assert( function( el ) {
- el.innerHTML = "<a href='' disabled='disabled'></a>" +
- "<select disabled='disabled'><option/></select>";
-
- // Support: Windows 8 Native Apps
- // The type and name attributes are restricted during .innerHTML assignment
- var input = document.createElement( "input" );
- input.setAttribute( "type", "hidden" );
- el.appendChild( input ).setAttribute( "name", "D" );
-
- // Support: IE8
- // Enforce case-sensitivity of name attribute
- if ( el.querySelectorAll( "[name=d]" ).length ) {
- rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
- }
-
- // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
- // IE8 throws error here and will not see later tests
- if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: IE9-11+
- // IE's :disabled selector does not pick up the children of disabled fieldsets
- docElem.appendChild( el ).disabled = true;
- if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: Opera 10 - 11 only
- // Opera 10-11 does not throw on post-comma invalid pseudos
- el.querySelectorAll( "*,:x" );
- rbuggyQSA.push( ",.*:" );
- } );
- }
-
- if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
- docElem.webkitMatchesSelector ||
- docElem.mozMatchesSelector ||
- docElem.oMatchesSelector ||
- docElem.msMatchesSelector ) ) ) ) {
-
- assert( function( el ) {
-
- // Check to see if it's possible to do matchesSelector
- // on a disconnected node (IE 9)
- support.disconnectedMatch = matches.call( el, "*" );
-
- // This should fail with an exception
- // Gecko does not error, returns false instead
- matches.call( el, "[s!='']:x" );
- rbuggyMatches.push( "!=", pseudos );
- } );
- }
-
- rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
- rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
- /* Contains
- ---------------------------------------------------------------------- */
- hasCompare = rnative.test( docElem.compareDocumentPosition );
-
- // Element contains another
- // Purposefully self-exclusive
- // As in, an element does not contain itself
- contains = hasCompare || rnative.test( docElem.contains ) ?
- function( a, b ) {
- var adown = a.nodeType === 9 ? a.documentElement : a,
- bup = b && b.parentNode;
- return a === bup || !!( bup && bup.nodeType === 1 && (
- adown.contains ?
- adown.contains( bup ) :
- a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ) );
- } :
- function( a, b ) {
- if ( b ) {
- while ( ( b = b.parentNode ) ) {
- if ( b === a ) {
- return true;
- }
- }
- }
- return false;
- };
-
- /* Sorting
- ---------------------------------------------------------------------- */
-
- // Document order sorting
- sortOrder = hasCompare ?
- function( a, b ) {
-
- // Flag for duplicate removal
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- // Sort on method existence if only one input has compareDocumentPosition
- var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
- if ( compare ) {
- return compare;
- }
-
- // Calculate position if both inputs belong to the same document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
- a.compareDocumentPosition( b ) :
-
- // Otherwise we know they are disconnected
- 1;
-
- // Disconnected nodes
- if ( compare & 1 ||
- ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
- // Choose the first element that is related to our preferred document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( a == document || a.ownerDocument == preferredDoc &&
- contains( preferredDoc, a ) ) {
- return -1;
- }
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( b == document || b.ownerDocument == preferredDoc &&
- contains( preferredDoc, b ) ) {
- return 1;
- }
-
- // Maintain original order
- return sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
- }
-
- return compare & 4 ? -1 : 1;
- } :
- function( a, b ) {
-
- // Exit early if the nodes are identical
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- var cur,
- i = 0,
- aup = a.parentNode,
- bup = b.parentNode,
- ap = [ a ],
- bp = [ b ];
-
- // Parentless nodes are either documents or disconnected
- if ( !aup || !bup ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- return a == document ? -1 :
- b == document ? 1 :
- /* eslint-enable eqeqeq */
- aup ? -1 :
- bup ? 1 :
- sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
-
- // If the nodes are siblings, we can do a quick check
- } else if ( aup === bup ) {
- return siblingCheck( a, b );
- }
-
- // Otherwise we need full lists of their ancestors for comparison
- cur = a;
- while ( ( cur = cur.parentNode ) ) {
- ap.unshift( cur );
- }
- cur = b;
- while ( ( cur = cur.parentNode ) ) {
- bp.unshift( cur );
- }
-
- // Walk down the tree looking for a discrepancy
- while ( ap[ i ] === bp[ i ] ) {
- i++;
- }
-
- return i ?
-
- // Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[ i ], bp[ i ] ) :
-
- // Otherwise nodes in our document sort first
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- ap[ i ] == preferredDoc ? -1 :
- bp[ i ] == preferredDoc ? 1 :
- /* eslint-enable eqeqeq */
- 0;
- };
-
- return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
- return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
- setDocument( elem );
-
- if ( support.matchesSelector && documentIsHTML &&
- !nonnativeSelectorCache[ expr + " " ] &&
- ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
- ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {
-
- try {
- var ret = matches.call( elem, expr );
-
- // IE 9's matchesSelector returns false on disconnected nodes
- if ( ret || support.disconnectedMatch ||
-
- // As well, disconnected nodes are said to be in a document
- // fragment in IE 9
- elem.document && elem.document.nodeType !== 11 ) {
- return ret;
- }
- } catch ( e ) {
- nonnativeSelectorCache( expr, true );
- }
- }
-
- return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( context.ownerDocument || context ) != document ) {
- setDocument( context );
- }
- return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( elem.ownerDocument || elem ) != document ) {
- setDocument( elem );
- }
-
- var fn = Expr.attrHandle[ name.toLowerCase() ],
-
- // Don't get fooled by Object.prototype properties (jQuery #13807)
- val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
- fn( elem, name, !documentIsHTML ) :
- undefined;
-
- return val !== undefined ?
- val :
- support.attributes || !documentIsHTML ?
- elem.getAttribute( name ) :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
-};
-
-Sizzle.escape = function( sel ) {
- return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
- throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
- var elem,
- duplicates = [],
- j = 0,
- i = 0;
-
- // Unless we *know* we can detect duplicates, assume their presence
- hasDuplicate = !support.detectDuplicates;
- sortInput = !support.sortStable && results.slice( 0 );
- results.sort( sortOrder );
-
- if ( hasDuplicate ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem === results[ i ] ) {
- j = duplicates.push( i );
- }
- }
- while ( j-- ) {
- results.splice( duplicates[ j ], 1 );
- }
- }
-
- // Clear input after sorting to release objects
- // See https://github.com/jquery/sizzle/pull/225
- sortInput = null;
-
- return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
- var node,
- ret = "",
- i = 0,
- nodeType = elem.nodeType;
-
- if ( !nodeType ) {
-
- // If no nodeType, this is expected to be an array
- while ( ( node = elem[ i++ ] ) ) {
-
- // Do not traverse comment nodes
- ret += getText( node );
- }
- } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
- // Use textContent for elements
- // innerText usage removed for consistency of new lines (jQuery #11153)
- if ( typeof elem.textContent === "string" ) {
- return elem.textContent;
- } else {
-
- // Traverse its children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- ret += getText( elem );
- }
- }
- } else if ( nodeType === 3 || nodeType === 4 ) {
- return elem.nodeValue;
- }
-
- // Do not include comment or processing instruction nodes
-
- return ret;
-};
-
-Expr = Sizzle.selectors = {
-
- // Can be adjusted by the user
- cacheLength: 50,
-
- createPseudo: markFunction,
-
- match: matchExpr,
-
- attrHandle: {},
-
- find: {},
-
- relative: {
- ">": { dir: "parentNode", first: true },
- " ": { dir: "parentNode" },
- "+": { dir: "previousSibling", first: true },
- "~": { dir: "previousSibling" }
- },
-
- preFilter: {
- "ATTR": function( match ) {
- match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
- // Move the given value to match[3] whether quoted or unquoted
- match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
- match[ 5 ] || "" ).replace( runescape, funescape );
-
- if ( match[ 2 ] === "~=" ) {
- match[ 3 ] = " " + match[ 3 ] + " ";
- }
-
- return match.slice( 0, 4 );
- },
-
- "CHILD": function( match ) {
-
- /* matches from matchExpr["CHILD"]
- 1 type (only|nth|...)
- 2 what (child|of-type)
- 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
- 4 xn-component of xn+y argument ([+-]?\d*n|)
- 5 sign of xn-component
- 6 x of xn-component
- 7 sign of y-component
- 8 y of y-component
- */
- match[ 1 ] = match[ 1 ].toLowerCase();
-
- if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
- // nth-* requires argument
- if ( !match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- // numeric x and y parameters for Expr.filter.CHILD
- // remember that false/true cast respectively to 0/1
- match[ 4 ] = +( match[ 4 ] ?
- match[ 5 ] + ( match[ 6 ] || 1 ) :
- 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
- match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
- // other types prohibit arguments
- } else if ( match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- return match;
- },
-
- "PSEUDO": function( match ) {
- var excess,
- unquoted = !match[ 6 ] && match[ 2 ];
-
- if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
- return null;
- }
-
- // Accept quoted arguments as-is
- if ( match[ 3 ] ) {
- match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
- // Strip excess characters from unquoted arguments
- } else if ( unquoted && rpseudo.test( unquoted ) &&
-
- // Get excess from tokenize (recursively)
- ( excess = tokenize( unquoted, true ) ) &&
-
- // advance to the next closing parenthesis
- ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
- // excess is a negative index
- match[ 0 ] = match[ 0 ].slice( 0, excess );
- match[ 2 ] = unquoted.slice( 0, excess );
- }
-
- // Return only captures needed by the pseudo filter method (type and argument)
- return match.slice( 0, 3 );
- }
- },
-
- filter: {
-
- "TAG": function( nodeNameSelector ) {
- var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
- return nodeNameSelector === "*" ?
- function() {
- return true;
- } :
- function( elem ) {
- return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
- };
- },
-
- "CLASS": function( className ) {
- var pattern = classCache[ className + " " ];
-
- return pattern ||
- ( pattern = new RegExp( "(^|" + whitespace +
- ")" + className + "(" + whitespace + "|$)" ) ) && classCache(
- className, function( elem ) {
- return pattern.test(
- typeof elem.className === "string" && elem.className ||
- typeof elem.getAttribute !== "undefined" &&
- elem.getAttribute( "class" ) ||
- ""
- );
- } );
- },
-
- "ATTR": function( name, operator, check ) {
- return function( elem ) {
- var result = Sizzle.attr( elem, name );
-
- if ( result == null ) {
- return operator === "!=";
- }
- if ( !operator ) {
- return true;
- }
-
- result += "";
-
- /* eslint-disable max-len */
-
- return operator === "=" ? result === check :
- operator === "!=" ? result !== check :
- operator === "^=" ? check && result.indexOf( check ) === 0 :
- operator === "*=" ? check && result.indexOf( check ) > -1 :
- operator === "$=" ? check && result.slice( -check.length ) === check :
- operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
- operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
- false;
- /* eslint-enable max-len */
-
- };
- },
-
- "CHILD": function( type, what, _argument, first, last ) {
- var simple = type.slice( 0, 3 ) !== "nth",
- forward = type.slice( -4 ) !== "last",
- ofType = what === "of-type";
-
- return first === 1 && last === 0 ?
-
- // Shortcut for :nth-*(n)
- function( elem ) {
- return !!elem.parentNode;
- } :
-
- function( elem, _context, xml ) {
- var cache, uniqueCache, outerCache, node, nodeIndex, start,
- dir = simple !== forward ? "nextSibling" : "previousSibling",
- parent = elem.parentNode,
- name = ofType && elem.nodeName.toLowerCase(),
- useCache = !xml && !ofType,
- diff = false;
-
- if ( parent ) {
-
- // :(first|last|only)-(child|of-type)
- if ( simple ) {
- while ( dir ) {
- node = elem;
- while ( ( node = node[ dir ] ) ) {
- if ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) {
-
- return false;
- }
- }
-
- // Reverse direction for :only-* (if we haven't yet done so)
- start = dir = type === "only" && !start && "nextSibling";
- }
- return true;
- }
-
- start = [ forward ? parent.firstChild : parent.lastChild ];
-
- // non-xml :nth-child(...) stores cache data on `parent`
- if ( forward && useCache ) {
-
- // Seek `elem` from a previously-cached index
-
- // ...in a gzip-friendly way
- node = parent;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex && cache[ 2 ];
- node = nodeIndex && parent.childNodes[ nodeIndex ];
-
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
- // Fallback to seeking `elem` from the start
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- // When found, cache indexes on `parent` and break
- if ( node.nodeType === 1 && ++diff && node === elem ) {
- uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
- break;
- }
- }
-
- } else {
-
- // Use previously-cached element index if available
- if ( useCache ) {
-
- // ...in a gzip-friendly way
- node = elem;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex;
- }
-
- // xml :nth-child(...)
- // or :nth-last-child(...) or :nth(-last)?-of-type(...)
- if ( diff === false ) {
-
- // Use the same loop as above to seek `elem` from the start
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- if ( ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) &&
- ++diff ) {
-
- // Cache the index of each encountered element
- if ( useCache ) {
- outerCache = node[ expando ] ||
- ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- uniqueCache[ type ] = [ dirruns, diff ];
- }
-
- if ( node === elem ) {
- break;
- }
- }
- }
- }
- }
-
- // Incorporate the offset, then check against cycle size
- diff -= last;
- return diff === first || ( diff % first === 0 && diff / first >= 0 );
- }
- };
- },
-
- "PSEUDO": function( pseudo, argument ) {
-
- // pseudo-class names are case-insensitive
- // http://www.w3.org/TR/selectors/#pseudo-classes
- // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
- // Remember that setFilters inherits from pseudos
- var args,
- fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
- Sizzle.error( "unsupported pseudo: " + pseudo );
-
- // The user may use createPseudo to indicate that
- // arguments are needed to create the filter function
- // just as Sizzle does
- if ( fn[ expando ] ) {
- return fn( argument );
- }
-
- // But maintain support for old signatures
- if ( fn.length > 1 ) {
- args = [ pseudo, pseudo, "", argument ];
- return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction( function( seed, matches ) {
- var idx,
- matched = fn( seed, argument ),
- i = matched.length;
- while ( i-- ) {
- idx = indexOf( seed, matched[ i ] );
- seed[ idx ] = !( matches[ idx ] = matched[ i ] );
- }
- } ) :
- function( elem ) {
- return fn( elem, 0, args );
- };
- }
-
- return fn;
- }
- },
-
- pseudos: {
-
- // Potentially complex pseudos
- "not": markFunction( function( selector ) {
-
- // Trim the selector passed to compile
- // to avoid treating leading and trailing
- // spaces as combinators
- var input = [],
- results = [],
- matcher = compile( selector.replace( rtrim, "$1" ) );
-
- return matcher[ expando ] ?
- markFunction( function( seed, matches, _context, xml ) {
- var elem,
- unmatched = matcher( seed, null, xml, [] ),
- i = seed.length;
-
- // Match elements unmatched by `matcher`
- while ( i-- ) {
- if ( ( elem = unmatched[ i ] ) ) {
- seed[ i ] = !( matches[ i ] = elem );
- }
- }
- } ) :
- function( elem, _context, xml ) {
- input[ 0 ] = elem;
- matcher( input, null, xml, results );
-
- // Don't keep the element (issue #299)
- input[ 0 ] = null;
- return !results.pop();
- };
- } ),
-
- "has": markFunction( function( selector ) {
- return function( elem ) {
- return Sizzle( selector, elem ).length > 0;
- };
- } ),
-
- "contains": markFunction( function( text ) {
- text = text.replace( runescape, funescape );
- return function( elem ) {
- return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
- };
- } ),
-
- // "Whether an element is represented by a :lang() selector
- // is based solely on the element's language value
- // being equal to the identifier C,
- // or beginning with the identifier C immediately followed by "-".
- // The matching of C against the element's language value is performed case-insensitively.
- // The identifier C does not have to be a valid language name."
- // http://www.w3.org/TR/selectors/#lang-pseudo
- "lang": markFunction( function( lang ) {
-
- // lang value must be a valid identifier
- if ( !ridentifier.test( lang || "" ) ) {
- Sizzle.error( "unsupported lang: " + lang );
- }
- lang = lang.replace( runescape, funescape ).toLowerCase();
- return function( elem ) {
- var elemLang;
- do {
- if ( ( elemLang = documentIsHTML ?
- elem.lang :
- elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
- elemLang = elemLang.toLowerCase();
- return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
- }
- } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
- return false;
- };
- } ),
-
- // Miscellaneous
- "target": function( elem ) {
- var hash = window.location && window.location.hash;
- return hash && hash.slice( 1 ) === elem.id;
- },
-
- "root": function( elem ) {
- return elem === docElem;
- },
-
- "focus": function( elem ) {
- return elem === document.activeElement &&
- ( !document.hasFocus || document.hasFocus() ) &&
- !!( elem.type || elem.href || ~elem.tabIndex );
- },
-
- // Boolean properties
- "enabled": createDisabledPseudo( false ),
- "disabled": createDisabledPseudo( true ),
-
- "checked": function( elem ) {
-
- // In CSS3, :checked should return both checked and selected elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- var nodeName = elem.nodeName.toLowerCase();
- return ( nodeName === "input" && !!elem.checked ) ||
- ( nodeName === "option" && !!elem.selected );
- },
-
- "selected": function( elem ) {
-
- // Accessing this property makes selected-by-default
- // options in Safari work properly
- if ( elem.parentNode ) {
- // eslint-disable-next-line no-unused-expressions
- elem.parentNode.selectedIndex;
- }
-
- return elem.selected === true;
- },
-
- // Contents
- "empty": function( elem ) {
-
- // http://www.w3.org/TR/selectors/#empty-pseudo
- // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
- // but not by others (comment: 8; processing instruction: 7; etc.)
- // nodeType < 6 works because attributes (2) do not appear as children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- if ( elem.nodeType < 6 ) {
- return false;
- }
- }
- return true;
- },
-
- "parent": function( elem ) {
- return !Expr.pseudos[ "empty" ]( elem );
- },
-
- // Element/input types
- "header": function( elem ) {
- return rheader.test( elem.nodeName );
- },
-
- "input": function( elem ) {
- return rinputs.test( elem.nodeName );
- },
-
- "button": function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === "button" || name === "button";
- },
-
- "text": function( elem ) {
- var attr;
- return elem.nodeName.toLowerCase() === "input" &&
- elem.type === "text" &&
-
- // Support: IE<8
- // New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( ( attr = elem.getAttribute( "type" ) ) == null ||
- attr.toLowerCase() === "text" );
- },
-
- // Position-in-collection
- "first": createPositionalPseudo( function() {
- return [ 0 ];
- } ),
-
- "last": createPositionalPseudo( function( _matchIndexes, length ) {
- return [ length - 1 ];
- } ),
-
- "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
- return [ argument < 0 ? argument + length : argument ];
- } ),
-
- "even": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 0;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "odd": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 1;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ?
- argument + length :
- argument > length ?
- length :
- argument;
- for ( ; --i >= 0; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ? argument + length : argument;
- for ( ; ++i < length; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } )
- }
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
- Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
- Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
- var matched, match, tokens, type,
- soFar, groups, preFilters,
- cached = tokenCache[ selector + " " ];
-
- if ( cached ) {
- return parseOnly ? 0 : cached.slice( 0 );
- }
-
- soFar = selector;
- groups = [];
- preFilters = Expr.preFilter;
-
- while ( soFar ) {
-
- // Comma and first run
- if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
- if ( match ) {
-
- // Don't consume trailing commas as valid
- soFar = soFar.slice( match[ 0 ].length ) || soFar;
- }
- groups.push( ( tokens = [] ) );
- }
-
- matched = false;
-
- // Combinators
- if ( ( match = rcombinators.exec( soFar ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
-
- // Cast descendant combinators to space
- type: match[ 0 ].replace( rtrim, " " )
- } );
- soFar = soFar.slice( matched.length );
- }
-
- // Filters
- for ( type in Expr.filter ) {
- if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
- ( match = preFilters[ type ]( match ) ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
- type: type,
- matches: match
- } );
- soFar = soFar.slice( matched.length );
- }
- }
-
- if ( !matched ) {
- break;
- }
- }
-
- // Return the length of the invalid excess
- // if we're just parsing
- // Otherwise, throw an error or return tokens
- return parseOnly ?
- soFar.length :
- soFar ?
- Sizzle.error( selector ) :
-
- // Cache the tokens
- tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
- var i = 0,
- len = tokens.length,
- selector = "";
- for ( ; i < len; i++ ) {
- selector += tokens[ i ].value;
- }
- return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
- var dir = combinator.dir,
- skip = combinator.next,
- key = skip || dir,
- checkNonElements = base && key === "parentNode",
- doneName = done++;
-
- return combinator.first ?
-
- // Check against closest ancestor/preceding element
- function( elem, context, xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- return matcher( elem, context, xml );
- }
- }
- return false;
- } :
-
- // Check against all ancestor/preceding elements
- function( elem, context, xml ) {
- var oldCache, uniqueCache, outerCache,
- newCache = [ dirruns, doneName ];
-
- // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
- if ( xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- if ( matcher( elem, context, xml ) ) {
- return true;
- }
- }
- }
- } else {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] ||
- ( outerCache[ elem.uniqueID ] = {} );
-
- if ( skip && skip === elem.nodeName.toLowerCase() ) {
- elem = elem[ dir ] || elem;
- } else if ( ( oldCache = uniqueCache[ key ] ) &&
- oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
- // Assign to newCache so results back-propagate to previous elements
- return ( newCache[ 2 ] = oldCache[ 2 ] );
- } else {
-
- // Reuse newcache so results back-propagate to previous elements
- uniqueCache[ key ] = newCache;
-
- // A match means we're done; a fail means we have to keep checking
- if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
- return true;
- }
- }
- }
- }
- }
- return false;
- };
-}
-
-function elementMatcher( matchers ) {
- return matchers.length > 1 ?
- function( elem, context, xml ) {
- var i = matchers.length;
- while ( i-- ) {
- if ( !matchers[ i ]( elem, context, xml ) ) {
- return false;
- }
- }
- return true;
- } :
- matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
- var i = 0,
- len = contexts.length;
- for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[ i ], results );
- }
- return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
- var elem,
- newUnmatched = [],
- i = 0,
- len = unmatched.length,
- mapped = map != null;
-
- for ( ; i < len; i++ ) {
- if ( ( elem = unmatched[ i ] ) ) {
- if ( !filter || filter( elem, context, xml ) ) {
- newUnmatched.push( elem );
- if ( mapped ) {
- map.push( i );
- }
- }
- }
- }
-
- return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
- if ( postFilter && !postFilter[ expando ] ) {
- postFilter = setMatcher( postFilter );
- }
- if ( postFinder && !postFinder[ expando ] ) {
- postFinder = setMatcher( postFinder, postSelector );
- }
- return markFunction( function( seed, results, context, xml ) {
- var temp, i, elem,
- preMap = [],
- postMap = [],
- preexisting = results.length,
-
- // Get initial elements from seed or context
- elems = seed || multipleContexts(
- selector || "*",
- context.nodeType ? [ context ] : context,
- []
- ),
-
- // Prefilter to get matcher input, preserving a map for seed-results synchronization
- matcherIn = preFilter && ( seed || !selector ) ?
- condense( elems, preMap, preFilter, context, xml ) :
- elems,
-
- matcherOut = matcher ?
-
- // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
- postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
- // ...intermediate processing is necessary
- [] :
-
- // ...otherwise use results directly
- results :
- matcherIn;
-
- // Find primary matches
- if ( matcher ) {
- matcher( matcherIn, matcherOut, context, xml );
- }
-
- // Apply postFilter
- if ( postFilter ) {
- temp = condense( matcherOut, postMap );
- postFilter( temp, [], context, xml );
-
- // Un-match failing elements by moving them back to matcherIn
- i = temp.length;
- while ( i-- ) {
- if ( ( elem = temp[ i ] ) ) {
- matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
- }
- }
- }
-
- if ( seed ) {
- if ( postFinder || preFilter ) {
- if ( postFinder ) {
-
- // Get the final matcherOut by condensing this intermediate into postFinder contexts
- temp = [];
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) ) {
-
- // Restore matcherIn since elem is not yet a final match
- temp.push( ( matcherIn[ i ] = elem ) );
- }
- }
- postFinder( null, ( matcherOut = [] ), temp, xml );
- }
-
- // Move matched elements from seed to results to keep them synchronized
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) &&
- ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
- seed[ temp ] = !( results[ temp ] = elem );
- }
- }
- }
-
- // Add elements to results, through postFinder if defined
- } else {
- matcherOut = condense(
- matcherOut === results ?
- matcherOut.splice( preexisting, matcherOut.length ) :
- matcherOut
- );
- if ( postFinder ) {
- postFinder( null, results, matcherOut, xml );
- } else {
- push.apply( results, matcherOut );
- }
- }
- } );
-}
-
-function matcherFromTokens( tokens ) {
- var checkContext, matcher, j,
- len = tokens.length,
- leadingRelative = Expr.relative[ tokens[ 0 ].type ],
- implicitRelative = leadingRelative || Expr.relative[ " " ],
- i = leadingRelative ? 1 : 0,
-
- // The foundational matcher ensures that elements are reachable from top-level context(s)
- matchContext = addCombinator( function( elem ) {
- return elem === checkContext;
- }, implicitRelative, true ),
- matchAnyContext = addCombinator( function( elem ) {
- return indexOf( checkContext, elem ) > -1;
- }, implicitRelative, true ),
- matchers = [ function( elem, context, xml ) {
- var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- ( checkContext = context ).nodeType ?
- matchContext( elem, context, xml ) :
- matchAnyContext( elem, context, xml ) );
-
- // Avoid hanging onto element (issue #299)
- checkContext = null;
- return ret;
- } ];
-
- for ( ; i < len; i++ ) {
- if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
- matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
- } else {
- matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
- // Return special upon seeing a positional matcher
- if ( matcher[ expando ] ) {
-
- // Find the next relative operator (if any) for proper handling
- j = ++i;
- for ( ; j < len; j++ ) {
- if ( Expr.relative[ tokens[ j ].type ] ) {
- break;
- }
- }
- return setMatcher(
- i > 1 && elementMatcher( matchers ),
- i > 1 && toSelector(
-
- // If the preceding token was a descendant combinator, insert an implicit any-element `*`
- tokens
- .slice( 0, i - 1 )
- .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
- ).replace( rtrim, "$1" ),
- matcher,
- i < j && matcherFromTokens( tokens.slice( i, j ) ),
- j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
- j < len && toSelector( tokens )
- );
- }
- matchers.push( matcher );
- }
- }
-
- return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
- var bySet = setMatchers.length > 0,
- byElement = elementMatchers.length > 0,
- superMatcher = function( seed, context, xml, results, outermost ) {
- var elem, j, matcher,
- matchedCount = 0,
- i = "0",
- unmatched = seed && [],
- setMatched = [],
- contextBackup = outermostContext,
-
- // We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
- // Use integer dirruns iff this is the outermost matcher
- dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
- len = elems.length;
-
- if ( outermost ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- outermostContext = context == document || context || outermost;
- }
-
- // Add elements passing elementMatchers directly to results
- // Support: IE<9, Safari
- // Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
- for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
- if ( byElement && elem ) {
- j = 0;
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( !context && elem.ownerDocument != document ) {
- setDocument( elem );
- xml = !documentIsHTML;
- }
- while ( ( matcher = elementMatchers[ j++ ] ) ) {
- if ( matcher( elem, context || document, xml ) ) {
- results.push( elem );
- break;
- }
- }
- if ( outermost ) {
- dirruns = dirrunsUnique;
- }
- }
-
- // Track unmatched elements for set filters
- if ( bySet ) {
-
- // They will have gone through all possible matchers
- if ( ( elem = !matcher && elem ) ) {
- matchedCount--;
- }
-
- // Lengthen the array for every element, matched or not
- if ( seed ) {
- unmatched.push( elem );
- }
- }
- }
-
- // `i` is now the count of elements visited above, and adding it to `matchedCount`
- // makes the latter nonnegative.
- matchedCount += i;
-
- // Apply set filters to unmatched elements
- // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
- // equals `i`), unless we didn't visit _any_ elements in the above loop because we have
- // no element matchers and no seed.
- // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
- // case, which will result in a "00" `matchedCount` that differs from `i` but is also
- // numerically zero.
- if ( bySet && i !== matchedCount ) {
- j = 0;
- while ( ( matcher = setMatchers[ j++ ] ) ) {
- matcher( unmatched, setMatched, context, xml );
- }
-
- if ( seed ) {
-
- // Reintegrate element matches to eliminate the need for sorting
- if ( matchedCount > 0 ) {
- while ( i-- ) {
- if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
- setMatched[ i ] = pop.call( results );
- }
- }
- }
-
- // Discard index placeholder values to get only actual matches
- setMatched = condense( setMatched );
- }
-
- // Add matches to results
- push.apply( results, setMatched );
-
- // Seedless set matches succeeding multiple successful matchers stipulate sorting
- if ( outermost && !seed && setMatched.length > 0 &&
- ( matchedCount + setMatchers.length ) > 1 ) {
-
- Sizzle.uniqueSort( results );
- }
- }
-
- // Override manipulation of globals by nested matchers
- if ( outermost ) {
- dirruns = dirrunsUnique;
- outermostContext = contextBackup;
- }
-
- return unmatched;
- };
-
- return bySet ?
- markFunction( superMatcher ) :
- superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
- var i,
- setMatchers = [],
- elementMatchers = [],
- cached = compilerCache[ selector + " " ];
-
- if ( !cached ) {
-
- // Generate a function of recursive functions that can be used to check each element
- if ( !match ) {
- match = tokenize( selector );
- }
- i = match.length;
- while ( i-- ) {
- cached = matcherFromTokens( match[ i ] );
- if ( cached[ expando ] ) {
- setMatchers.push( cached );
- } else {
- elementMatchers.push( cached );
- }
- }
-
- // Cache the compiled function
- cached = compilerCache(
- selector,
- matcherFromGroupMatchers( elementMatchers, setMatchers )
- );
-
- // Save selector and tokenization
- cached.selector = selector;
- }
- return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- * selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- * selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
- var i, tokens, token, type, find,
- compiled = typeof selector === "function" && selector,
- match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
- results = results || [];
-
- // Try to minimize operations if there is only one selector in the list and no seed
- // (the latter of which guarantees us context)
- if ( match.length === 1 ) {
-
- // Reduce context if the leading compound selector is an ID
- tokens = match[ 0 ] = match[ 0 ].slice( 0 );
- if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
- context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
- context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
- .replace( runescape, funescape ), context ) || [] )[ 0 ];
- if ( !context ) {
- return results;
-
- // Precompiled matchers will still verify ancestry, so step up a level
- } else if ( compiled ) {
- context = context.parentNode;
- }
-
- selector = selector.slice( tokens.shift().value.length );
- }
-
- // Fetch a seed set for right-to-left matching
- i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
- while ( i-- ) {
- token = tokens[ i ];
-
- // Abort if we hit a combinator
- if ( Expr.relative[ ( type = token.type ) ] ) {
- break;
- }
- if ( ( find = Expr.find[ type ] ) ) {
-
- // Search, expanding context for leading sibling combinators
- if ( ( seed = find(
- token.matches[ 0 ].replace( runescape, funescape ),
- rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
- context
- ) ) ) {
-
- // If seed is empty or no tokens remain, we can return early
- tokens.splice( i, 1 );
- selector = seed.length && toSelector( tokens );
- if ( !selector ) {
- push.apply( results, seed );
- return results;
- }
-
- break;
- }
- }
- }
- }
-
- // Compile and execute a filtering function if one is not provided
- // Provide `match` to avoid retokenization if we modified the selector above
- ( compiled || compile( selector, match ) )(
- seed,
- context,
- !documentIsHTML,
- results,
- !context || rsibling.test( selector ) && testContext( context.parentNode ) || context
- );
- return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
- // Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
- el.innerHTML = "<a href='#'></a>";
- return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
- addHandle( "type|href|height|width", function( elem, name, isXML ) {
- if ( !isXML ) {
- return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
- }
- } );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
- el.innerHTML = "<input/>";
- el.firstChild.setAttribute( "value", "" );
- return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
- addHandle( "value", function( elem, _name, isXML ) {
- if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
- return elem.defaultValue;
- }
- } );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
- return el.getAttribute( "disabled" ) == null;
-} ) ) {
- addHandle( booleans, function( elem, name, isXML ) {
- var val;
- if ( !isXML ) {
- return elem[ name ] === true ? name.toLowerCase() :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
- }
- } );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
- var matched = [],
- truncate = until !== undefined;
-
- while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
- if ( elem.nodeType === 1 ) {
- if ( truncate && jQuery( elem ).is( until ) ) {
- break;
- }
- matched.push( elem );
- }
- }
- return matched;
-};
-
-
-var siblings = function( n, elem ) {
- var matched = [];
-
- for ( ; n; n = n.nextSibling ) {
- if ( n.nodeType === 1 && n !== elem ) {
- matched.push( n );
- }
- }
-
- return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
- return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
- if ( isFunction( qualifier ) ) {
- return jQuery.grep( elements, function( elem, i ) {
- return !!qualifier.call( elem, i, elem ) !== not;
- } );
- }
-
- // Single element
- if ( qualifier.nodeType ) {
- return jQuery.grep( elements, function( elem ) {
- return ( elem === qualifier ) !== not;
- } );
- }
-
- // Arraylike of elements (jQuery, arguments, Array)
- if ( typeof qualifier !== "string" ) {
- return jQuery.grep( elements, function( elem ) {
- return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
- } );
- }
-
- // Filtered directly for both simple and complex selectors
- return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
- var elem = elems[ 0 ];
-
- if ( not ) {
- expr = ":not(" + expr + ")";
- }
-
- if ( elems.length === 1 && elem.nodeType === 1 ) {
- return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
- }
-
- return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
- return elem.nodeType === 1;
- } ) );
-};
-
-jQuery.fn.extend( {
- find: function( selector ) {
- var i, ret,
- len = this.length,
- self = this;
-
- if ( typeof selector !== "string" ) {
- return this.pushStack( jQuery( selector ).filter( function() {
- for ( i = 0; i < len; i++ ) {
- if ( jQuery.contains( self[ i ], this ) ) {
- return true;
- }
- }
- } ) );
- }
-
- ret = this.pushStack( [] );
-
- for ( i = 0; i < len; i++ ) {
- jQuery.find( selector, self[ i ], ret );
- }
-
- return len > 1 ? jQuery.uniqueSort( ret ) : ret;
- },
- filter: function( selector ) {
- return this.pushStack( winnow( this, selector || [], false ) );
- },
- not: function( selector ) {
- return this.pushStack( winnow( this, selector || [], true ) );
- },
- is: function( selector ) {
- return !!winnow(
- this,
-
- // If this is a positional/relative selector, check membership in the returned set
- // so $("p:first").is("p:last") won't return true for a doc with two "p".
- typeof selector === "string" && rneedsContext.test( selector ) ?
- jQuery( selector ) :
- selector || [],
- false
- ).length;
- }
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
- // A simple way to check for HTML strings
- // Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
- // Strict HTML recognition (#11290: must start with <)
- // Shortcut simple #id case for speed
- rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
- init = jQuery.fn.init = function( selector, context, root ) {
- var match, elem;
-
- // HANDLE: $(""), $(null), $(undefined), $(false)
- if ( !selector ) {
- return this;
- }
-
- // Method init() accepts an alternate rootjQuery
- // so migrate can support jQuery.sub (gh-2101)
- root = root || rootjQuery;
-
- // Handle HTML strings
- if ( typeof selector === "string" ) {
- if ( selector[ 0 ] === "<" &&
- selector[ selector.length - 1 ] === ">" &&
- selector.length >= 3 ) {
-
- // Assume that strings that start and end with <> are HTML and skip the regex check
- match = [ null, selector, null ];
-
- } else {
- match = rquickExpr.exec( selector );
- }
-
- // Match html or make sure no context is specified for #id
- if ( match && ( match[ 1 ] || !context ) ) {
-
- // HANDLE: $(html) -> $(array)
- if ( match[ 1 ] ) {
- context = context instanceof jQuery ? context[ 0 ] : context;
-
- // Option to run scripts is true for back-compat
- // Intentionally let the error be thrown if parseHTML is not present
- jQuery.merge( this, jQuery.parseHTML(
- match[ 1 ],
- context && context.nodeType ? context.ownerDocument || context : document,
- true
- ) );
-
- // HANDLE: $(html, props)
- if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
- for ( match in context ) {
-
- // Properties of context are called as methods if possible
- if ( isFunction( this[ match ] ) ) {
- this[ match ]( context[ match ] );
-
- // ...and otherwise set as attributes
- } else {
- this.attr( match, context[ match ] );
- }
- }
- }
-
- return this;
-
- // HANDLE: $(#id)
- } else {
- elem = document.getElementById( match[ 2 ] );
-
- if ( elem ) {
-
- // Inject the element directly into the jQuery object
- this[ 0 ] = elem;
- this.length = 1;
- }
- return this;
- }
-
- // HANDLE: $(expr, $(...))
- } else if ( !context || context.jquery ) {
- return ( context || root ).find( selector );
-
- // HANDLE: $(expr, context)
- // (which is just equivalent to: $(context).find(expr)
- } else {
- return this.constructor( context ).find( selector );
- }
-
- // HANDLE: $(DOMElement)
- } else if ( selector.nodeType ) {
- this[ 0 ] = selector;
- this.length = 1;
- return this;
-
- // HANDLE: $(function)
- // Shortcut for document ready
- } else if ( isFunction( selector ) ) {
- return root.ready !== undefined ?
- root.ready( selector ) :
-
- // Execute immediately if ready is not present
- selector( jQuery );
- }
-
- return jQuery.makeArray( selector, this );
- };
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
- // Methods guaranteed to produce a unique set when starting from a unique set
- guaranteedUnique = {
- children: true,
- contents: true,
- next: true,
- prev: true
- };
-
-jQuery.fn.extend( {
- has: function( target ) {
- var targets = jQuery( target, this ),
- l = targets.length;
-
- return this.filter( function() {
- var i = 0;
- for ( ; i < l; i++ ) {
- if ( jQuery.contains( this, targets[ i ] ) ) {
- return true;
- }
- }
- } );
- },
-
- closest: function( selectors, context ) {
- var cur,
- i = 0,
- l = this.length,
- matched = [],
- targets = typeof selectors !== "string" && jQuery( selectors );
-
- // Positional selectors never match, since there's no _selection_ context
- if ( !rneedsContext.test( selectors ) ) {
- for ( ; i < l; i++ ) {
- for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
- // Always skip document fragments
- if ( cur.nodeType < 11 && ( targets ?
- targets.index( cur ) > -1 :
-
- // Don't pass non-elements to Sizzle
- cur.nodeType === 1 &&
- jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
- matched.push( cur );
- break;
- }
- }
- }
- }
-
- return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
- },
-
- // Determine the position of an element within the set
- index: function( elem ) {
-
- // No argument, return index in parent
- if ( !elem ) {
- return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
- }
-
- // Index in selector
- if ( typeof elem === "string" ) {
- return indexOf.call( jQuery( elem ), this[ 0 ] );
- }
-
- // Locate the position of the desired element
- return indexOf.call( this,
-
- // If it receives a jQuery object, the first element is used
- elem.jquery ? elem[ 0 ] : elem
- );
- },
-
- add: function( selector, context ) {
- return this.pushStack(
- jQuery.uniqueSort(
- jQuery.merge( this.get(), jQuery( selector, context ) )
- )
- );
- },
-
- addBack: function( selector ) {
- return this.add( selector == null ?
- this.prevObject : this.prevObject.filter( selector )
- );
- }
-} );
-
-function sibling( cur, dir ) {
- while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
- return cur;
-}
-
-jQuery.each( {
- parent: function( elem ) {
- var parent = elem.parentNode;
- return parent && parent.nodeType !== 11 ? parent : null;
- },
- parents: function( elem ) {
- return dir( elem, "parentNode" );
- },
- parentsUntil: function( elem, _i, until ) {
- return dir( elem, "parentNode", until );
- },
- next: function( elem ) {
- return sibling( elem, "nextSibling" );
- },
- prev: function( elem ) {
- return sibling( elem, "previousSibling" );
- },
- nextAll: function( elem ) {
- return dir( elem, "nextSibling" );
- },
- prevAll: function( elem ) {
- return dir( elem, "previousSibling" );
- },
- nextUntil: function( elem, _i, until ) {
- return dir( elem, "nextSibling", until );
- },
- prevUntil: function( elem, _i, until ) {
- return dir( elem, "previousSibling", until );
- },
- siblings: function( elem ) {
- return siblings( ( elem.parentNode || {} ).firstChild, elem );
- },
- children: function( elem ) {
- return siblings( elem.firstChild );
- },
- contents: function( elem ) {
- if ( elem.contentDocument != null &&
-
- // Support: IE 11+
- // <object> elements with no `data` attribute has an object
- // `contentDocument` with a `null` prototype.
- getProto( elem.contentDocument ) ) {
-
- return elem.contentDocument;
- }
-
- // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
- // Treat the template element as a regular one in browsers that
- // don't support it.
- if ( nodeName( elem, "template" ) ) {
- elem = elem.content || elem;
- }
-
- return jQuery.merge( [], elem.childNodes );
- }
-}, function( name, fn ) {
- jQuery.fn[ name ] = function( until, selector ) {
- var matched = jQuery.map( this, fn, until );
-
- if ( name.slice( -5 ) !== "Until" ) {
- selector = until;
- }
-
- if ( selector && typeof selector === "string" ) {
- matched = jQuery.filter( selector, matched );
- }
-
- if ( this.length > 1 ) {
-
- // Remove duplicates
- if ( !guaranteedUnique[ name ] ) {
- jQuery.uniqueSort( matched );
- }
-
- // Reverse order for parents* and prev-derivatives
- if ( rparentsprev.test( name ) ) {
- matched.reverse();
- }
- }
-
- return this.pushStack( matched );
- };
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
- var object = {};
- jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
- object[ flag ] = true;
- } );
- return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- * options: an optional list of space-separated options that will change how
- * the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- * once: will ensure the callback list can only be fired once (like a Deferred)
- *
- * memory: will keep track of previous values and will call any callback added
- * after the list has been fired right away with the latest "memorized"
- * values (like a Deferred)
- *
- * unique: will ensure a callback can only be added once (no duplicate in the list)
- *
- * stopOnFalse: interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
- // Convert options from String-formatted to Object-formatted if needed
- // (we check in cache first)
- options = typeof options === "string" ?
- createOptions( options ) :
- jQuery.extend( {}, options );
-
- var // Flag to know if list is currently firing
- firing,
-
- // Last fire value for non-forgettable lists
- memory,
-
- // Flag to know if list was already fired
- fired,
-
- // Flag to prevent firing
- locked,
-
- // Actual callback list
- list = [],
-
- // Queue of execution data for repeatable lists
- queue = [],
-
- // Index of currently firing callback (modified by add/remove as needed)
- firingIndex = -1,
-
- // Fire callbacks
- fire = function() {
-
- // Enforce single-firing
- locked = locked || options.once;
-
- // Execute callbacks for all pending executions,
- // respecting firingIndex overrides and runtime changes
- fired = firing = true;
- for ( ; queue.length; firingIndex = -1 ) {
- memory = queue.shift();
- while ( ++firingIndex < list.length ) {
-
- // Run callback and check for early termination
- if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
- options.stopOnFalse ) {
-
- // Jump to end and forget the data so .add doesn't re-fire
- firingIndex = list.length;
- memory = false;
- }
- }
- }
-
- // Forget the data if we're done with it
- if ( !options.memory ) {
- memory = false;
- }
-
- firing = false;
-
- // Clean up if we're done firing for good
- if ( locked ) {
-
- // Keep an empty list if we have data for future add calls
- if ( memory ) {
- list = [];
-
- // Otherwise, this object is spent
- } else {
- list = "";
- }
- }
- },
-
- // Actual Callbacks object
- self = {
-
- // Add a callback or a collection of callbacks to the list
- add: function() {
- if ( list ) {
-
- // If we have memory from a past run, we should fire after adding
- if ( memory && !firing ) {
- firingIndex = list.length - 1;
- queue.push( memory );
- }
-
- ( function add( args ) {
- jQuery.each( args, function( _, arg ) {
- if ( isFunction( arg ) ) {
- if ( !options.unique || !self.has( arg ) ) {
- list.push( arg );
- }
- } else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
- // Inspect recursively
- add( arg );
- }
- } );
- } )( arguments );
-
- if ( memory && !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Remove a callback from the list
- remove: function() {
- jQuery.each( arguments, function( _, arg ) {
- var index;
- while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
- list.splice( index, 1 );
-
- // Handle firing indexes
- if ( index <= firingIndex ) {
- firingIndex--;
- }
- }
- } );
- return this;
- },
-
- // Check if a given callback is in the list.
- // If no argument is given, return whether or not list has callbacks attached.
- has: function( fn ) {
- return fn ?
- jQuery.inArray( fn, list ) > -1 :
- list.length > 0;
- },
-
- // Remove all callbacks from the list
- empty: function() {
- if ( list ) {
- list = [];
- }
- return this;
- },
-
- // Disable .fire and .add
- // Abort any current/pending executions
- // Clear all callbacks and values
- disable: function() {
- locked = queue = [];
- list = memory = "";
- return this;
- },
- disabled: function() {
- return !list;
- },
-
- // Disable .fire
- // Also disable .add unless we have memory (since it would have no effect)
- // Abort any pending executions
- lock: function() {
- locked = queue = [];
- if ( !memory && !firing ) {
- list = memory = "";
- }
- return this;
- },
- locked: function() {
- return !!locked;
- },
-
- // Call all callbacks with the given context and arguments
- fireWith: function( context, args ) {
- if ( !locked ) {
- args = args || [];
- args = [ context, args.slice ? args.slice() : args ];
- queue.push( args );
- if ( !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Call all the callbacks with the given arguments
- fire: function() {
- self.fireWith( this, arguments );
- return this;
- },
-
- // To know if the callbacks have already been called at least once
- fired: function() {
- return !!fired;
- }
- };
-
- return self;
-};
-
-
-function Identity( v ) {
- return v;
-}
-function Thrower( ex ) {
- throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
- var method;
-
- try {
-
- // Check for promise aspect first to privilege synchronous behavior
- if ( value && isFunction( ( method = value.promise ) ) ) {
- method.call( value ).done( resolve ).fail( reject );
-
- // Other thenables
- } else if ( value && isFunction( ( method = value.then ) ) ) {
- method.call( value, resolve, reject );
-
- // Other non-thenables
- } else {
-
- // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
- // * false: [ value ].slice( 0 ) => resolve( value )
- // * true: [ value ].slice( 1 ) => resolve()
- resolve.apply( undefined, [ value ].slice( noValue ) );
- }
-
- // For Promises/A+, convert exceptions into rejections
- // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
- // Deferred#then to conditionally suppress rejection.
- } catch ( value ) {
-
- // Support: Android 4.0 only
- // Strict mode functions invoked without .call/.apply get global-object context
- reject.apply( undefined, [ value ] );
- }
-}
-
-jQuery.extend( {
-
- Deferred: function( func ) {
- var tuples = [
-
- // action, add listener, callbacks,
- // ... .then handlers, argument index, [final state]
- [ "notify", "progress", jQuery.Callbacks( "memory" ),
- jQuery.Callbacks( "memory" ), 2 ],
- [ "resolve", "done", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 0, "resolved" ],
- [ "reject", "fail", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 1, "rejected" ]
- ],
- state = "pending",
- promise = {
- state: function() {
- return state;
- },
- always: function() {
- deferred.done( arguments ).fail( arguments );
- return this;
- },
- "catch": function( fn ) {
- return promise.then( null, fn );
- },
-
- // Keep pipe for back-compat
- pipe: function( /* fnDone, fnFail, fnProgress */ ) {
- var fns = arguments;
-
- return jQuery.Deferred( function( newDefer ) {
- jQuery.each( tuples, function( _i, tuple ) {
-
- // Map tuples (progress, done, fail) to arguments (done, fail, progress)
- var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
- // deferred.progress(function() { bind to newDefer or newDefer.notify })
- // deferred.done(function() { bind to newDefer or newDefer.resolve })
- // deferred.fail(function() { bind to newDefer or newDefer.reject })
- deferred[ tuple[ 1 ] ]( function() {
- var returned = fn && fn.apply( this, arguments );
- if ( returned && isFunction( returned.promise ) ) {
- returned.promise()
- .progress( newDefer.notify )
- .done( newDefer.resolve )
- .fail( newDefer.reject );
- } else {
- newDefer[ tuple[ 0 ] + "With" ](
- this,
- fn ? [ returned ] : arguments
- );
- }
- } );
- } );
- fns = null;
- } ).promise();
- },
- then: function( onFulfilled, onRejected, onProgress ) {
- var maxDepth = 0;
- function resolve( depth, deferred, handler, special ) {
- return function() {
- var that = this,
- args = arguments,
- mightThrow = function() {
- var returned, then;
-
- // Support: Promises/A+ section 2.3.3.3.3
- // https://promisesaplus.com/#point-59
- // Ignore double-resolution attempts
- if ( depth < maxDepth ) {
- return;
- }
-
- returned = handler.apply( that, args );
-
- // Support: Promises/A+ section 2.3.1
- // https://promisesaplus.com/#point-48
- if ( returned === deferred.promise() ) {
- throw new TypeError( "Thenable self-resolution" );
- }
-
- // Support: Promises/A+ sections 2.3.3.1, 3.5
- // https://promisesaplus.com/#point-54
- // https://promisesaplus.com/#point-75
- // Retrieve `then` only once
- then = returned &&
-
- // Support: Promises/A+ section 2.3.4
- // https://promisesaplus.com/#point-64
- // Only check objects and functions for thenability
- ( typeof returned === "object" ||
- typeof returned === "function" ) &&
- returned.then;
-
- // Handle a returned thenable
- if ( isFunction( then ) ) {
-
- // Special processors (notify) just wait for resolution
- if ( special ) {
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special )
- );
-
- // Normal processors (resolve) also hook into progress
- } else {
-
- // ...and disregard older resolution values
- maxDepth++;
-
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special ),
- resolve( maxDepth, deferred, Identity,
- deferred.notifyWith )
- );
- }
-
- // Handle all other returned values
- } else {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Identity ) {
- that = undefined;
- args = [ returned ];
- }
-
- // Process the value(s)
- // Default process is resolve
- ( special || deferred.resolveWith )( that, args );
- }
- },
-
- // Only normal processors (resolve) catch and reject exceptions
- process = special ?
- mightThrow :
- function() {
- try {
- mightThrow();
- } catch ( e ) {
-
- if ( jQuery.Deferred.exceptionHook ) {
- jQuery.Deferred.exceptionHook( e,
- process.stackTrace );
- }
-
- // Support: Promises/A+ section 2.3.3.3.4.1
- // https://promisesaplus.com/#point-61
- // Ignore post-resolution exceptions
- if ( depth + 1 >= maxDepth ) {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Thrower ) {
- that = undefined;
- args = [ e ];
- }
-
- deferred.rejectWith( that, args );
- }
- }
- };
-
- // Support: Promises/A+ section 2.3.3.3.1
- // https://promisesaplus.com/#point-57
- // Re-resolve promises immediately to dodge false rejection from
- // subsequent errors
- if ( depth ) {
- process();
- } else {
-
- // Call an optional hook to record the stack, in case of exception
- // since it's otherwise lost when execution goes async
- if ( jQuery.Deferred.getStackHook ) {
- process.stackTrace = jQuery.Deferred.getStackHook();
- }
- window.setTimeout( process );
- }
- };
- }
-
- return jQuery.Deferred( function( newDefer ) {
-
- // progress_handlers.add( ... )
- tuples[ 0 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onProgress ) ?
- onProgress :
- Identity,
- newDefer.notifyWith
- )
- );
-
- // fulfilled_handlers.add( ... )
- tuples[ 1 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onFulfilled ) ?
- onFulfilled :
- Identity
- )
- );
-
- // rejected_handlers.add( ... )
- tuples[ 2 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onRejected ) ?
- onRejected :
- Thrower
- )
- );
- } ).promise();
- },
-
- // Get a promise for this deferred
- // If obj is provided, the promise aspect is added to the object
- promise: function( obj ) {
- return obj != null ? jQuery.extend( obj, promise ) : promise;
- }
- },
- deferred = {};
-
- // Add list-specific methods
- jQuery.each( tuples, function( i, tuple ) {
- var list = tuple[ 2 ],
- stateString = tuple[ 5 ];
-
- // promise.progress = list.add
- // promise.done = list.add
- // promise.fail = list.add
- promise[ tuple[ 1 ] ] = list.add;
-
- // Handle state
- if ( stateString ) {
- list.add(
- function() {
-
- // state = "resolved" (i.e., fulfilled)
- // state = "rejected"
- state = stateString;
- },
-
- // rejected_callbacks.disable
- // fulfilled_callbacks.disable
- tuples[ 3 - i ][ 2 ].disable,
-
- // rejected_handlers.disable
- // fulfilled_handlers.disable
- tuples[ 3 - i ][ 3 ].disable,
-
- // progress_callbacks.lock
- tuples[ 0 ][ 2 ].lock,
-
- // progress_handlers.lock
- tuples[ 0 ][ 3 ].lock
- );
- }
-
- // progress_handlers.fire
- // fulfilled_handlers.fire
- // rejected_handlers.fire
- list.add( tuple[ 3 ].fire );
-
- // deferred.notify = function() { deferred.notifyWith(...) }
- // deferred.resolve = function() { deferred.resolveWith(...) }
- // deferred.reject = function() { deferred.rejectWith(...) }
- deferred[ tuple[ 0 ] ] = function() {
- deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
- return this;
- };
-
- // deferred.notifyWith = list.fireWith
- // deferred.resolveWith = list.fireWith
- // deferred.rejectWith = list.fireWith
- deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
- } );
-
- // Make the deferred a promise
- promise.promise( deferred );
-
- // Call given func if any
- if ( func ) {
- func.call( deferred, deferred );
- }
-
- // All done!
- return deferred;
- },
-
- // Deferred helper
- when: function( singleValue ) {
- var
-
- // count of uncompleted subordinates
- remaining = arguments.length,
-
- // count of unprocessed arguments
- i = remaining,
-
- // subordinate fulfillment data
- resolveContexts = Array( i ),
- resolveValues = slice.call( arguments ),
-
- // the primary Deferred
- primary = jQuery.Deferred(),
-
- // subordinate callback factory
- updateFunc = function( i ) {
- return function( value ) {
- resolveContexts[ i ] = this;
- resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
- if ( !( --remaining ) ) {
- primary.resolveWith( resolveContexts, resolveValues );
- }
- };
- };
-
- // Single- and empty arguments are adopted like Promise.resolve
- if ( remaining <= 1 ) {
- adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
- !remaining );
-
- // Use .then() to unwrap secondary thenables (cf. gh-3000)
- if ( primary.state() === "pending" ||
- isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
- return primary.then();
- }
- }
-
- // Multiple arguments are aggregated like Promise.all array elements
- while ( i-- ) {
- adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
- }
-
- return primary.promise();
- }
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
- // Support: IE 8 - 9 only
- // Console exists when dev tools are open, which can happen at any time
- if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
- window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
- }
-};
-
-
-
-
-jQuery.readyException = function( error ) {
- window.setTimeout( function() {
- throw error;
- } );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
- readyList
- .then( fn )
-
- // Wrap jQuery.readyException in a function so that the lookup
- // happens at the time of error handling instead of callback
- // registration.
- .catch( function( error ) {
- jQuery.readyException( error );
- } );
-
- return this;
-};
-
-jQuery.extend( {
-
- // Is the DOM ready to be used? Set to true once it occurs.
- isReady: false,
-
- // A counter to track how many items to wait for before
- // the ready event fires. See #6781
- readyWait: 1,
-
- // Handle when the DOM is ready
- ready: function( wait ) {
-
- // Abort if there are pending holds or we're already ready
- if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
- return;
- }
-
- // Remember that the DOM is ready
- jQuery.isReady = true;
-
- // If a normal DOM Ready event fired, decrement, and wait if need be
- if ( wait !== true && --jQuery.readyWait > 0 ) {
- return;
- }
-
- // If there are functions bound, to execute
- readyList.resolveWith( document, [ jQuery ] );
- }
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
- document.removeEventListener( "DOMContentLoaded", completed );
- window.removeEventListener( "load", completed );
- jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
- ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
- // Handle it asynchronously to allow scripts the opportunity to delay ready
- window.setTimeout( jQuery.ready );
-
-} else {
-
- // Use the handy event callback
- document.addEventListener( "DOMContentLoaded", completed );
-
- // A fallback to window.onload, that will always work
- window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
- var i = 0,
- len = elems.length,
- bulk = key == null;
-
- // Sets many values
- if ( toType( key ) === "object" ) {
- chainable = true;
- for ( i in key ) {
- access( elems, fn, i, key[ i ], true, emptyGet, raw );
- }
-
- // Sets one value
- } else if ( value !== undefined ) {
- chainable = true;
-
- if ( !isFunction( value ) ) {
- raw = true;
- }
-
- if ( bulk ) {
-
- // Bulk operations run against the entire set
- if ( raw ) {
- fn.call( elems, value );
- fn = null;
-
- // ...except when executing function values
- } else {
- bulk = fn;
- fn = function( elem, _key, value ) {
- return bulk.call( jQuery( elem ), value );
- };
- }
- }
-
- if ( fn ) {
- for ( ; i < len; i++ ) {
- fn(
- elems[ i ], key, raw ?
- value :
- value.call( elems[ i ], i, fn( elems[ i ], key ) )
- );
- }
- }
- }
-
- if ( chainable ) {
- return elems;
- }
-
- // Gets
- if ( bulk ) {
- return fn.call( elems );
- }
-
- return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
- rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
- return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
- return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
- // Accepts only:
- // - Node
- // - Node.ELEMENT_NODE
- // - Node.DOCUMENT_NODE
- // - Object
- // - Any
- return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
- this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
- cache: function( owner ) {
-
- // Check if the owner object already has a cache
- var value = owner[ this.expando ];
-
- // If not, create one
- if ( !value ) {
- value = {};
-
- // We can accept data for non-element nodes in modern browsers,
- // but we should not, see #8335.
- // Always return an empty object.
- if ( acceptData( owner ) ) {
-
- // If it is a node unlikely to be stringify-ed or looped over
- // use plain assignment
- if ( owner.nodeType ) {
- owner[ this.expando ] = value;
-
- // Otherwise secure it in a non-enumerable property
- // configurable must be true to allow the property to be
- // deleted when data is removed
- } else {
- Object.defineProperty( owner, this.expando, {
- value: value,
- configurable: true
- } );
- }
- }
- }
-
- return value;
- },
- set: function( owner, data, value ) {
- var prop,
- cache = this.cache( owner );
-
- // Handle: [ owner, key, value ] args
- // Always use camelCase key (gh-2257)
- if ( typeof data === "string" ) {
- cache[ camelCase( data ) ] = value;
-
- // Handle: [ owner, { properties } ] args
- } else {
-
- // Copy the properties one-by-one to the cache object
- for ( prop in data ) {
- cache[ camelCase( prop ) ] = data[ prop ];
- }
- }
- return cache;
- },
- get: function( owner, key ) {
- return key === undefined ?
- this.cache( owner ) :
-
- // Always use camelCase key (gh-2257)
- owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
- },
- access: function( owner, key, value ) {
-
- // In cases where either:
- //
- // 1. No key was specified
- // 2. A string key was specified, but no value provided
- //
- // Take the "read" path and allow the get method to determine
- // which value to return, respectively either:
- //
- // 1. The entire cache object
- // 2. The data stored at the key
- //
- if ( key === undefined ||
- ( ( key && typeof key === "string" ) && value === undefined ) ) {
-
- return this.get( owner, key );
- }
-
- // When the key is not a string, or both a key and value
- // are specified, set or extend (existing objects) with either:
- //
- // 1. An object of properties
- // 2. A key and value
- //
- this.set( owner, key, value );
-
- // Since the "set" path can have two possible entry points
- // return the expected data based on which path was taken[*]
- return value !== undefined ? value : key;
- },
- remove: function( owner, key ) {
- var i,
- cache = owner[ this.expando ];
-
- if ( cache === undefined ) {
- return;
- }
-
- if ( key !== undefined ) {
-
- // Support array or space separated string of keys
- if ( Array.isArray( key ) ) {
-
- // If key is an array of keys...
- // We always set camelCase keys, so remove that.
- key = key.map( camelCase );
- } else {
- key = camelCase( key );
-
- // If a key with the spaces exists, use it.
- // Otherwise, create an array by matching non-whitespace
- key = key in cache ?
- [ key ] :
- ( key.match( rnothtmlwhite ) || [] );
- }
-
- i = key.length;
-
- while ( i-- ) {
- delete cache[ key[ i ] ];
- }
- }
-
- // Remove the expando if there's no more data
- if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
- // Support: Chrome <=35 - 45
- // Webkit & Blink performance suffers when deleting properties
- // from DOM nodes, so set to undefined instead
- // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
- if ( owner.nodeType ) {
- owner[ this.expando ] = undefined;
- } else {
- delete owner[ this.expando ];
- }
- }
- },
- hasData: function( owner ) {
- var cache = owner[ this.expando ];
- return cache !== undefined && !jQuery.isEmptyObject( cache );
- }
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-// Implementation Summary
-//
-// 1. Enforce API surface and semantic compatibility with 1.9.x branch
-// 2. Improve the module's maintainability by reducing the storage
-// paths to a single mechanism.
-// 3. Use the same single mechanism to support "private" and "user" data.
-// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-// 5. Avoid exposing implementation details on user objects (eg. expando properties)
-// 6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
- rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
- if ( data === "true" ) {
- return true;
- }
-
- if ( data === "false" ) {
- return false;
- }
-
- if ( data === "null" ) {
- return null;
- }
-
- // Only convert to a number if it doesn't change the string
- if ( data === +data + "" ) {
- return +data;
- }
-
- if ( rbrace.test( data ) ) {
- return JSON.parse( data );
- }
-
- return data;
-}
-
-function dataAttr( elem, key, data ) {
- var name;
-
- // If nothing was found internally, try to fetch any
- // data from the HTML5 data-* attribute
- if ( data === undefined && elem.nodeType === 1 ) {
- name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
- data = elem.getAttribute( name );
-
- if ( typeof data === "string" ) {
- try {
- data = getData( data );
- } catch ( e ) {}
-
- // Make sure we set the data so it isn't changed later
- dataUser.set( elem, key, data );
- } else {
- data = undefined;
- }
- }
- return data;
-}
-
-jQuery.extend( {
- hasData: function( elem ) {
- return dataUser.hasData( elem ) || dataPriv.hasData( elem );
- },
-
- data: function( elem, name, data ) {
- return dataUser.access( elem, name, data );
- },
-
- removeData: function( elem, name ) {
- dataUser.remove( elem, name );
- },
-
- // TODO: Now that all calls to _data and _removeData have been replaced
- // with direct calls to dataPriv methods, these can be deprecated.
- _data: function( elem, name, data ) {
- return dataPriv.access( elem, name, data );
- },
-
- _removeData: function( elem, name ) {
- dataPriv.remove( elem, name );
- }
-} );
-
-jQuery.fn.extend( {
- data: function( key, value ) {
- var i, name, data,
- elem = this[ 0 ],
- attrs = elem && elem.attributes;
-
- // Gets all values
- if ( key === undefined ) {
- if ( this.length ) {
- data = dataUser.get( elem );
-
- if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
- i = attrs.length;
- while ( i-- ) {
-
- // Support: IE 11 only
- // The attrs elements can be null (#14894)
- if ( attrs[ i ] ) {
- name = attrs[ i ].name;
- if ( name.indexOf( "data-" ) === 0 ) {
- name = camelCase( name.slice( 5 ) );
- dataAttr( elem, name, data[ name ] );
- }
- }
- }
- dataPriv.set( elem, "hasDataAttrs", true );
- }
- }
-
- return data;
- }
-
- // Sets multiple values
- if ( typeof key === "object" ) {
- return this.each( function() {
- dataUser.set( this, key );
- } );
- }
-
- return access( this, function( value ) {
- var data;
-
- // The calling jQuery object (element matches) is not empty
- // (and therefore has an element appears at this[ 0 ]) and the
- // `value` parameter was not undefined. An empty jQuery object
- // will result in `undefined` for elem = this[ 0 ] which will
- // throw an exception if an attempt to read a data cache is made.
- if ( elem && value === undefined ) {
-
- // Attempt to get data from the cache
- // The key will always be camelCased in Data
- data = dataUser.get( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // Attempt to "discover" the data in
- // HTML5 custom data-* attrs
- data = dataAttr( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // We tried really hard, but the data doesn't exist.
- return;
- }
-
- // Set the data...
- this.each( function() {
-
- // We always store the camelCased key
- dataUser.set( this, key, value );
- } );
- }, null, value, arguments.length > 1, null, true );
- },
-
- removeData: function( key ) {
- return this.each( function() {
- dataUser.remove( this, key );
- } );
- }
-} );
-
-
-jQuery.extend( {
- queue: function( elem, type, data ) {
- var queue;
-
- if ( elem ) {
- type = ( type || "fx" ) + "queue";
- queue = dataPriv.get( elem, type );
-
- // Speed up dequeue by getting out quickly if this is just a lookup
- if ( data ) {
- if ( !queue || Array.isArray( data ) ) {
- queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
- } else {
- queue.push( data );
- }
- }
- return queue || [];
- }
- },
-
- dequeue: function( elem, type ) {
- type = type || "fx";
-
- var queue = jQuery.queue( elem, type ),
- startLength = queue.length,
- fn = queue.shift(),
- hooks = jQuery._queueHooks( elem, type ),
- next = function() {
- jQuery.dequeue( elem, type );
- };
-
- // If the fx queue is dequeued, always remove the progress sentinel
- if ( fn === "inprogress" ) {
- fn = queue.shift();
- startLength--;
- }
-
- if ( fn ) {
-
- // Add a progress sentinel to prevent the fx queue from being
- // automatically dequeued
- if ( type === "fx" ) {
- queue.unshift( "inprogress" );
- }
-
- // Clear up the last queue stop function
- delete hooks.stop;
- fn.call( elem, next, hooks );
- }
-
- if ( !startLength && hooks ) {
- hooks.empty.fire();
- }
- },
-
- // Not public - generate a queueHooks object, or return the current one
- _queueHooks: function( elem, type ) {
- var key = type + "queueHooks";
- return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
- empty: jQuery.Callbacks( "once memory" ).add( function() {
- dataPriv.remove( elem, [ type + "queue", key ] );
- } )
- } );
- }
-} );
-
-jQuery.fn.extend( {
- queue: function( type, data ) {
- var setter = 2;
-
- if ( typeof type !== "string" ) {
- data = type;
- type = "fx";
- setter--;
- }
-
- if ( arguments.length < setter ) {
- return jQuery.queue( this[ 0 ], type );
- }
-
- return data === undefined ?
- this :
- this.each( function() {
- var queue = jQuery.queue( this, type, data );
-
- // Ensure a hooks for this queue
- jQuery._queueHooks( this, type );
-
- if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- dequeue: function( type ) {
- return this.each( function() {
- jQuery.dequeue( this, type );
- } );
- },
- clearQueue: function( type ) {
- return this.queue( type || "fx", [] );
- },
-
- // Get a promise resolved when queues of a certain type
- // are emptied (fx is the type by default)
- promise: function( type, obj ) {
- var tmp,
- count = 1,
- defer = jQuery.Deferred(),
- elements = this,
- i = this.length,
- resolve = function() {
- if ( !( --count ) ) {
- defer.resolveWith( elements, [ elements ] );
- }
- };
-
- if ( typeof type !== "string" ) {
- obj = type;
- type = undefined;
- }
- type = type || "fx";
-
- while ( i-- ) {
- tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
- if ( tmp && tmp.empty ) {
- count++;
- tmp.empty.add( resolve );
- }
- }
- resolve();
- return defer.promise( obj );
- }
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
- var isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem );
- },
- composed = { composed: true };
-
- // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
- // Check attachment across shadow DOM boundaries when possible (gh-3504)
- // Support: iOS 10.0-10.2 only
- // Early iOS 10 versions support `attachShadow` but not `getRootNode`,
- // leading to errors. We need to check for `getRootNode`.
- if ( documentElement.getRootNode ) {
- isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem ) ||
- elem.getRootNode( composed ) === elem.ownerDocument;
- };
- }
-var isHiddenWithinTree = function( elem, el ) {
-
- // isHiddenWithinTree might be called from jQuery#filter function;
- // in that case, element will be second argument
- elem = el || elem;
-
- // Inline style trumps all
- return elem.style.display === "none" ||
- elem.style.display === "" &&
-
- // Otherwise, check computed style
- // Support: Firefox <=43 - 45
- // Disconnected elements can have computed display: none, so first confirm that elem is
- // in the document.
- isAttached( elem ) &&
-
- jQuery.css( elem, "display" ) === "none";
- };
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
- var adjusted, scale,
- maxIterations = 20,
- currentValue = tween ?
- function() {
- return tween.cur();
- } :
- function() {
- return jQuery.css( elem, prop, "" );
- },
- initial = currentValue(),
- unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
- // Starting value computation is required for potential unit mismatches
- initialInUnit = elem.nodeType &&
- ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
- rcssNum.exec( jQuery.css( elem, prop ) );
-
- if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
- // Support: Firefox <=54
- // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
- initial = initial / 2;
-
- // Trust units reported by jQuery.css
- unit = unit || initialInUnit[ 3 ];
-
- // Iteratively approximate from a nonzero starting point
- initialInUnit = +initial || 1;
-
- while ( maxIterations-- ) {
-
- // Evaluate and update our best guess (doubling guesses that zero out).
- // Finish if the scale equals or crosses 1 (making the old*new product non-positive).
- jQuery.style( elem, prop, initialInUnit + unit );
- if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
- maxIterations = 0;
- }
- initialInUnit = initialInUnit / scale;
-
- }
-
- initialInUnit = initialInUnit * 2;
- jQuery.style( elem, prop, initialInUnit + unit );
-
- // Make sure we update the tween properties later on
- valueParts = valueParts || [];
- }
-
- if ( valueParts ) {
- initialInUnit = +initialInUnit || +initial || 0;
-
- // Apply relative offset (+=/-=) if specified
- adjusted = valueParts[ 1 ] ?
- initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
- +valueParts[ 2 ];
- if ( tween ) {
- tween.unit = unit;
- tween.start = initialInUnit;
- tween.end = adjusted;
- }
- }
- return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
- var temp,
- doc = elem.ownerDocument,
- nodeName = elem.nodeName,
- display = defaultDisplayMap[ nodeName ];
-
- if ( display ) {
- return display;
- }
-
- temp = doc.body.appendChild( doc.createElement( nodeName ) );
- display = jQuery.css( temp, "display" );
-
- temp.parentNode.removeChild( temp );
-
- if ( display === "none" ) {
- display = "block";
- }
- defaultDisplayMap[ nodeName ] = display;
-
- return display;
-}
-
-function showHide( elements, show ) {
- var display, elem,
- values = [],
- index = 0,
- length = elements.length;
-
- // Determine new display value for elements that need to change
- for ( ; index < length; index++ ) {
- elem = elements[ index ];
- if ( !elem.style ) {
- continue;
- }
-
- display = elem.style.display;
- if ( show ) {
-
- // Since we force visibility upon cascade-hidden elements, an immediate (and slow)
- // check is required in this first loop unless we have a nonempty display value (either
- // inline or about-to-be-restored)
- if ( display === "none" ) {
- values[ index ] = dataPriv.get( elem, "display" ) || null;
- if ( !values[ index ] ) {
- elem.style.display = "";
- }
- }
- if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
- values[ index ] = getDefaultDisplay( elem );
- }
- } else {
- if ( display !== "none" ) {
- values[ index ] = "none";
-
- // Remember what we're overwriting
- dataPriv.set( elem, "display", display );
- }
- }
- }
-
- // Set the display of the elements in a second loop to avoid constant reflow
- for ( index = 0; index < length; index++ ) {
- if ( values[ index ] != null ) {
- elements[ index ].style.display = values[ index ];
- }
- }
-
- return elements;
-}
-
-jQuery.fn.extend( {
- show: function() {
- return showHide( this, true );
- },
- hide: function() {
- return showHide( this );
- },
- toggle: function( state ) {
- if ( typeof state === "boolean" ) {
- return state ? this.show() : this.hide();
- }
-
- return this.each( function() {
- if ( isHiddenWithinTree( this ) ) {
- jQuery( this ).show();
- } else {
- jQuery( this ).hide();
- }
- } );
- }
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
- var fragment = document.createDocumentFragment(),
- div = fragment.appendChild( document.createElement( "div" ) ),
- input = document.createElement( "input" );
-
- // Support: Android 4.0 - 4.3 only
- // Check state lost if the name is set (#11217)
- // Support: Windows Web Apps (WWA)
- // `name` and `type` must use .setAttribute for WWA (#14901)
- input.setAttribute( "type", "radio" );
- input.setAttribute( "checked", "checked" );
- input.setAttribute( "name", "t" );
-
- div.appendChild( input );
-
- // Support: Android <=4.1 only
- // Older WebKit doesn't clone checked state correctly in fragments
- support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
- // Support: IE <=11 only
- // Make sure textarea (and checkbox) defaultValue is properly cloned
- div.innerHTML = "<textarea>x</textarea>";
- support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
- // Support: IE <=9 only
- // IE <=9 replaces <option> tags with their contents when inserted outside of
- // the select element.
- div.innerHTML = "<option></option>";
- support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
- // XHTML parsers do not magically insert elements in the
- // same way that tag soup parsers do. So we cannot shorten
- // this by omitting <tbody> or other required elements.
- thead: [ 1, "<table>", "</table>" ],
- col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
- tr: [ 2, "<table><tbody>", "</tbody></table>" ],
- td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
- _default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
- wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
- // Support: IE <=9 - 11 only
- // Use typeof to avoid zero-argument method invocation on host objects (#15151)
- var ret;
-
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- ret = context.getElementsByTagName( tag || "*" );
-
- } else if ( typeof context.querySelectorAll !== "undefined" ) {
- ret = context.querySelectorAll( tag || "*" );
-
- } else {
- ret = [];
- }
-
- if ( tag === undefined || tag && nodeName( context, tag ) ) {
- return jQuery.merge( [ context ], ret );
- }
-
- return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
- var i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- dataPriv.set(
- elems[ i ],
- "globalEval",
- !refElements || dataPriv.get( refElements[ i ], "globalEval" )
- );
- }
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
- var elem, tmp, tag, wrap, attached, j,
- fragment = context.createDocumentFragment(),
- nodes = [],
- i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- elem = elems[ i ];
-
- if ( elem || elem === 0 ) {
-
- // Add nodes directly
- if ( toType( elem ) === "object" ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
- // Convert non-html into a text node
- } else if ( !rhtml.test( elem ) ) {
- nodes.push( context.createTextNode( elem ) );
-
- // Convert html into DOM nodes
- } else {
- tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
- // Deserialize a standard representation
- tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
- wrap = wrapMap[ tag ] || wrapMap._default;
- tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
- // Descend through wrappers to the right content
- j = wrap[ 0 ];
- while ( j-- ) {
- tmp = tmp.lastChild;
- }
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, tmp.childNodes );
-
- // Remember the top-level container
- tmp = fragment.firstChild;
-
- // Ensure the created nodes are orphaned (#12392)
- tmp.textContent = "";
- }
- }
- }
-
- // Remove wrapper from fragment
- fragment.textContent = "";
-
- i = 0;
- while ( ( elem = nodes[ i++ ] ) ) {
-
- // Skip elements already in the context collection (trac-4087)
- if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
- if ( ignored ) {
- ignored.push( elem );
- }
- continue;
- }
-
- attached = isAttached( elem );
-
- // Append to fragment
- tmp = getAll( fragment.appendChild( elem ), "script" );
-
- // Preserve script evaluation history
- if ( attached ) {
- setGlobalEval( tmp );
- }
-
- // Capture executables
- if ( scripts ) {
- j = 0;
- while ( ( elem = tmp[ j++ ] ) ) {
- if ( rscriptType.test( elem.type || "" ) ) {
- scripts.push( elem );
- }
- }
- }
- }
-
- return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
- return true;
-}
-
-function returnFalse() {
- return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
- return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
- try {
- return document.activeElement;
- } catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
- var origFn, type;
-
- // Types can be a map of types/handlers
- if ( typeof types === "object" ) {
-
- // ( types-Object, selector, data )
- if ( typeof selector !== "string" ) {
-
- // ( types-Object, data )
- data = data || selector;
- selector = undefined;
- }
- for ( type in types ) {
- on( elem, type, selector, data, types[ type ], one );
- }
- return elem;
- }
-
- if ( data == null && fn == null ) {
-
- // ( types, fn )
- fn = selector;
- data = selector = undefined;
- } else if ( fn == null ) {
- if ( typeof selector === "string" ) {
-
- // ( types, selector, fn )
- fn = data;
- data = undefined;
- } else {
-
- // ( types, data, fn )
- fn = data;
- data = selector;
- selector = undefined;
- }
- }
- if ( fn === false ) {
- fn = returnFalse;
- } else if ( !fn ) {
- return elem;
- }
-
- if ( one === 1 ) {
- origFn = fn;
- fn = function( event ) {
-
- // Can use an empty set, since event contains the info
- jQuery().off( event );
- return origFn.apply( this, arguments );
- };
-
- // Use same guid so caller can remove using origFn
- fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
- }
- return elem.each( function() {
- jQuery.event.add( this, types, fn, data, selector );
- } );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
- global: {},
-
- add: function( elem, types, handler, data, selector ) {
-
- var handleObjIn, eventHandle, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.get( elem );
-
- // Only attach events to objects that accept data
- if ( !acceptData( elem ) ) {
- return;
- }
-
- // Caller can pass in an object of custom data in lieu of the handler
- if ( handler.handler ) {
- handleObjIn = handler;
- handler = handleObjIn.handler;
- selector = handleObjIn.selector;
- }
-
- // Ensure that invalid selectors throw exceptions at attach time
- // Evaluate against documentElement in case elem is a non-element node (e.g., document)
- if ( selector ) {
- jQuery.find.matchesSelector( documentElement, selector );
- }
-
- // Make sure that the handler has a unique ID, used to find/remove it later
- if ( !handler.guid ) {
- handler.guid = jQuery.guid++;
- }
-
- // Init the element's event structure and main handler, if this is the first
- if ( !( events = elemData.events ) ) {
- events = elemData.events = Object.create( null );
- }
- if ( !( eventHandle = elemData.handle ) ) {
- eventHandle = elemData.handle = function( e ) {
-
- // Discard the second event of a jQuery.event.trigger() and
- // when an event is called after a page has unloaded
- return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
- jQuery.event.dispatch.apply( elem, arguments ) : undefined;
- };
- }
-
- // Handle multiple events separated by a space
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // There *must* be a type, no attaching namespace-only handlers
- if ( !type ) {
- continue;
- }
-
- // If event changes its type, use the special event handlers for the changed type
- special = jQuery.event.special[ type ] || {};
-
- // If selector defined, determine special event api type, otherwise given type
- type = ( selector ? special.delegateType : special.bindType ) || type;
-
- // Update special based on newly reset type
- special = jQuery.event.special[ type ] || {};
-
- // handleObj is passed to all event handlers
- handleObj = jQuery.extend( {
- type: type,
- origType: origType,
- data: data,
- handler: handler,
- guid: handler.guid,
- selector: selector,
- needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
- namespace: namespaces.join( "." )
- }, handleObjIn );
-
- // Init the event handler queue if we're the first
- if ( !( handlers = events[ type ] ) ) {
- handlers = events[ type ] = [];
- handlers.delegateCount = 0;
-
- // Only use addEventListener if the special events handler returns false
- if ( !special.setup ||
- special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
- if ( elem.addEventListener ) {
- elem.addEventListener( type, eventHandle );
- }
- }
- }
-
- if ( special.add ) {
- special.add.call( elem, handleObj );
-
- if ( !handleObj.handler.guid ) {
- handleObj.handler.guid = handler.guid;
- }
- }
-
- // Add to the element's handler list, delegates in front
- if ( selector ) {
- handlers.splice( handlers.delegateCount++, 0, handleObj );
- } else {
- handlers.push( handleObj );
- }
-
- // Keep track of which events have ever been used, for event optimization
- jQuery.event.global[ type ] = true;
- }
-
- },
-
- // Detach an event or set of events from an element
- remove: function( elem, types, handler, selector, mappedTypes ) {
-
- var j, origCount, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
- if ( !elemData || !( events = elemData.events ) ) {
- return;
- }
-
- // Once for each type.namespace in types; type may be omitted
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // Unbind all events (on this namespace, if provided) for the element
- if ( !type ) {
- for ( type in events ) {
- jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
- }
- continue;
- }
-
- special = jQuery.event.special[ type ] || {};
- type = ( selector ? special.delegateType : special.bindType ) || type;
- handlers = events[ type ] || [];
- tmp = tmp[ 2 ] &&
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
- // Remove matching events
- origCount = j = handlers.length;
- while ( j-- ) {
- handleObj = handlers[ j ];
-
- if ( ( mappedTypes || origType === handleObj.origType ) &&
- ( !handler || handler.guid === handleObj.guid ) &&
- ( !tmp || tmp.test( handleObj.namespace ) ) &&
- ( !selector || selector === handleObj.selector ||
- selector === "**" && handleObj.selector ) ) {
- handlers.splice( j, 1 );
-
- if ( handleObj.selector ) {
- handlers.delegateCount--;
- }
- if ( special.remove ) {
- special.remove.call( elem, handleObj );
- }
- }
- }
-
- // Remove generic event handler if we removed something and no more handlers exist
- // (avoids potential for endless recursion during removal of special event handlers)
- if ( origCount && !handlers.length ) {
- if ( !special.teardown ||
- special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
- jQuery.removeEvent( elem, type, elemData.handle );
- }
-
- delete events[ type ];
- }
- }
-
- // Remove data and the expando if it's no longer used
- if ( jQuery.isEmptyObject( events ) ) {
- dataPriv.remove( elem, "handle events" );
- }
- },
-
- dispatch: function( nativeEvent ) {
-
- var i, j, ret, matched, handleObj, handlerQueue,
- args = new Array( arguments.length ),
-
- // Make a writable jQuery.Event from the native event object
- event = jQuery.event.fix( nativeEvent ),
-
- handlers = (
- dataPriv.get( this, "events" ) || Object.create( null )
- )[ event.type ] || [],
- special = jQuery.event.special[ event.type ] || {};
-
- // Use the fix-ed jQuery.Event rather than the (read-only) native event
- args[ 0 ] = event;
-
- for ( i = 1; i < arguments.length; i++ ) {
- args[ i ] = arguments[ i ];
- }
-
- event.delegateTarget = this;
-
- // Call the preDispatch hook for the mapped type, and let it bail if desired
- if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
- return;
- }
-
- // Determine handlers
- handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
- // Run delegates first; they may want to stop propagation beneath us
- i = 0;
- while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
- event.currentTarget = matched.elem;
-
- j = 0;
- while ( ( handleObj = matched.handlers[ j++ ] ) &&
- !event.isImmediatePropagationStopped() ) {
-
- // If the event is namespaced, then each handler is only invoked if it is
- // specially universal or its namespaces are a superset of the event's.
- if ( !event.rnamespace || handleObj.namespace === false ||
- event.rnamespace.test( handleObj.namespace ) ) {
-
- event.handleObj = handleObj;
- event.data = handleObj.data;
-
- ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
- handleObj.handler ).apply( matched.elem, args );
-
- if ( ret !== undefined ) {
- if ( ( event.result = ret ) === false ) {
- event.preventDefault();
- event.stopPropagation();
- }
- }
- }
- }
- }
-
- // Call the postDispatch hook for the mapped type
- if ( special.postDispatch ) {
- special.postDispatch.call( this, event );
- }
-
- return event.result;
- },
-
- handlers: function( event, handlers ) {
- var i, handleObj, sel, matchedHandlers, matchedSelectors,
- handlerQueue = [],
- delegateCount = handlers.delegateCount,
- cur = event.target;
-
- // Find delegate handlers
- if ( delegateCount &&
-
- // Support: IE <=9
- // Black-hole SVG <use> instance trees (trac-13180)
- cur.nodeType &&
-
- // Support: Firefox <=42
- // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
- // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
- // Support: IE 11 only
- // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
- !( event.type === "click" && event.button >= 1 ) ) {
-
- for ( ; cur !== this; cur = cur.parentNode || this ) {
-
- // Don't check non-elements (#13208)
- // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
- if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
- matchedHandlers = [];
- matchedSelectors = {};
- for ( i = 0; i < delegateCount; i++ ) {
- handleObj = handlers[ i ];
-
- // Don't conflict with Object.prototype properties (#13203)
- sel = handleObj.selector + " ";
-
- if ( matchedSelectors[ sel ] === undefined ) {
- matchedSelectors[ sel ] = handleObj.needsContext ?
- jQuery( sel, this ).index( cur ) > -1 :
- jQuery.find( sel, this, null, [ cur ] ).length;
- }
- if ( matchedSelectors[ sel ] ) {
- matchedHandlers.push( handleObj );
- }
- }
- if ( matchedHandlers.length ) {
- handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
- }
- }
- }
- }
-
- // Add the remaining (directly-bound) handlers
- cur = this;
- if ( delegateCount < handlers.length ) {
- handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
- }
-
- return handlerQueue;
- },
-
- addProp: function( name, hook ) {
- Object.defineProperty( jQuery.Event.prototype, name, {
- enumerable: true,
- configurable: true,
-
- get: isFunction( hook ) ?
- function() {
- if ( this.originalEvent ) {
- return hook( this.originalEvent );
- }
- } :
- function() {
- if ( this.originalEvent ) {
- return this.originalEvent[ name ];
- }
- },
-
- set: function( value ) {
- Object.defineProperty( this, name, {
- enumerable: true,
- configurable: true,
- writable: true,
- value: value
- } );
- }
- } );
- },
-
- fix: function( originalEvent ) {
- return originalEvent[ jQuery.expando ] ?
- originalEvent :
- new jQuery.Event( originalEvent );
- },
-
- special: {
- load: {
-
- // Prevent triggered image.load events from bubbling to window.load
- noBubble: true
- },
- click: {
-
- // Utilize native event to ensure correct state for checkable inputs
- setup: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Claim the first handler
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- // dataPriv.set( el, "click", ... )
- leverageNative( el, "click", returnTrue );
- }
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Force setup before triggering a click
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- leverageNative( el, "click" );
- }
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // For cross-browser consistency, suppress native .click() on links
- // Also prevent it if we're currently inside a leveraged native-event stack
- _default: function( event ) {
- var target = event.target;
- return rcheckableType.test( target.type ) &&
- target.click && nodeName( target, "input" ) &&
- dataPriv.get( target, "click" ) ||
- nodeName( target, "a" );
- }
- },
-
- beforeunload: {
- postDispatch: function( event ) {
-
- // Support: Firefox 20+
- // Firefox doesn't alert if the returnValue field is not set.
- if ( event.result !== undefined && event.originalEvent ) {
- event.originalEvent.returnValue = event.result;
- }
- }
- }
- }
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
- // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
- if ( !expectSync ) {
- if ( dataPriv.get( el, type ) === undefined ) {
- jQuery.event.add( el, type, returnTrue );
- }
- return;
- }
-
- // Register the controller as a special universal handler for all event namespaces
- dataPriv.set( el, type, false );
- jQuery.event.add( el, type, {
- namespace: false,
- handler: function( event ) {
- var notAsync, result,
- saved = dataPriv.get( this, type );
-
- if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
- // Interrupt processing of the outer synthetic .trigger()ed event
- // Saved data should be false in such cases, but might be a leftover capture object
- // from an async native handler (gh-4350)
- if ( !saved.length ) {
-
- // Store arguments for use when handling the inner native event
- // There will always be at least one argument (an event object), so this array
- // will not be confused with a leftover capture object.
- saved = slice.call( arguments );
- dataPriv.set( this, type, saved );
-
- // Trigger the native event and capture its result
- // Support: IE <=9 - 11+
- // focus() and blur() are asynchronous
- notAsync = expectSync( this, type );
- this[ type ]();
- result = dataPriv.get( this, type );
- if ( saved !== result || notAsync ) {
- dataPriv.set( this, type, false );
- } else {
- result = {};
- }
- if ( saved !== result ) {
-
- // Cancel the outer synthetic event
- event.stopImmediatePropagation();
- event.preventDefault();
-
- // Support: Chrome 86+
- // In Chrome, if an element having a focusout handler is blurred by
- // clicking outside of it, it invokes the handler synchronously. If
- // that handler calls `.remove()` on the element, the data is cleared,
- // leaving `result` undefined. We need to guard against this.
- return result && result.value;
- }
-
- // If this is an inner synthetic event for an event with a bubbling surrogate
- // (focus or blur), assume that the surrogate already propagated from triggering the
- // native event and prevent that from happening again here.
- // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
- // bubbling surrogate propagates *after* the non-bubbling base), but that seems
- // less bad than duplication.
- } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
- event.stopPropagation();
- }
-
- // If this is a native event triggered above, everything is now in order
- // Fire an inner synthetic event with the original arguments
- } else if ( saved.length ) {
-
- // ...and capture the result
- dataPriv.set( this, type, {
- value: jQuery.event.trigger(
-
- // Support: IE <=9 - 11+
- // Extend with the prototype to reset the above stopImmediatePropagation()
- jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
- saved.slice( 1 ),
- this
- )
- } );
-
- // Abort handling of the native event
- event.stopImmediatePropagation();
- }
- }
- } );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
- // This "if" is needed for plain objects
- if ( elem.removeEventListener ) {
- elem.removeEventListener( type, handle );
- }
-};
-
-jQuery.Event = function( src, props ) {
-
- // Allow instantiation without the 'new' keyword
- if ( !( this instanceof jQuery.Event ) ) {
- return new jQuery.Event( src, props );
- }
-
- // Event object
- if ( src && src.type ) {
- this.originalEvent = src;
- this.type = src.type;
-
- // Events bubbling up the document may have been marked as prevented
- // by a handler lower down the tree; reflect the correct value.
- this.isDefaultPrevented = src.defaultPrevented ||
- src.defaultPrevented === undefined &&
-
- // Support: Android <=2.3 only
- src.returnValue === false ?
- returnTrue :
- returnFalse;
-
- // Create target properties
- // Support: Safari <=6 - 7 only
- // Target should not be a text node (#504, #13143)
- this.target = ( src.target && src.target.nodeType === 3 ) ?
- src.target.parentNode :
- src.target;
-
- this.currentTarget = src.currentTarget;
- this.relatedTarget = src.relatedTarget;
-
- // Event type
- } else {
- this.type = src;
- }
-
- // Put explicitly provided properties onto the event object
- if ( props ) {
- jQuery.extend( this, props );
- }
-
- // Create a timestamp if incoming event doesn't have one
- this.timeStamp = src && src.timeStamp || Date.now();
-
- // Mark it as fixed
- this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
- constructor: jQuery.Event,
- isDefaultPrevented: returnFalse,
- isPropagationStopped: returnFalse,
- isImmediatePropagationStopped: returnFalse,
- isSimulated: false,
-
- preventDefault: function() {
- var e = this.originalEvent;
-
- this.isDefaultPrevented = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.preventDefault();
- }
- },
- stopPropagation: function() {
- var e = this.originalEvent;
-
- this.isPropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopPropagation();
- }
- },
- stopImmediatePropagation: function() {
- var e = this.originalEvent;
-
- this.isImmediatePropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopImmediatePropagation();
- }
-
- this.stopPropagation();
- }
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
- altKey: true,
- bubbles: true,
- cancelable: true,
- changedTouches: true,
- ctrlKey: true,
- detail: true,
- eventPhase: true,
- metaKey: true,
- pageX: true,
- pageY: true,
- shiftKey: true,
- view: true,
- "char": true,
- code: true,
- charCode: true,
- key: true,
- keyCode: true,
- button: true,
- buttons: true,
- clientX: true,
- clientY: true,
- offsetX: true,
- offsetY: true,
- pointerId: true,
- pointerType: true,
- screenX: true,
- screenY: true,
- targetTouches: true,
- toElement: true,
- touches: true,
- which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
- jQuery.event.special[ type ] = {
-
- // Utilize native event if possible so blur/focus sequence is correct
- setup: function() {
-
- // Claim the first handler
- // dataPriv.set( this, "focus", ... )
- // dataPriv.set( this, "blur", ... )
- leverageNative( this, type, expectSync );
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function() {
-
- // Force setup before trigger
- leverageNative( this, type );
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // Suppress native focus or blur as it's already being fired
- // in leverageNative.
- _default: function() {
- return true;
- },
-
- delegateType: delegateType
- };
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
- mouseenter: "mouseover",
- mouseleave: "mouseout",
- pointerenter: "pointerover",
- pointerleave: "pointerout"
-}, function( orig, fix ) {
- jQuery.event.special[ orig ] = {
- delegateType: fix,
- bindType: fix,
-
- handle: function( event ) {
- var ret,
- target = this,
- related = event.relatedTarget,
- handleObj = event.handleObj;
-
- // For mouseenter/leave call the handler if related is outside the target.
- // NB: No relatedTarget if the mouse left/entered the browser window
- if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
- event.type = handleObj.origType;
- ret = handleObj.handler.apply( this, arguments );
- event.type = fix;
- }
- return ret;
- }
- };
-} );
-
-jQuery.fn.extend( {
-
- on: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn );
- },
- one: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn, 1 );
- },
- off: function( types, selector, fn ) {
- var handleObj, type;
- if ( types && types.preventDefault && types.handleObj ) {
-
- // ( event ) dispatched jQuery.Event
- handleObj = types.handleObj;
- jQuery( types.delegateTarget ).off(
- handleObj.namespace ?
- handleObj.origType + "." + handleObj.namespace :
- handleObj.origType,
- handleObj.selector,
- handleObj.handler
- );
- return this;
- }
- if ( typeof types === "object" ) {
-
- // ( types-object [, selector] )
- for ( type in types ) {
- this.off( type, selector, types[ type ] );
- }
- return this;
- }
- if ( selector === false || typeof selector === "function" ) {
-
- // ( types [, fn] )
- fn = selector;
- selector = undefined;
- }
- if ( fn === false ) {
- fn = returnFalse;
- }
- return this.each( function() {
- jQuery.event.remove( this, types, fn, selector );
- } );
- }
-} );
-
-
-var
-
- // Support: IE <=10 - 11, Edge 12 - 13 only
- // In IE/Edge using regex groups here causes severe slowdowns.
- // See https://connect.microsoft.com/IE/feedback/details/1736512/
- rnoInnerhtml = /<script|<style|<link/i,
-
- // checked="checked" or checked
- rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
- rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
- if ( nodeName( elem, "table" ) &&
- nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
- return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
- }
-
- return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
- elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
- return elem;
-}
-function restoreScript( elem ) {
- if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
- elem.type = elem.type.slice( 5 );
- } else {
- elem.removeAttribute( "type" );
- }
-
- return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
- var i, l, type, pdataOld, udataOld, udataCur, events;
-
- if ( dest.nodeType !== 1 ) {
- return;
- }
-
- // 1. Copy private data: events, handlers, etc.
- if ( dataPriv.hasData( src ) ) {
- pdataOld = dataPriv.get( src );
- events = pdataOld.events;
-
- if ( events ) {
- dataPriv.remove( dest, "handle events" );
-
- for ( type in events ) {
- for ( i = 0, l = events[ type ].length; i < l; i++ ) {
- jQuery.event.add( dest, type, events[ type ][ i ] );
- }
- }
- }
- }
-
- // 2. Copy user data
- if ( dataUser.hasData( src ) ) {
- udataOld = dataUser.access( src );
- udataCur = jQuery.extend( {}, udataOld );
-
- dataUser.set( dest, udataCur );
- }
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
- var nodeName = dest.nodeName.toLowerCase();
-
- // Fails to persist the checked state of a cloned checkbox or radio button.
- if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
- dest.checked = src.checked;
-
- // Fails to return the selected option to the default selected state when cloning options
- } else if ( nodeName === "input" || nodeName === "textarea" ) {
- dest.defaultValue = src.defaultValue;
- }
-}
-
-function domManip( collection, args, callback, ignored ) {
-
- // Flatten any nested arrays
- args = flat( args );
-
- var fragment, first, scripts, hasScripts, node, doc,
- i = 0,
- l = collection.length,
- iNoClone = l - 1,
- value = args[ 0 ],
- valueIsFunction = isFunction( value );
-
- // We can't cloneNode fragments that contain checked, in WebKit
- if ( valueIsFunction ||
- ( l > 1 && typeof value === "string" &&
- !support.checkClone && rchecked.test( value ) ) ) {
- return collection.each( function( index ) {
- var self = collection.eq( index );
- if ( valueIsFunction ) {
- args[ 0 ] = value.call( this, index, self.html() );
- }
- domManip( self, args, callback, ignored );
- } );
- }
-
- if ( l ) {
- fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
- first = fragment.firstChild;
-
- if ( fragment.childNodes.length === 1 ) {
- fragment = first;
- }
-
- // Require either new content or an interest in ignored elements to invoke the callback
- if ( first || ignored ) {
- scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
- hasScripts = scripts.length;
-
- // Use the original fragment for the last item
- // instead of the first because it can end up
- // being emptied incorrectly in certain situations (#8070).
- for ( ; i < l; i++ ) {
- node = fragment;
-
- if ( i !== iNoClone ) {
- node = jQuery.clone( node, true, true );
-
- // Keep references to cloned scripts for later restoration
- if ( hasScripts ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( scripts, getAll( node, "script" ) );
- }
- }
-
- callback.call( collection[ i ], node, i );
- }
-
- if ( hasScripts ) {
- doc = scripts[ scripts.length - 1 ].ownerDocument;
-
- // Reenable scripts
- jQuery.map( scripts, restoreScript );
-
- // Evaluate executable scripts on first document insertion
- for ( i = 0; i < hasScripts; i++ ) {
- node = scripts[ i ];
- if ( rscriptType.test( node.type || "" ) &&
- !dataPriv.access( node, "globalEval" ) &&
- jQuery.contains( doc, node ) ) {
-
- if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) {
-
- // Optional AJAX dependency, but won't run scripts if not present
- if ( jQuery._evalUrl && !node.noModule ) {
- jQuery._evalUrl( node.src, {
- nonce: node.nonce || node.getAttribute( "nonce" )
- }, doc );
- }
- } else {
- DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
- }
- }
- }
- }
- }
- }
-
- return collection;
-}
-
-function remove( elem, selector, keepData ) {
- var node,
- nodes = selector ? jQuery.filter( selector, elem ) : elem,
- i = 0;
-
- for ( ; ( node = nodes[ i ] ) != null; i++ ) {
- if ( !keepData && node.nodeType === 1 ) {
- jQuery.cleanData( getAll( node ) );
- }
-
- if ( node.parentNode ) {
- if ( keepData && isAttached( node ) ) {
- setGlobalEval( getAll( node, "script" ) );
- }
- node.parentNode.removeChild( node );
- }
- }
-
- return elem;
-}
-
-jQuery.extend( {
- htmlPrefilter: function( html ) {
- return html;
- },
-
- clone: function( elem, dataAndEvents, deepDataAndEvents ) {
- var i, l, srcElements, destElements,
- clone = elem.cloneNode( true ),
- inPage = isAttached( elem );
-
- // Fix IE cloning issues
- if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
- !jQuery.isXMLDoc( elem ) ) {
-
- // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
- destElements = getAll( clone );
- srcElements = getAll( elem );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- fixInput( srcElements[ i ], destElements[ i ] );
- }
- }
-
- // Copy the events from the original to the clone
- if ( dataAndEvents ) {
- if ( deepDataAndEvents ) {
- srcElements = srcElements || getAll( elem );
- destElements = destElements || getAll( clone );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- cloneCopyEvent( srcElements[ i ], destElements[ i ] );
- }
- } else {
- cloneCopyEvent( elem, clone );
- }
- }
-
- // Preserve script evaluation history
- destElements = getAll( clone, "script" );
- if ( destElements.length > 0 ) {
- setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
- }
-
- // Return the cloned set
- return clone;
- },
-
- cleanData: function( elems ) {
- var data, elem, type,
- special = jQuery.event.special,
- i = 0;
-
- for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
- if ( acceptData( elem ) ) {
- if ( ( data = elem[ dataPriv.expando ] ) ) {
- if ( data.events ) {
- for ( type in data.events ) {
- if ( special[ type ] ) {
- jQuery.event.remove( elem, type );
-
- // This is a shortcut to avoid jQuery.event.remove's overhead
- } else {
- jQuery.removeEvent( elem, type, data.handle );
- }
- }
- }
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataPriv.expando ] = undefined;
- }
- if ( elem[ dataUser.expando ] ) {
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataUser.expando ] = undefined;
- }
- }
- }
- }
-} );
-
-jQuery.fn.extend( {
- detach: function( selector ) {
- return remove( this, selector, true );
- },
-
- remove: function( selector ) {
- return remove( this, selector );
- },
-
- text: function( value ) {
- return access( this, function( value ) {
- return value === undefined ?
- jQuery.text( this ) :
- this.empty().each( function() {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- this.textContent = value;
- }
- } );
- }, null, value, arguments.length );
- },
-
- append: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.appendChild( elem );
- }
- } );
- },
-
- prepend: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.insertBefore( elem, target.firstChild );
- }
- } );
- },
-
- before: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this );
- }
- } );
- },
-
- after: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this.nextSibling );
- }
- } );
- },
-
- empty: function() {
- var elem,
- i = 0;
-
- for ( ; ( elem = this[ i ] ) != null; i++ ) {
- if ( elem.nodeType === 1 ) {
-
- // Prevent memory leaks
- jQuery.cleanData( getAll( elem, false ) );
-
- // Remove any remaining nodes
- elem.textContent = "";
- }
- }
-
- return this;
- },
-
- clone: function( dataAndEvents, deepDataAndEvents ) {
- dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
- deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
- return this.map( function() {
- return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
- } );
- },
-
- html: function( value ) {
- return access( this, function( value ) {
- var elem = this[ 0 ] || {},
- i = 0,
- l = this.length;
-
- if ( value === undefined && elem.nodeType === 1 ) {
- return elem.innerHTML;
- }
-
- // See if we can take a shortcut and just use innerHTML
- if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
- !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
- value = jQuery.htmlPrefilter( value );
-
- try {
- for ( ; i < l; i++ ) {
- elem = this[ i ] || {};
-
- // Remove element nodes and prevent memory leaks
- if ( elem.nodeType === 1 ) {
- jQuery.cleanData( getAll( elem, false ) );
- elem.innerHTML = value;
- }
- }
-
- elem = 0;
-
- // If using innerHTML throws an exception, use the fallback method
- } catch ( e ) {}
- }
-
- if ( elem ) {
- this.empty().append( value );
- }
- }, null, value, arguments.length );
- },
-
- replaceWith: function() {
- var ignored = [];
-
- // Make the changes, replacing each non-ignored context element with the new content
- return domManip( this, arguments, function( elem ) {
- var parent = this.parentNode;
-
- if ( jQuery.inArray( this, ignored ) < 0 ) {
- jQuery.cleanData( getAll( this ) );
- if ( parent ) {
- parent.replaceChild( elem, this );
- }
- }
-
- // Force callback invocation
- }, ignored );
- }
-} );
-
-jQuery.each( {
- appendTo: "append",
- prependTo: "prepend",
- insertBefore: "before",
- insertAfter: "after",
- replaceAll: "replaceWith"
-}, function( name, original ) {
- jQuery.fn[ name ] = function( selector ) {
- var elems,
- ret = [],
- insert = jQuery( selector ),
- last = insert.length - 1,
- i = 0;
-
- for ( ; i <= last; i++ ) {
- elems = i === last ? this : this.clone( true );
- jQuery( insert[ i ] )[ original ]( elems );
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // .get() because push.apply(_, arraylike) throws on ancient WebKit
- push.apply( ret, elems.get() );
- }
-
- return this.pushStack( ret );
- };
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
- // Support: IE <=11 only, Firefox <=30 (#15098, #14150)
- // IE throws on elements created in popups
- // FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
- var view = elem.ownerDocument.defaultView;
-
- if ( !view || !view.opener ) {
- view = window;
- }
-
- return view.getComputedStyle( elem );
- };
-
-var swap = function( elem, options, callback ) {
- var ret, name,
- old = {};
-
- // Remember the old values, and insert the new ones
- for ( name in options ) {
- old[ name ] = elem.style[ name ];
- elem.style[ name ] = options[ name ];
- }
-
- ret = callback.call( elem );
-
- // Revert the old values
- for ( name in options ) {
- elem.style[ name ] = old[ name ];
- }
-
- return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
- // Executing both pixelPosition & boxSizingReliable tests require only one layout
- // so they're executed at the same time to save the second computation.
- function computeStyleTests() {
-
- // This is a singleton, we need to execute it only once
- if ( !div ) {
- return;
- }
-
- container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
- "margin-top:1px;padding:0;border:0";
- div.style.cssText =
- "position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
- "margin:auto;border:1px;padding:1px;" +
- "width:60%;top:1%";
- documentElement.appendChild( container ).appendChild( div );
-
- var divStyle = window.getComputedStyle( div );
- pixelPositionVal = divStyle.top !== "1%";
-
- // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
- reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
- // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
- // Some styles come back with percentage values, even though they shouldn't
- div.style.right = "60%";
- pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
- // Support: IE 9 - 11 only
- // Detect misreporting of content dimensions for box-sizing:border-box elements
- boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
- // Support: IE 9 only
- // Detect overflow:scroll screwiness (gh-3699)
- // Support: Chrome <=64
- // Don't get tricked when zoom affects offsetWidth (gh-4029)
- div.style.position = "absolute";
- scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
- documentElement.removeChild( container );
-
- // Nullify the div so it wouldn't be stored in the memory and
- // it will also be a sign that checks already performed
- div = null;
- }
-
- function roundPixelMeasures( measure ) {
- return Math.round( parseFloat( measure ) );
- }
-
- var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
- reliableTrDimensionsVal, reliableMarginLeftVal,
- container = document.createElement( "div" ),
- div = document.createElement( "div" );
-
- // Finish early in limited (non-browser) environments
- if ( !div.style ) {
- return;
- }
-
- // Support: IE <=9 - 11 only
- // Style of cloned element affects source element cloned (#8908)
- div.style.backgroundClip = "content-box";
- div.cloneNode( true ).style.backgroundClip = "";
- support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
- jQuery.extend( support, {
- boxSizingReliable: function() {
- computeStyleTests();
- return boxSizingReliableVal;
- },
- pixelBoxStyles: function() {
- computeStyleTests();
- return pixelBoxStylesVal;
- },
- pixelPosition: function() {
- computeStyleTests();
- return pixelPositionVal;
- },
- reliableMarginLeft: function() {
- computeStyleTests();
- return reliableMarginLeftVal;
- },
- scrollboxSize: function() {
- computeStyleTests();
- return scrollboxSizeVal;
- },
-
- // Support: IE 9 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Behavior in IE 9 is more subtle than in newer versions & it passes
- // some versions of this test; make sure not to make it pass there!
- //
- // Support: Firefox 70+
- // Only Firefox includes border widths
- // in computed dimensions. (gh-4529)
- reliableTrDimensions: function() {
- var table, tr, trChild, trStyle;
- if ( reliableTrDimensionsVal == null ) {
- table = document.createElement( "table" );
- tr = document.createElement( "tr" );
- trChild = document.createElement( "div" );
-
- table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
- tr.style.cssText = "border:1px solid";
-
- // Support: Chrome 86+
- // Height set through cssText does not get applied.
- // Computed height then comes back as 0.
- tr.style.height = "1px";
- trChild.style.height = "9px";
-
- // Support: Android 8 Chrome 86+
- // In our bodyBackground.html iframe,
- // display for all div elements is set to "inline",
- // which causes a problem only in Android 8 Chrome 86.
- // Ensuring the div is display: block
- // gets around this issue.
- trChild.style.display = "block";
-
- documentElement
- .appendChild( table )
- .appendChild( tr )
- .appendChild( trChild );
-
- trStyle = window.getComputedStyle( tr );
- reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
- parseInt( trStyle.borderTopWidth, 10 ) +
- parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
- documentElement.removeChild( table );
- }
- return reliableTrDimensionsVal;
- }
- } );
-} )();
-
-
-function curCSS( elem, name, computed ) {
- var width, minWidth, maxWidth, ret,
-
- // Support: Firefox 51+
- // Retrieving style before computed somehow
- // fixes an issue with getting wrong values
- // on detached elements
- style = elem.style;
-
- computed = computed || getStyles( elem );
-
- // getPropertyValue is needed for:
- // .css('filter') (IE 9 only, #12537)
- // .css('--customProperty) (#3144)
- if ( computed ) {
- ret = computed.getPropertyValue( name ) || computed[ name ];
-
- if ( ret === "" && !isAttached( elem ) ) {
- ret = jQuery.style( elem, name );
- }
-
- // A tribute to the "awesome hack by Dean Edwards"
- // Android Browser returns percentage for some values,
- // but width seems to be reliably pixels.
- // This is against the CSSOM draft spec:
- // https://drafts.csswg.org/cssom/#resolved-values
- if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
- // Remember the original values
- width = style.width;
- minWidth = style.minWidth;
- maxWidth = style.maxWidth;
-
- // Put in the new values to get a computed value out
- style.minWidth = style.maxWidth = style.width = ret;
- ret = computed.width;
-
- // Revert the changed values
- style.width = width;
- style.minWidth = minWidth;
- style.maxWidth = maxWidth;
- }
- }
-
- return ret !== undefined ?
-
- // Support: IE <=9 - 11 only
- // IE returns zIndex value as an integer.
- ret + "" :
- ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
- // Define the hook, we'll check on the first run if it's really needed.
- return {
- get: function() {
- if ( conditionFn() ) {
-
- // Hook not needed (or it's not possible to use it due
- // to missing dependency), remove it.
- delete this.get;
- return;
- }
-
- // Hook needed; redefine it so that the support test is not executed again.
- return ( this.get = hookFn ).apply( this, arguments );
- }
- };
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
- emptyStyle = document.createElement( "div" ).style,
- vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
- // Check for vendor prefixed names
- var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
- i = cssPrefixes.length;
-
- while ( i-- ) {
- name = cssPrefixes[ i ] + capName;
- if ( name in emptyStyle ) {
- return name;
- }
- }
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
- var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
- if ( final ) {
- return final;
- }
- if ( name in emptyStyle ) {
- return name;
- }
- return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
- // Swappable if display is none or starts with table
- // except "table", "table-cell", or "table-caption"
- // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
- rdisplayswap = /^(none|table(?!-c[ea]).+)/,
- rcustomProp = /^--/,
- cssShow = { position: "absolute", visibility: "hidden", display: "block" },
- cssNormalTransform = {
- letterSpacing: "0",
- fontWeight: "400"
- };
-
-function setPositiveNumber( _elem, value, subtract ) {
-
- // Any relative (+/-) values have already been
- // normalized at this point
- var matches = rcssNum.exec( value );
- return matches ?
-
- // Guard against undefined "subtract", e.g., when used as in cssHooks
- Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
- value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
- var i = dimension === "width" ? 1 : 0,
- extra = 0,
- delta = 0;
-
- // Adjustment may not be necessary
- if ( box === ( isBorderBox ? "border" : "content" ) ) {
- return 0;
- }
-
- for ( ; i < 4; i += 2 ) {
-
- // Both box models exclude margin
- if ( box === "margin" ) {
- delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
- }
-
- // If we get here with a content-box, we're seeking "padding" or "border" or "margin"
- if ( !isBorderBox ) {
-
- // Add padding
- delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
- // For "border" or "margin", add border
- if ( box !== "padding" ) {
- delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
- // But still keep track of it otherwise
- } else {
- extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
-
- // If we get here with a border-box (content + padding + border), we're seeking "content" or
- // "padding" or "margin"
- } else {
-
- // For "content", subtract padding
- if ( box === "content" ) {
- delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
- }
-
- // For "content" or "padding", subtract border
- if ( box !== "margin" ) {
- delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
- }
- }
-
- // Account for positive content-box scroll gutter when requested by providing computedVal
- if ( !isBorderBox && computedVal >= 0 ) {
-
- // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
- // Assuming integer scroll gutter, subtract the rest and round down
- delta += Math.max( 0, Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- computedVal -
- delta -
- extra -
- 0.5
-
- // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
- // Use an explicit zero to avoid NaN (gh-3964)
- ) ) || 0;
- }
-
- return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
- // Start with computed style
- var styles = getStyles( elem ),
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
- // Fake content-box until we know it's needed to know the true value.
- boxSizingNeeded = !support.boxSizingReliable() || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- valueIsBorderBox = isBorderBox,
-
- val = curCSS( elem, dimension, styles ),
- offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
- // Support: Firefox <=54
- // Return a confounding non-pixel value or feign ignorance, as appropriate.
- if ( rnumnonpx.test( val ) ) {
- if ( !extra ) {
- return val;
- }
- val = "auto";
- }
-
-
- // Support: IE 9 - 11 only
- // Use offsetWidth/offsetHeight for when box sizing is unreliable.
- // In those cases, the computed value can be trusted to be border-box.
- if ( ( !support.boxSizingReliable() && isBorderBox ||
-
- // Support: IE 10 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Interestingly, in some cases IE 9 doesn't suffer from this issue.
- !support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
- // Fall back to offsetWidth/offsetHeight when value is "auto"
- // This happens for inline elements with no explicit setting (gh-3571)
- val === "auto" ||
-
- // Support: Android <=4.1 - 4.3 only
- // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
- !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
- // Make sure the element is visible & connected
- elem.getClientRects().length ) {
-
- isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
- // Where available, offsetWidth/offsetHeight approximate border box dimensions.
- // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
- // retrieved value as a content box dimension.
- valueIsBorderBox = offsetProp in elem;
- if ( valueIsBorderBox ) {
- val = elem[ offsetProp ];
- }
- }
-
- // Normalize "" and auto
- val = parseFloat( val ) || 0;
-
- // Adjust for the element's box model
- return ( val +
- boxModelAdjustment(
- elem,
- dimension,
- extra || ( isBorderBox ? "border" : "content" ),
- valueIsBorderBox,
- styles,
-
- // Provide the current computed size to request scroll gutter calculation (gh-3589)
- val
- )
- ) + "px";
-}
-
-jQuery.extend( {
-
- // Add in style property hooks for overriding the default
- // behavior of getting and setting a style property
- cssHooks: {
- opacity: {
- get: function( elem, computed ) {
- if ( computed ) {
-
- // We should always get a number back from opacity
- var ret = curCSS( elem, "opacity" );
- return ret === "" ? "1" : ret;
- }
- }
- }
- },
-
- // Don't automatically add "px" to these possibly-unitless properties
- cssNumber: {
- "animationIterationCount": true,
- "columnCount": true,
- "fillOpacity": true,
- "flexGrow": true,
- "flexShrink": true,
- "fontWeight": true,
- "gridArea": true,
- "gridColumn": true,
- "gridColumnEnd": true,
- "gridColumnStart": true,
- "gridRow": true,
- "gridRowEnd": true,
- "gridRowStart": true,
- "lineHeight": true,
- "opacity": true,
- "order": true,
- "orphans": true,
- "widows": true,
- "zIndex": true,
- "zoom": true
- },
-
- // Add in properties whose names you wish to fix before
- // setting or getting the value
- cssProps: {},
-
- // Get and set the style property on a DOM Node
- style: function( elem, name, value, extra ) {
-
- // Don't set styles on text and comment nodes
- if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
- return;
- }
-
- // Make sure that we're working with the right name
- var ret, type, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name ),
- style = elem.style;
-
- // Make sure that we're working with the right name. We don't
- // want to query the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Gets hook for the prefixed version, then unprefixed version
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // Check if we're setting a value
- if ( value !== undefined ) {
- type = typeof value;
-
- // Convert "+=" or "-=" to relative numbers (#7345)
- if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
- value = adjustCSS( elem, name, ret );
-
- // Fixes bug #9237
- type = "number";
- }
-
- // Make sure that null and NaN values aren't set (#7116)
- if ( value == null || value !== value ) {
- return;
- }
-
- // If a number was passed in, add the unit (except for certain CSS properties)
- // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
- // "px" to a few hardcoded values.
- if ( type === "number" && !isCustomProp ) {
- value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
- }
-
- // background-* props affect original clone's values
- if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
- style[ name ] = "inherit";
- }
-
- // If a hook was provided, use that value, otherwise just set the specified value
- if ( !hooks || !( "set" in hooks ) ||
- ( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
- if ( isCustomProp ) {
- style.setProperty( name, value );
- } else {
- style[ name ] = value;
- }
- }
-
- } else {
-
- // If a hook was provided get the non-computed value from there
- if ( hooks && "get" in hooks &&
- ( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
- return ret;
- }
-
- // Otherwise just get the value from the style object
- return style[ name ];
- }
- },
-
- css: function( elem, name, extra, styles ) {
- var val, num, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name );
-
- // Make sure that we're working with the right name. We don't
- // want to modify the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Try prefixed name followed by the unprefixed name
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // If a hook was provided get the computed value from there
- if ( hooks && "get" in hooks ) {
- val = hooks.get( elem, true, extra );
- }
-
- // Otherwise, if a way to get the computed value exists, use that
- if ( val === undefined ) {
- val = curCSS( elem, name, styles );
- }
-
- // Convert "normal" to computed value
- if ( val === "normal" && name in cssNormalTransform ) {
- val = cssNormalTransform[ name ];
- }
-
- // Make numeric if forced or a qualifier was provided and val looks numeric
- if ( extra === "" || extra ) {
- num = parseFloat( val );
- return extra === true || isFinite( num ) ? num || 0 : val;
- }
-
- return val;
- }
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
- jQuery.cssHooks[ dimension ] = {
- get: function( elem, computed, extra ) {
- if ( computed ) {
-
- // Certain elements can have dimension info if we invisibly show them
- // but it must have a current display style that would benefit
- return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
- // Support: Safari 8+
- // Table columns in Safari have non-zero offsetWidth & zero
- // getBoundingClientRect().width unless display is changed.
- // Support: IE <=11 only
- // Running getBoundingClientRect on a disconnected node
- // in IE throws an error.
- ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
- swap( elem, cssShow, function() {
- return getWidthOrHeight( elem, dimension, extra );
- } ) :
- getWidthOrHeight( elem, dimension, extra );
- }
- },
-
- set: function( elem, value, extra ) {
- var matches,
- styles = getStyles( elem ),
-
- // Only read styles.position if the test has a chance to fail
- // to avoid forcing a reflow.
- scrollboxSizeBuggy = !support.scrollboxSize() &&
- styles.position === "absolute",
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
- boxSizingNeeded = scrollboxSizeBuggy || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- subtract = extra ?
- boxModelAdjustment(
- elem,
- dimension,
- extra,
- isBorderBox,
- styles
- ) :
- 0;
-
- // Account for unreliable border-box dimensions by comparing offset* to computed and
- // faking a content-box to get border and padding (gh-3699)
- if ( isBorderBox && scrollboxSizeBuggy ) {
- subtract -= Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- parseFloat( styles[ dimension ] ) -
- boxModelAdjustment( elem, dimension, "border", false, styles ) -
- 0.5
- );
- }
-
- // Convert to pixels if value adjustment is needed
- if ( subtract && ( matches = rcssNum.exec( value ) ) &&
- ( matches[ 3 ] || "px" ) !== "px" ) {
-
- elem.style[ dimension ] = value;
- value = jQuery.css( elem, dimension );
- }
-
- return setPositiveNumber( elem, value, subtract );
- }
- };
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
- function( elem, computed ) {
- if ( computed ) {
- return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
- elem.getBoundingClientRect().left -
- swap( elem, { marginLeft: 0 }, function() {
- return elem.getBoundingClientRect().left;
- } )
- ) + "px";
- }
- }
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
- margin: "",
- padding: "",
- border: "Width"
-}, function( prefix, suffix ) {
- jQuery.cssHooks[ prefix + suffix ] = {
- expand: function( value ) {
- var i = 0,
- expanded = {},
-
- // Assumes a single number if not a string
- parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
- for ( ; i < 4; i++ ) {
- expanded[ prefix + cssExpand[ i ] + suffix ] =
- parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
- }
-
- return expanded;
- }
- };
-
- if ( prefix !== "margin" ) {
- jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
- }
-} );
-
-jQuery.fn.extend( {
- css: function( name, value ) {
- return access( this, function( elem, name, value ) {
- var styles, len,
- map = {},
- i = 0;
-
- if ( Array.isArray( name ) ) {
- styles = getStyles( elem );
- len = name.length;
-
- for ( ; i < len; i++ ) {
- map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
- }
-
- return map;
- }
-
- return value !== undefined ?
- jQuery.style( elem, name, value ) :
- jQuery.css( elem, name );
- }, name, value, arguments.length > 1 );
- }
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
- return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
- constructor: Tween,
- init: function( elem, options, prop, end, easing, unit ) {
- this.elem = elem;
- this.prop = prop;
- this.easing = easing || jQuery.easing._default;
- this.options = options;
- this.start = this.now = this.cur();
- this.end = end;
- this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
- },
- cur: function() {
- var hooks = Tween.propHooks[ this.prop ];
-
- return hooks && hooks.get ?
- hooks.get( this ) :
- Tween.propHooks._default.get( this );
- },
- run: function( percent ) {
- var eased,
- hooks = Tween.propHooks[ this.prop ];
-
- if ( this.options.duration ) {
- this.pos = eased = jQuery.easing[ this.easing ](
- percent, this.options.duration * percent, 0, 1, this.options.duration
- );
- } else {
- this.pos = eased = percent;
- }
- this.now = ( this.end - this.start ) * eased + this.start;
-
- if ( this.options.step ) {
- this.options.step.call( this.elem, this.now, this );
- }
-
- if ( hooks && hooks.set ) {
- hooks.set( this );
- } else {
- Tween.propHooks._default.set( this );
- }
- return this;
- }
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
- _default: {
- get: function( tween ) {
- var result;
-
- // Use a property on the element directly when it is not a DOM element,
- // or when there is no matching style property that exists.
- if ( tween.elem.nodeType !== 1 ||
- tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
- return tween.elem[ tween.prop ];
- }
-
- // Passing an empty string as a 3rd parameter to .css will automatically
- // attempt a parseFloat and fallback to a string if the parse fails.
- // Simple values such as "10px" are parsed to Float;
- // complex values such as "rotate(1rad)" are returned as-is.
- result = jQuery.css( tween.elem, tween.prop, "" );
-
- // Empty strings, null, undefined and "auto" are converted to 0.
- return !result || result === "auto" ? 0 : result;
- },
- set: function( tween ) {
-
- // Use step hook for back compat.
- // Use cssHook if its there.
- // Use .style if available and use plain properties where available.
- if ( jQuery.fx.step[ tween.prop ] ) {
- jQuery.fx.step[ tween.prop ]( tween );
- } else if ( tween.elem.nodeType === 1 && (
- jQuery.cssHooks[ tween.prop ] ||
- tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
- jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
- } else {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
- }
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
- set: function( tween ) {
- if ( tween.elem.nodeType && tween.elem.parentNode ) {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
-};
-
-jQuery.easing = {
- linear: function( p ) {
- return p;
- },
- swing: function( p ) {
- return 0.5 - Math.cos( p * Math.PI ) / 2;
- },
- _default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
- fxNow, inProgress,
- rfxtypes = /^(?:toggle|show|hide)$/,
- rrun = /queueHooks$/;
-
-function schedule() {
- if ( inProgress ) {
- if ( document.hidden === false && window.requestAnimationFrame ) {
- window.requestAnimationFrame( schedule );
- } else {
- window.setTimeout( schedule, jQuery.fx.interval );
- }
-
- jQuery.fx.tick();
- }
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
- window.setTimeout( function() {
- fxNow = undefined;
- } );
- return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
- var which,
- i = 0,
- attrs = { height: type };
-
- // If we include width, step value is 1 to do all cssExpand values,
- // otherwise step value is 2 to skip over Left and Right
- includeWidth = includeWidth ? 1 : 0;
- for ( ; i < 4; i += 2 - includeWidth ) {
- which = cssExpand[ i ];
- attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
- }
-
- if ( includeWidth ) {
- attrs.opacity = attrs.width = type;
- }
-
- return attrs;
-}
-
-function createTween( value, prop, animation ) {
- var tween,
- collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
- index = 0,
- length = collection.length;
- for ( ; index < length; index++ ) {
- if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
- // We're done with this property
- return tween;
- }
- }
-}
-
-function defaultPrefilter( elem, props, opts ) {
- var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
- isBox = "width" in props || "height" in props,
- anim = this,
- orig = {},
- style = elem.style,
- hidden = elem.nodeType && isHiddenWithinTree( elem ),
- dataShow = dataPriv.get( elem, "fxshow" );
-
- // Queue-skipping animations hijack the fx hooks
- if ( !opts.queue ) {
- hooks = jQuery._queueHooks( elem, "fx" );
- if ( hooks.unqueued == null ) {
- hooks.unqueued = 0;
- oldfire = hooks.empty.fire;
- hooks.empty.fire = function() {
- if ( !hooks.unqueued ) {
- oldfire();
- }
- };
- }
- hooks.unqueued++;
-
- anim.always( function() {
-
- // Ensure the complete handler is called before this completes
- anim.always( function() {
- hooks.unqueued--;
- if ( !jQuery.queue( elem, "fx" ).length ) {
- hooks.empty.fire();
- }
- } );
- } );
- }
-
- // Detect show/hide animations
- for ( prop in props ) {
- value = props[ prop ];
- if ( rfxtypes.test( value ) ) {
- delete props[ prop ];
- toggle = toggle || value === "toggle";
- if ( value === ( hidden ? "hide" : "show" ) ) {
-
- // Pretend to be hidden if this is a "show" and
- // there is still data from a stopped show/hide
- if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
- hidden = true;
-
- // Ignore all other no-op show/hide data
- } else {
- continue;
- }
- }
- orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
- }
- }
-
- // Bail out if this is a no-op like .hide().hide()
- propTween = !jQuery.isEmptyObject( props );
- if ( !propTween && jQuery.isEmptyObject( orig ) ) {
- return;
- }
-
- // Restrict "overflow" and "display" styles during box animations
- if ( isBox && elem.nodeType === 1 ) {
-
- // Support: IE <=9 - 11, Edge 12 - 15
- // Record all 3 overflow attributes because IE does not infer the shorthand
- // from identically-valued overflowX and overflowY and Edge just mirrors
- // the overflowX value there.
- opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
- // Identify a display type, preferring old show/hide data over the CSS cascade
- restoreDisplay = dataShow && dataShow.display;
- if ( restoreDisplay == null ) {
- restoreDisplay = dataPriv.get( elem, "display" );
- }
- display = jQuery.css( elem, "display" );
- if ( display === "none" ) {
- if ( restoreDisplay ) {
- display = restoreDisplay;
- } else {
-
- // Get nonempty value(s) by temporarily forcing visibility
- showHide( [ elem ], true );
- restoreDisplay = elem.style.display || restoreDisplay;
- display = jQuery.css( elem, "display" );
- showHide( [ elem ] );
- }
- }
-
- // Animate inline elements as inline-block
- if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
- if ( jQuery.css( elem, "float" ) === "none" ) {
-
- // Restore the original display value at the end of pure show/hide animations
- if ( !propTween ) {
- anim.done( function() {
- style.display = restoreDisplay;
- } );
- if ( restoreDisplay == null ) {
- display = style.display;
- restoreDisplay = display === "none" ? "" : display;
- }
- }
- style.display = "inline-block";
- }
- }
- }
-
- if ( opts.overflow ) {
- style.overflow = "hidden";
- anim.always( function() {
- style.overflow = opts.overflow[ 0 ];
- style.overflowX = opts.overflow[ 1 ];
- style.overflowY = opts.overflow[ 2 ];
- } );
- }
-
- // Implement show/hide animations
- propTween = false;
- for ( prop in orig ) {
-
- // General show/hide setup for this element animation
- if ( !propTween ) {
- if ( dataShow ) {
- if ( "hidden" in dataShow ) {
- hidden = dataShow.hidden;
- }
- } else {
- dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
- }
-
- // Store hidden/visible for toggle so `.stop().toggle()` "reverses"
- if ( toggle ) {
- dataShow.hidden = !hidden;
- }
-
- // Show elements before animating them
- if ( hidden ) {
- showHide( [ elem ], true );
- }
-
- /* eslint-disable no-loop-func */
-
- anim.done( function() {
-
- /* eslint-enable no-loop-func */
-
- // The final step of a "hide" animation is actually hiding the element
- if ( !hidden ) {
- showHide( [ elem ] );
- }
- dataPriv.remove( elem, "fxshow" );
- for ( prop in orig ) {
- jQuery.style( elem, prop, orig[ prop ] );
- }
- } );
- }
-
- // Per-property setup
- propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
- if ( !( prop in dataShow ) ) {
- dataShow[ prop ] = propTween.start;
- if ( hidden ) {
- propTween.end = propTween.start;
- propTween.start = 0;
- }
- }
- }
-}
-
-function propFilter( props, specialEasing ) {
- var index, name, easing, value, hooks;
-
- // camelCase, specialEasing and expand cssHook pass
- for ( index in props ) {
- name = camelCase( index );
- easing = specialEasing[ name ];
- value = props[ index ];
- if ( Array.isArray( value ) ) {
- easing = value[ 1 ];
- value = props[ index ] = value[ 0 ];
- }
-
- if ( index !== name ) {
- props[ name ] = value;
- delete props[ index ];
- }
-
- hooks = jQuery.cssHooks[ name ];
- if ( hooks && "expand" in hooks ) {
- value = hooks.expand( value );
- delete props[ name ];
-
- // Not quite $.extend, this won't overwrite existing keys.
- // Reusing 'index' because we have the correct "name"
- for ( index in value ) {
- if ( !( index in props ) ) {
- props[ index ] = value[ index ];
- specialEasing[ index ] = easing;
- }
- }
- } else {
- specialEasing[ name ] = easing;
- }
- }
-}
-
-function Animation( elem, properties, options ) {
- var result,
- stopped,
- index = 0,
- length = Animation.prefilters.length,
- deferred = jQuery.Deferred().always( function() {
-
- // Don't match elem in the :animated selector
- delete tick.elem;
- } ),
- tick = function() {
- if ( stopped ) {
- return false;
- }
- var currentTime = fxNow || createFxNow(),
- remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
- // Support: Android 2.3 only
- // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
- temp = remaining / animation.duration || 0,
- percent = 1 - temp,
- index = 0,
- length = animation.tweens.length;
-
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( percent );
- }
-
- deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
- // If there's more to do, yield
- if ( percent < 1 && length ) {
- return remaining;
- }
-
- // If this was an empty animation, synthesize a final progress notification
- if ( !length ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- }
-
- // Resolve the animation and report its conclusion
- deferred.resolveWith( elem, [ animation ] );
- return false;
- },
- animation = deferred.promise( {
- elem: elem,
- props: jQuery.extend( {}, properties ),
- opts: jQuery.extend( true, {
- specialEasing: {},
- easing: jQuery.easing._default
- }, options ),
- originalProperties: properties,
- originalOptions: options,
- startTime: fxNow || createFxNow(),
- duration: options.duration,
- tweens: [],
- createTween: function( prop, end ) {
- var tween = jQuery.Tween( elem, animation.opts, prop, end,
- animation.opts.specialEasing[ prop ] || animation.opts.easing );
- animation.tweens.push( tween );
- return tween;
- },
- stop: function( gotoEnd ) {
- var index = 0,
-
- // If we are going to the end, we want to run all the tweens
- // otherwise we skip this part
- length = gotoEnd ? animation.tweens.length : 0;
- if ( stopped ) {
- return this;
- }
- stopped = true;
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( 1 );
- }
-
- // Resolve when we played the last frame; otherwise, reject
- if ( gotoEnd ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- deferred.resolveWith( elem, [ animation, gotoEnd ] );
- } else {
- deferred.rejectWith( elem, [ animation, gotoEnd ] );
- }
- return this;
- }
- } ),
- props = animation.props;
-
- propFilter( props, animation.opts.specialEasing );
-
- for ( ; index < length; index++ ) {
- result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
- if ( result ) {
- if ( isFunction( result.stop ) ) {
- jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
- result.stop.bind( result );
- }
- return result;
- }
- }
-
- jQuery.map( props, createTween, animation );
-
- if ( isFunction( animation.opts.start ) ) {
- animation.opts.start.call( elem, animation );
- }
-
- // Attach callbacks from options
- animation
- .progress( animation.opts.progress )
- .done( animation.opts.done, animation.opts.complete )
- .fail( animation.opts.fail )
- .always( animation.opts.always );
-
- jQuery.fx.timer(
- jQuery.extend( tick, {
- elem: elem,
- anim: animation,
- queue: animation.opts.queue
- } )
- );
-
- return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
- tweeners: {
- "*": [ function( prop, value ) {
- var tween = this.createTween( prop, value );
- adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
- return tween;
- } ]
- },
-
- tweener: function( props, callback ) {
- if ( isFunction( props ) ) {
- callback = props;
- props = [ "*" ];
- } else {
- props = props.match( rnothtmlwhite );
- }
-
- var prop,
- index = 0,
- length = props.length;
-
- for ( ; index < length; index++ ) {
- prop = props[ index ];
- Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
- Animation.tweeners[ prop ].unshift( callback );
- }
- },
-
- prefilters: [ defaultPrefilter ],
-
- prefilter: function( callback, prepend ) {
- if ( prepend ) {
- Animation.prefilters.unshift( callback );
- } else {
- Animation.prefilters.push( callback );
- }
- }
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
- var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
- complete: fn || !fn && easing ||
- isFunction( speed ) && speed,
- duration: speed,
- easing: fn && easing || easing && !isFunction( easing ) && easing
- };
-
- // Go to the end state if fx are off
- if ( jQuery.fx.off ) {
- opt.duration = 0;
-
- } else {
- if ( typeof opt.duration !== "number" ) {
- if ( opt.duration in jQuery.fx.speeds ) {
- opt.duration = jQuery.fx.speeds[ opt.duration ];
-
- } else {
- opt.duration = jQuery.fx.speeds._default;
- }
- }
- }
-
- // Normalize opt.queue - true/undefined/null -> "fx"
- if ( opt.queue == null || opt.queue === true ) {
- opt.queue = "fx";
- }
-
- // Queueing
- opt.old = opt.complete;
-
- opt.complete = function() {
- if ( isFunction( opt.old ) ) {
- opt.old.call( this );
- }
-
- if ( opt.queue ) {
- jQuery.dequeue( this, opt.queue );
- }
- };
-
- return opt;
-};
-
-jQuery.fn.extend( {
- fadeTo: function( speed, to, easing, callback ) {
-
- // Show any hidden elements after setting opacity to 0
- return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
- // Animate to the value specified
- .end().animate( { opacity: to }, speed, easing, callback );
- },
- animate: function( prop, speed, easing, callback ) {
- var empty = jQuery.isEmptyObject( prop ),
- optall = jQuery.speed( speed, easing, callback ),
- doAnimation = function() {
-
- // Operate on a copy of prop so per-property easing won't be lost
- var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
- // Empty animations, or finishing resolves immediately
- if ( empty || dataPriv.get( this, "finish" ) ) {
- anim.stop( true );
- }
- };
-
- doAnimation.finish = doAnimation;
-
- return empty || optall.queue === false ?
- this.each( doAnimation ) :
- this.queue( optall.queue, doAnimation );
- },
- stop: function( type, clearQueue, gotoEnd ) {
- var stopQueue = function( hooks ) {
- var stop = hooks.stop;
- delete hooks.stop;
- stop( gotoEnd );
- };
-
- if ( typeof type !== "string" ) {
- gotoEnd = clearQueue;
- clearQueue = type;
- type = undefined;
- }
- if ( clearQueue ) {
- this.queue( type || "fx", [] );
- }
-
- return this.each( function() {
- var dequeue = true,
- index = type != null && type + "queueHooks",
- timers = jQuery.timers,
- data = dataPriv.get( this );
-
- if ( index ) {
- if ( data[ index ] && data[ index ].stop ) {
- stopQueue( data[ index ] );
- }
- } else {
- for ( index in data ) {
- if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
- stopQueue( data[ index ] );
- }
- }
- }
-
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this &&
- ( type == null || timers[ index ].queue === type ) ) {
-
- timers[ index ].anim.stop( gotoEnd );
- dequeue = false;
- timers.splice( index, 1 );
- }
- }
-
- // Start the next in the queue if the last step wasn't forced.
- // Timers currently will call their complete callbacks, which
- // will dequeue but only if they were gotoEnd.
- if ( dequeue || !gotoEnd ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- finish: function( type ) {
- if ( type !== false ) {
- type = type || "fx";
- }
- return this.each( function() {
- var index,
- data = dataPriv.get( this ),
- queue = data[ type + "queue" ],
- hooks = data[ type + "queueHooks" ],
- timers = jQuery.timers,
- length = queue ? queue.length : 0;
-
- // Enable finishing flag on private data
- data.finish = true;
-
- // Empty the queue first
- jQuery.queue( this, type, [] );
-
- if ( hooks && hooks.stop ) {
- hooks.stop.call( this, true );
- }
-
- // Look for any active animations, and finish them
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
- timers[ index ].anim.stop( true );
- timers.splice( index, 1 );
- }
- }
-
- // Look for any animations in the old queue and finish them
- for ( index = 0; index < length; index++ ) {
- if ( queue[ index ] && queue[ index ].finish ) {
- queue[ index ].finish.call( this );
- }
- }
-
- // Turn off finishing flag
- delete data.finish;
- } );
- }
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
- var cssFn = jQuery.fn[ name ];
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return speed == null || typeof speed === "boolean" ?
- cssFn.apply( this, arguments ) :
- this.animate( genFx( name, true ), speed, easing, callback );
- };
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
- slideDown: genFx( "show" ),
- slideUp: genFx( "hide" ),
- slideToggle: genFx( "toggle" ),
- fadeIn: { opacity: "show" },
- fadeOut: { opacity: "hide" },
- fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return this.animate( props, speed, easing, callback );
- };
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
- var timer,
- i = 0,
- timers = jQuery.timers;
-
- fxNow = Date.now();
-
- for ( ; i < timers.length; i++ ) {
- timer = timers[ i ];
-
- // Run the timer and safely remove it when done (allowing for external removal)
- if ( !timer() && timers[ i ] === timer ) {
- timers.splice( i--, 1 );
- }
- }
-
- if ( !timers.length ) {
- jQuery.fx.stop();
- }
- fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
- jQuery.timers.push( timer );
- jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
- if ( inProgress ) {
- return;
- }
-
- inProgress = true;
- schedule();
-};
-
-jQuery.fx.stop = function() {
- inProgress = null;
-};
-
-jQuery.fx.speeds = {
- slow: 600,
- fast: 200,
-
- // Default speed
- _default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
- time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
- type = type || "fx";
-
- return this.queue( type, function( next, hooks ) {
- var timeout = window.setTimeout( next, time );
- hooks.stop = function() {
- window.clearTimeout( timeout );
- };
- } );
-};
-
-
-( function() {
- var input = document.createElement( "input" ),
- select = document.createElement( "select" ),
- opt = select.appendChild( document.createElement( "option" ) );
-
- input.type = "checkbox";
-
- // Support: Android <=4.3 only
- // Default value for a checkbox should be "on"
- support.checkOn = input.value !== "";
-
- // Support: IE <=11 only
- // Must access selectedIndex to make default options select
- support.optSelected = opt.selected;
-
- // Support: IE <=11 only
- // An input loses its value after becoming a radio
- input = document.createElement( "input" );
- input.value = "t";
- input.type = "radio";
- support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
- attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
- attr: function( name, value ) {
- return access( this, jQuery.attr, name, value, arguments.length > 1 );
- },
-
- removeAttr: function( name ) {
- return this.each( function() {
- jQuery.removeAttr( this, name );
- } );
- }
-} );
-
-jQuery.extend( {
- attr: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set attributes on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- // Fallback to prop when attributes are not supported
- if ( typeof elem.getAttribute === "undefined" ) {
- return jQuery.prop( elem, name, value );
- }
-
- // Attribute hooks are determined by the lowercase version
- // Grab necessary hook if one is defined
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
- hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
- ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
- }
-
- if ( value !== undefined ) {
- if ( value === null ) {
- jQuery.removeAttr( elem, name );
- return;
- }
-
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- elem.setAttribute( name, value + "" );
- return value;
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- ret = jQuery.find.attr( elem, name );
-
- // Non-existent attributes return null, we normalize to undefined
- return ret == null ? undefined : ret;
- },
-
- attrHooks: {
- type: {
- set: function( elem, value ) {
- if ( !support.radioValue && value === "radio" &&
- nodeName( elem, "input" ) ) {
- var val = elem.value;
- elem.setAttribute( "type", value );
- if ( val ) {
- elem.value = val;
- }
- return value;
- }
- }
- }
- },
-
- removeAttr: function( elem, value ) {
- var name,
- i = 0,
-
- // Attribute names can contain non-HTML whitespace characters
- // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
- attrNames = value && value.match( rnothtmlwhite );
-
- if ( attrNames && elem.nodeType === 1 ) {
- while ( ( name = attrNames[ i++ ] ) ) {
- elem.removeAttribute( name );
- }
- }
- }
-} );
-
-// Hooks for boolean attributes
-boolHook = {
- set: function( elem, value, name ) {
- if ( value === false ) {
-
- // Remove boolean attributes when set to false
- jQuery.removeAttr( elem, name );
- } else {
- elem.setAttribute( name, name );
- }
- return name;
- }
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
- var getter = attrHandle[ name ] || jQuery.find.attr;
-
- attrHandle[ name ] = function( elem, name, isXML ) {
- var ret, handle,
- lowercaseName = name.toLowerCase();
-
- if ( !isXML ) {
-
- // Avoid an infinite loop by temporarily removing this function from the getter
- handle = attrHandle[ lowercaseName ];
- attrHandle[ lowercaseName ] = ret;
- ret = getter( elem, name, isXML ) != null ?
- lowercaseName :
- null;
- attrHandle[ lowercaseName ] = handle;
- }
- return ret;
- };
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
- rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
- prop: function( name, value ) {
- return access( this, jQuery.prop, name, value, arguments.length > 1 );
- },
-
- removeProp: function( name ) {
- return this.each( function() {
- delete this[ jQuery.propFix[ name ] || name ];
- } );
- }
-} );
-
-jQuery.extend( {
- prop: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set properties on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
- // Fix name and attach hooks
- name = jQuery.propFix[ name ] || name;
- hooks = jQuery.propHooks[ name ];
- }
-
- if ( value !== undefined ) {
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- return ( elem[ name ] = value );
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- return elem[ name ];
- },
-
- propHooks: {
- tabIndex: {
- get: function( elem ) {
-
- // Support: IE <=9 - 11 only
- // elem.tabIndex doesn't always return the
- // correct value when it hasn't been explicitly set
- // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
- // Use proper attribute retrieval(#12072)
- var tabindex = jQuery.find.attr( elem, "tabindex" );
-
- if ( tabindex ) {
- return parseInt( tabindex, 10 );
- }
-
- if (
- rfocusable.test( elem.nodeName ) ||
- rclickable.test( elem.nodeName ) &&
- elem.href
- ) {
- return 0;
- }
-
- return -1;
- }
- }
- },
-
- propFix: {
- "for": "htmlFor",
- "class": "className"
- }
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
- jQuery.propHooks.selected = {
- get: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent && parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- return null;
- },
- set: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent ) {
- parent.selectedIndex;
-
- if ( parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- }
- }
- };
-}
-
-jQuery.each( [
- "tabIndex",
- "readOnly",
- "maxLength",
- "cellSpacing",
- "cellPadding",
- "rowSpan",
- "colSpan",
- "useMap",
- "frameBorder",
- "contentEditable"
-], function() {
- jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
- // Strip and collapse whitespace according to HTML spec
- // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
- function stripAndCollapse( value ) {
- var tokens = value.match( rnothtmlwhite ) || [];
- return tokens.join( " " );
- }
-
-
-function getClass( elem ) {
- return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
- if ( Array.isArray( value ) ) {
- return value;
- }
- if ( typeof value === "string" ) {
- return value.match( rnothtmlwhite ) || [];
- }
- return [];
-}
-
-jQuery.fn.extend( {
- addClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
- if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
- cur += clazz + " ";
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- removeClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- if ( !arguments.length ) {
- return this.attr( "class", "" );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
-
- // This expression is here for better compressibility (see addClass)
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
-
- // Remove *all* instances
- while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
- cur = cur.replace( " " + clazz + " ", " " );
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- toggleClass: function( value, stateVal ) {
- var type = typeof value,
- isValidValue = type === "string" || Array.isArray( value );
-
- if ( typeof stateVal === "boolean" && isValidValue ) {
- return stateVal ? this.addClass( value ) : this.removeClass( value );
- }
-
- if ( isFunction( value ) ) {
- return this.each( function( i ) {
- jQuery( this ).toggleClass(
- value.call( this, i, getClass( this ), stateVal ),
- stateVal
- );
- } );
- }
-
- return this.each( function() {
- var className, i, self, classNames;
-
- if ( isValidValue ) {
-
- // Toggle individual class names
- i = 0;
- self = jQuery( this );
- classNames = classesToArray( value );
-
- while ( ( className = classNames[ i++ ] ) ) {
-
- // Check each className given, space separated list
- if ( self.hasClass( className ) ) {
- self.removeClass( className );
- } else {
- self.addClass( className );
- }
- }
-
- // Toggle whole class name
- } else if ( value === undefined || type === "boolean" ) {
- className = getClass( this );
- if ( className ) {
-
- // Store className if set
- dataPriv.set( this, "__className__", className );
- }
-
- // If the element has a class name or if we're passed `false`,
- // then remove the whole classname (if there was one, the above saved it).
- // Otherwise bring back whatever was previously saved (if anything),
- // falling back to the empty string if nothing was stored.
- if ( this.setAttribute ) {
- this.setAttribute( "class",
- className || value === false ?
- "" :
- dataPriv.get( this, "__className__" ) || ""
- );
- }
- }
- } );
- },
-
- hasClass: function( selector ) {
- var className, elem,
- i = 0;
-
- className = " " + selector + " ";
- while ( ( elem = this[ i++ ] ) ) {
- if ( elem.nodeType === 1 &&
- ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
- return true;
- }
- }
-
- return false;
- }
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
- val: function( value ) {
- var hooks, ret, valueIsFunction,
- elem = this[ 0 ];
-
- if ( !arguments.length ) {
- if ( elem ) {
- hooks = jQuery.valHooks[ elem.type ] ||
- jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
- if ( hooks &&
- "get" in hooks &&
- ( ret = hooks.get( elem, "value" ) ) !== undefined
- ) {
- return ret;
- }
-
- ret = elem.value;
-
- // Handle most common string cases
- if ( typeof ret === "string" ) {
- return ret.replace( rreturn, "" );
- }
-
- // Handle cases where value is null/undef or number
- return ret == null ? "" : ret;
- }
-
- return;
- }
-
- valueIsFunction = isFunction( value );
-
- return this.each( function( i ) {
- var val;
-
- if ( this.nodeType !== 1 ) {
- return;
- }
-
- if ( valueIsFunction ) {
- val = value.call( this, i, jQuery( this ).val() );
- } else {
- val = value;
- }
-
- // Treat null/undefined as ""; convert numbers to string
- if ( val == null ) {
- val = "";
-
- } else if ( typeof val === "number" ) {
- val += "";
-
- } else if ( Array.isArray( val ) ) {
- val = jQuery.map( val, function( value ) {
- return value == null ? "" : value + "";
- } );
- }
-
- hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
- // If set returns undefined, fall back to normal setting
- if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
- this.value = val;
- }
- } );
- }
-} );
-
-jQuery.extend( {
- valHooks: {
- option: {
- get: function( elem ) {
-
- var val = jQuery.find.attr( elem, "value" );
- return val != null ?
- val :
-
- // Support: IE <=10 - 11 only
- // option.text throws exceptions (#14686, #14858)
- // Strip and collapse whitespace
- // https://html.spec.whatwg.org/#strip-and-collapse-whitespace
- stripAndCollapse( jQuery.text( elem ) );
- }
- },
- select: {
- get: function( elem ) {
- var value, option, i,
- options = elem.options,
- index = elem.selectedIndex,
- one = elem.type === "select-one",
- values = one ? null : [],
- max = one ? index + 1 : options.length;
-
- if ( index < 0 ) {
- i = max;
-
- } else {
- i = one ? index : 0;
- }
-
- // Loop through all the selected options
- for ( ; i < max; i++ ) {
- option = options[ i ];
-
- // Support: IE <=9 only
- // IE8-9 doesn't update selected after form reset (#2551)
- if ( ( option.selected || i === index ) &&
-
- // Don't return options that are disabled or in a disabled optgroup
- !option.disabled &&
- ( !option.parentNode.disabled ||
- !nodeName( option.parentNode, "optgroup" ) ) ) {
-
- // Get the specific value for the option
- value = jQuery( option ).val();
-
- // We don't need an array for one selects
- if ( one ) {
- return value;
- }
-
- // Multi-Selects return an array
- values.push( value );
- }
- }
-
- return values;
- },
-
- set: function( elem, value ) {
- var optionSet, option,
- options = elem.options,
- values = jQuery.makeArray( value ),
- i = options.length;
-
- while ( i-- ) {
- option = options[ i ];
-
- /* eslint-disable no-cond-assign */
-
- if ( option.selected =
- jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
- ) {
- optionSet = true;
- }
-
- /* eslint-enable no-cond-assign */
- }
-
- // Force browsers to behave consistently when non-matching value is set
- if ( !optionSet ) {
- elem.selectedIndex = -1;
- }
- return values;
- }
- }
- }
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
- jQuery.valHooks[ this ] = {
- set: function( elem, value ) {
- if ( Array.isArray( value ) ) {
- return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
- }
- }
- };
- if ( !support.checkOn ) {
- jQuery.valHooks[ this ].get = function( elem ) {
- return elem.getAttribute( "value" ) === null ? "on" : elem.value;
- };
- }
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
- stopPropagationCallback = function( e ) {
- e.stopPropagation();
- };
-
-jQuery.extend( jQuery.event, {
-
- trigger: function( event, data, elem, onlyHandlers ) {
-
- var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
- eventPath = [ elem || document ],
- type = hasOwn.call( event, "type" ) ? event.type : event,
- namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
- cur = lastElement = tmp = elem = elem || document;
-
- // Don't do events on text and comment nodes
- if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
- return;
- }
-
- // focus/blur morphs to focusin/out; ensure we're not firing them right now
- if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
- return;
- }
-
- if ( type.indexOf( "." ) > -1 ) {
-
- // Namespaced trigger; create a regexp to match event type in handle()
- namespaces = type.split( "." );
- type = namespaces.shift();
- namespaces.sort();
- }
- ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
- // Caller can pass in a jQuery.Event object, Object, or just an event type string
- event = event[ jQuery.expando ] ?
- event :
- new jQuery.Event( type, typeof event === "object" && event );
-
- // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
- event.isTrigger = onlyHandlers ? 2 : 3;
- event.namespace = namespaces.join( "." );
- event.rnamespace = event.namespace ?
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
- null;
-
- // Clean up the event in case it is being reused
- event.result = undefined;
- if ( !event.target ) {
- event.target = elem;
- }
-
- // Clone any incoming data and prepend the event, creating the handler arg list
- data = data == null ?
- [ event ] :
- jQuery.makeArray( data, [ event ] );
-
- // Allow special events to draw outside the lines
- special = jQuery.event.special[ type ] || {};
- if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
- return;
- }
-
- // Determine event propagation path in advance, per W3C events spec (#9951)
- // Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
- if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
- bubbleType = special.delegateType || type;
- if ( !rfocusMorph.test( bubbleType + type ) ) {
- cur = cur.parentNode;
- }
- for ( ; cur; cur = cur.parentNode ) {
- eventPath.push( cur );
- tmp = cur;
- }
-
- // Only add window if we got to document (e.g., not plain obj or detached DOM)
- if ( tmp === ( elem.ownerDocument || document ) ) {
- eventPath.push( tmp.defaultView || tmp.parentWindow || window );
- }
- }
-
- // Fire handlers on the event path
- i = 0;
- while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
- lastElement = cur;
- event.type = i > 1 ?
- bubbleType :
- special.bindType || type;
-
- // jQuery handler
- handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
- dataPriv.get( cur, "handle" );
- if ( handle ) {
- handle.apply( cur, data );
- }
-
- // Native handler
- handle = ontype && cur[ ontype ];
- if ( handle && handle.apply && acceptData( cur ) ) {
- event.result = handle.apply( cur, data );
- if ( event.result === false ) {
- event.preventDefault();
- }
- }
- }
- event.type = type;
-
- // If nobody prevented the default action, do it now
- if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
- if ( ( !special._default ||
- special._default.apply( eventPath.pop(), data ) === false ) &&
- acceptData( elem ) ) {
-
- // Call a native DOM method on the target with the same name as the event.
- // Don't do default actions on window, that's where global variables be (#6170)
- if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
- // Don't re-trigger an onFOO event when we call its FOO() method
- tmp = elem[ ontype ];
-
- if ( tmp ) {
- elem[ ontype ] = null;
- }
-
- // Prevent re-triggering of the same event, since we already bubbled it above
- jQuery.event.triggered = type;
-
- if ( event.isPropagationStopped() ) {
- lastElement.addEventListener( type, stopPropagationCallback );
- }
-
- elem[ type ]();
-
- if ( event.isPropagationStopped() ) {
- lastElement.removeEventListener( type, stopPropagationCallback );
- }
-
- jQuery.event.triggered = undefined;
-
- if ( tmp ) {
- elem[ ontype ] = tmp;
- }
- }
- }
- }
-
- return event.result;
- },
-
- // Piggyback on a donor event to simulate a different one
- // Used only for `focus(in | out)` events
- simulate: function( type, elem, event ) {
- var e = jQuery.extend(
- new jQuery.Event(),
- event,
- {
- type: type,
- isSimulated: true
- }
- );
-
- jQuery.event.trigger( e, null, elem );
- }
-
-} );
-
-jQuery.fn.extend( {
-
- trigger: function( type, data ) {
- return this.each( function() {
- jQuery.event.trigger( type, data, this );
- } );
- },
- triggerHandler: function( type, data ) {
- var elem = this[ 0 ];
- if ( elem ) {
- return jQuery.event.trigger( type, data, elem, true );
- }
- }
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
- jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
- // Attach a single capturing handler on the document while someone wants focusin/focusout
- var handler = function( event ) {
- jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
- };
-
- jQuery.event.special[ fix ] = {
- setup: function() {
-
- // Handle: regular nodes (via `this.ownerDocument`), window
- // (via `this.document`) & document (via `this`).
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix );
-
- if ( !attaches ) {
- doc.addEventListener( orig, handler, true );
- }
- dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
- },
- teardown: function() {
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix ) - 1;
-
- if ( !attaches ) {
- doc.removeEventListener( orig, handler, true );
- dataPriv.remove( doc, fix );
-
- } else {
- dataPriv.access( doc, fix, attaches );
- }
- }
- };
- } );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
- var xml, parserErrorElem;
- if ( !data || typeof data !== "string" ) {
- return null;
- }
-
- // Support: IE 9 - 11 only
- // IE throws on parseFromString with invalid input.
- try {
- xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
- } catch ( e ) {}
-
- parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
- if ( !xml || parserErrorElem ) {
- jQuery.error( "Invalid XML: " + (
- parserErrorElem ?
- jQuery.map( parserErrorElem.childNodes, function( el ) {
- return el.textContent;
- } ).join( "\n" ) :
- data
- ) );
- }
- return xml;
-};
-
-
-var
- rbracket = /\[\]$/,
- rCRLF = /\r?\n/g,
- rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
- rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
- var name;
-
- if ( Array.isArray( obj ) ) {
-
- // Serialize array item.
- jQuery.each( obj, function( i, v ) {
- if ( traditional || rbracket.test( prefix ) ) {
-
- // Treat each array item as a scalar.
- add( prefix, v );
-
- } else {
-
- // Item is non-scalar (array or object), encode its numeric index.
- buildParams(
- prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
- v,
- traditional,
- add
- );
- }
- } );
-
- } else if ( !traditional && toType( obj ) === "object" ) {
-
- // Serialize object item.
- for ( name in obj ) {
- buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
- }
-
- } else {
-
- // Serialize scalar item.
- add( prefix, obj );
- }
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
- var prefix,
- s = [],
- add = function( key, valueOrFunction ) {
-
- // If value is a function, invoke it and use its return value
- var value = isFunction( valueOrFunction ) ?
- valueOrFunction() :
- valueOrFunction;
-
- s[ s.length ] = encodeURIComponent( key ) + "=" +
- encodeURIComponent( value == null ? "" : value );
- };
-
- if ( a == null ) {
- return "";
- }
-
- // If an array was passed in, assume that it is an array of form elements.
- if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
- // Serialize the form elements
- jQuery.each( a, function() {
- add( this.name, this.value );
- } );
-
- } else {
-
- // If traditional, encode the "old" way (the way 1.3.2 or older
- // did it), otherwise encode params recursively.
- for ( prefix in a ) {
- buildParams( prefix, a[ prefix ], traditional, add );
- }
- }
-
- // Return the resulting serialization
- return s.join( "&" );
-};
-
-jQuery.fn.extend( {
- serialize: function() {
- return jQuery.param( this.serializeArray() );
- },
- serializeArray: function() {
- return this.map( function() {
-
- // Can add propHook for "elements" to filter or add form elements
- var elements = jQuery.prop( this, "elements" );
- return elements ? jQuery.makeArray( elements ) : this;
- } ).filter( function() {
- var type = this.type;
-
- // Use .is( ":disabled" ) so that fieldset[disabled] works
- return this.name && !jQuery( this ).is( ":disabled" ) &&
- rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
- ( this.checked || !rcheckableType.test( type ) );
- } ).map( function( _i, elem ) {
- var val = jQuery( this ).val();
-
- if ( val == null ) {
- return null;
- }
-
- if ( Array.isArray( val ) ) {
- return jQuery.map( val, function( val ) {
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } );
- }
-
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } ).get();
- }
-} );
-
-
-var
- r20 = /%20/g,
- rhash = /#.*$/,
- rantiCache = /([?&])_=[^&]*/,
- rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
- // #7653, #8125, #8152: local protocol detection
- rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
- rnoContent = /^(?:GET|HEAD)$/,
- rprotocol = /^\/\//,
-
- /* Prefilters
- * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
- * 2) These are called:
- * - BEFORE asking for a transport
- * - AFTER param serialization (s.data is a string if s.processData is true)
- * 3) key is the dataType
- * 4) the catchall symbol "*" can be used
- * 5) execution will start with transport dataType and THEN continue down to "*" if needed
- */
- prefilters = {},
-
- /* Transports bindings
- * 1) key is the dataType
- * 2) the catchall symbol "*" can be used
- * 3) selection will start with transport dataType and THEN go to "*" if needed
- */
- transports = {},
-
- // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
- allTypes = "*/".concat( "*" ),
-
- // Anchor tag for parsing the document origin
- originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
- // dataTypeExpression is optional and defaults to "*"
- return function( dataTypeExpression, func ) {
-
- if ( typeof dataTypeExpression !== "string" ) {
- func = dataTypeExpression;
- dataTypeExpression = "*";
- }
-
- var dataType,
- i = 0,
- dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
- if ( isFunction( func ) ) {
-
- // For each dataType in the dataTypeExpression
- while ( ( dataType = dataTypes[ i++ ] ) ) {
-
- // Prepend if requested
- if ( dataType[ 0 ] === "+" ) {
- dataType = dataType.slice( 1 ) || "*";
- ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
- // Otherwise append
- } else {
- ( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
- }
- }
- }
- };
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
- var inspected = {},
- seekingTransport = ( structure === transports );
-
- function inspect( dataType ) {
- var selected;
- inspected[ dataType ] = true;
- jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
- var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
- if ( typeof dataTypeOrTransport === "string" &&
- !seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
- options.dataTypes.unshift( dataTypeOrTransport );
- inspect( dataTypeOrTransport );
- return false;
- } else if ( seekingTransport ) {
- return !( selected = dataTypeOrTransport );
- }
- } );
- return selected;
- }
-
- return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
- var key, deep,
- flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
- for ( key in src ) {
- if ( src[ key ] !== undefined ) {
- ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
- }
- }
- if ( deep ) {
- jQuery.extend( true, target, deep );
- }
-
- return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
- var ct, type, finalDataType, firstDataType,
- contents = s.contents,
- dataTypes = s.dataTypes;
-
- // Remove auto dataType and get content-type in the process
- while ( dataTypes[ 0 ] === "*" ) {
- dataTypes.shift();
- if ( ct === undefined ) {
- ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
- }
- }
-
- // Check if we're dealing with a known content-type
- if ( ct ) {
- for ( type in contents ) {
- if ( contents[ type ] && contents[ type ].test( ct ) ) {
- dataTypes.unshift( type );
- break;
- }
- }
- }
-
- // Check to see if we have a response for the expected dataType
- if ( dataTypes[ 0 ] in responses ) {
- finalDataType = dataTypes[ 0 ];
- } else {
-
- // Try convertible dataTypes
- for ( type in responses ) {
- if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
- finalDataType = type;
- break;
- }
- if ( !firstDataType ) {
- firstDataType = type;
- }
- }
-
- // Or just use first one
- finalDataType = finalDataType || firstDataType;
- }
-
- // If we found a dataType
- // We add the dataType to the list if needed
- // and return the corresponding response
- if ( finalDataType ) {
- if ( finalDataType !== dataTypes[ 0 ] ) {
- dataTypes.unshift( finalDataType );
- }
- return responses[ finalDataType ];
- }
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
- var conv2, current, conv, tmp, prev,
- converters = {},
-
- // Work with a copy of dataTypes in case we need to modify it for conversion
- dataTypes = s.dataTypes.slice();
-
- // Create converters map with lowercased keys
- if ( dataTypes[ 1 ] ) {
- for ( conv in s.converters ) {
- converters[ conv.toLowerCase() ] = s.converters[ conv ];
- }
- }
-
- current = dataTypes.shift();
-
- // Convert to each sequential dataType
- while ( current ) {
-
- if ( s.responseFields[ current ] ) {
- jqXHR[ s.responseFields[ current ] ] = response;
- }
-
- // Apply the dataFilter if provided
- if ( !prev && isSuccess && s.dataFilter ) {
- response = s.dataFilter( response, s.dataType );
- }
-
- prev = current;
- current = dataTypes.shift();
-
- if ( current ) {
-
- // There's only work to do if current dataType is non-auto
- if ( current === "*" ) {
-
- current = prev;
-
- // Convert response if prev dataType is non-auto and differs from current
- } else if ( prev !== "*" && prev !== current ) {
-
- // Seek a direct converter
- conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
- // If none found, seek a pair
- if ( !conv ) {
- for ( conv2 in converters ) {
-
- // If conv2 outputs current
- tmp = conv2.split( " " );
- if ( tmp[ 1 ] === current ) {
-
- // If prev can be converted to accepted input
- conv = converters[ prev + " " + tmp[ 0 ] ] ||
- converters[ "* " + tmp[ 0 ] ];
- if ( conv ) {
-
- // Condense equivalence converters
- if ( conv === true ) {
- conv = converters[ conv2 ];
-
- // Otherwise, insert the intermediate dataType
- } else if ( converters[ conv2 ] !== true ) {
- current = tmp[ 0 ];
- dataTypes.unshift( tmp[ 1 ] );
- }
- break;
- }
- }
- }
- }
-
- // Apply converter (if not an equivalence)
- if ( conv !== true ) {
-
- // Unless errors are allowed to bubble, catch and return them
- if ( conv && s.throws ) {
- response = conv( response );
- } else {
- try {
- response = conv( response );
- } catch ( e ) {
- return {
- state: "parsererror",
- error: conv ? e : "No conversion from " + prev + " to " + current
- };
- }
- }
- }
- }
- }
- }
-
- return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
- // Counter for holding the number of active queries
- active: 0,
-
- // Last-Modified header cache for next request
- lastModified: {},
- etag: {},
-
- ajaxSettings: {
- url: location.href,
- type: "GET",
- isLocal: rlocalProtocol.test( location.protocol ),
- global: true,
- processData: true,
- async: true,
- contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
- /*
- timeout: 0,
- data: null,
- dataType: null,
- username: null,
- password: null,
- cache: null,
- throws: false,
- traditional: false,
- headers: {},
- */
-
- accepts: {
- "*": allTypes,
- text: "text/plain",
- html: "text/html",
- xml: "application/xml, text/xml",
- json: "application/json, text/javascript"
- },
-
- contents: {
- xml: /\bxml\b/,
- html: /\bhtml/,
- json: /\bjson\b/
- },
-
- responseFields: {
- xml: "responseXML",
- text: "responseText",
- json: "responseJSON"
- },
-
- // Data converters
- // Keys separate source (or catchall "*") and destination types with a single space
- converters: {
-
- // Convert anything to text
- "* text": String,
-
- // Text to html (true = no transformation)
- "text html": true,
-
- // Evaluate text as a json expression
- "text json": JSON.parse,
-
- // Parse text as xml
- "text xml": jQuery.parseXML
- },
-
- // For options that shouldn't be deep extended:
- // you can add your own custom options here if
- // and when you create one that shouldn't be
- // deep extended (see ajaxExtend)
- flatOptions: {
- url: true,
- context: true
- }
- },
-
- // Creates a full fledged settings object into target
- // with both ajaxSettings and settings fields.
- // If target is omitted, writes into ajaxSettings.
- ajaxSetup: function( target, settings ) {
- return settings ?
-
- // Building a settings object
- ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
- // Extending ajaxSettings
- ajaxExtend( jQuery.ajaxSettings, target );
- },
-
- ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
- ajaxTransport: addToPrefiltersOrTransports( transports ),
-
- // Main method
- ajax: function( url, options ) {
-
- // If url is an object, simulate pre-1.5 signature
- if ( typeof url === "object" ) {
- options = url;
- url = undefined;
- }
-
- // Force options to be an object
- options = options || {};
-
- var transport,
-
- // URL without anti-cache param
- cacheURL,
-
- // Response headers
- responseHeadersString,
- responseHeaders,
-
- // timeout handle
- timeoutTimer,
-
- // Url cleanup var
- urlAnchor,
-
- // Request state (becomes false upon send and true upon completion)
- completed,
-
- // To know if global events are to be dispatched
- fireGlobals,
-
- // Loop variable
- i,
-
- // uncached part of the url
- uncached,
-
- // Create the final options object
- s = jQuery.ajaxSetup( {}, options ),
-
- // Callbacks context
- callbackContext = s.context || s,
-
- // Context for global events is callbackContext if it is a DOM node or jQuery collection
- globalEventContext = s.context &&
- ( callbackContext.nodeType || callbackContext.jquery ) ?
- jQuery( callbackContext ) :
- jQuery.event,
-
- // Deferreds
- deferred = jQuery.Deferred(),
- completeDeferred = jQuery.Callbacks( "once memory" ),
-
- // Status-dependent callbacks
- statusCode = s.statusCode || {},
-
- // Headers (they are sent all at once)
- requestHeaders = {},
- requestHeadersNames = {},
-
- // Default abort message
- strAbort = "canceled",
-
- // Fake xhr
- jqXHR = {
- readyState: 0,
-
- // Builds headers hashtable if needed
- getResponseHeader: function( key ) {
- var match;
- if ( completed ) {
- if ( !responseHeaders ) {
- responseHeaders = {};
- while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
- responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
- ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
- .concat( match[ 2 ] );
- }
- }
- match = responseHeaders[ key.toLowerCase() + " " ];
- }
- return match == null ? null : match.join( ", " );
- },
-
- // Raw string
- getAllResponseHeaders: function() {
- return completed ? responseHeadersString : null;
- },
-
- // Caches the header
- setRequestHeader: function( name, value ) {
- if ( completed == null ) {
- name = requestHeadersNames[ name.toLowerCase() ] =
- requestHeadersNames[ name.toLowerCase() ] || name;
- requestHeaders[ name ] = value;
- }
- return this;
- },
-
- // Overrides response content-type header
- overrideMimeType: function( type ) {
- if ( completed == null ) {
- s.mimeType = type;
- }
- return this;
- },
-
- // Status-dependent callbacks
- statusCode: function( map ) {
- var code;
- if ( map ) {
- if ( completed ) {
-
- // Execute the appropriate callbacks
- jqXHR.always( map[ jqXHR.status ] );
- } else {
-
- // Lazy-add the new callbacks in a way that preserves old ones
- for ( code in map ) {
- statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
- }
- }
- }
- return this;
- },
-
- // Cancel the request
- abort: function( statusText ) {
- var finalText = statusText || strAbort;
- if ( transport ) {
- transport.abort( finalText );
- }
- done( 0, finalText );
- return this;
- }
- };
-
- // Attach deferreds
- deferred.promise( jqXHR );
-
- // Add protocol if not provided (prefilters might expect it)
- // Handle falsy url in the settings object (#10093: consistency with old signature)
- // We also use the url parameter if available
- s.url = ( ( url || s.url || location.href ) + "" )
- .replace( rprotocol, location.protocol + "//" );
-
- // Alias method option to type as per ticket #12004
- s.type = options.method || options.type || s.method || s.type;
-
- // Extract dataTypes list
- s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
- // A cross-domain request is in order when the origin doesn't match the current origin.
- if ( s.crossDomain == null ) {
- urlAnchor = document.createElement( "a" );
-
- // Support: IE <=8 - 11, Edge 12 - 15
- // IE throws exception on accessing the href property if url is malformed,
- // e.g. http://example.com:80x/
- try {
- urlAnchor.href = s.url;
-
- // Support: IE <=8 - 11 only
- // Anchor's host property isn't correctly set when s.url is relative
- urlAnchor.href = urlAnchor.href;
- s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
- urlAnchor.protocol + "//" + urlAnchor.host;
- } catch ( e ) {
-
- // If there is an error parsing the URL, assume it is crossDomain,
- // it can be rejected by the transport if it is invalid
- s.crossDomain = true;
- }
- }
-
- // Convert data if not already a string
- if ( s.data && s.processData && typeof s.data !== "string" ) {
- s.data = jQuery.param( s.data, s.traditional );
- }
-
- // Apply prefilters
- inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
- // If request was aborted inside a prefilter, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // We can fire global events as of now if asked to
- // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
- fireGlobals = jQuery.event && s.global;
-
- // Watch for a new set of requests
- if ( fireGlobals && jQuery.active++ === 0 ) {
- jQuery.event.trigger( "ajaxStart" );
- }
-
- // Uppercase the type
- s.type = s.type.toUpperCase();
-
- // Determine if request has content
- s.hasContent = !rnoContent.test( s.type );
-
- // Save the URL in case we're toying with the If-Modified-Since
- // and/or If-None-Match header later on
- // Remove hash to simplify url manipulation
- cacheURL = s.url.replace( rhash, "" );
-
- // More options handling for requests with no content
- if ( !s.hasContent ) {
-
- // Remember the hash so we can put it back
- uncached = s.url.slice( cacheURL.length );
-
- // If data is available and should be processed, append data to url
- if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
- cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
- // #9682: remove data so that it's not used in an eventual retry
- delete s.data;
- }
-
- // Add or update anti-cache param if needed
- if ( s.cache === false ) {
- cacheURL = cacheURL.replace( rantiCache, "$1" );
- uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
- uncached;
- }
-
- // Put hash and anti-cache on the URL that will be requested (gh-1732)
- s.url = cacheURL + uncached;
-
- // Change '%20' to '+' if this is encoded form body content (gh-2658)
- } else if ( s.data && s.processData &&
- ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
- s.data = s.data.replace( r20, "+" );
- }
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- if ( jQuery.lastModified[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
- }
- if ( jQuery.etag[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
- }
- }
-
- // Set the correct header, if data is being sent
- if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
- jqXHR.setRequestHeader( "Content-Type", s.contentType );
- }
-
- // Set the Accepts header for the server, depending on the dataType
- jqXHR.setRequestHeader(
- "Accept",
- s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
- s.accepts[ s.dataTypes[ 0 ] ] +
- ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
- s.accepts[ "*" ]
- );
-
- // Check for headers option
- for ( i in s.headers ) {
- jqXHR.setRequestHeader( i, s.headers[ i ] );
- }
-
- // Allow custom headers/mimetypes and early abort
- if ( s.beforeSend &&
- ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
- // Abort if not done already and return
- return jqXHR.abort();
- }
-
- // Aborting is no longer a cancellation
- strAbort = "abort";
-
- // Install callbacks on deferreds
- completeDeferred.add( s.complete );
- jqXHR.done( s.success );
- jqXHR.fail( s.error );
-
- // Get transport
- transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
- // If no transport, we auto-abort
- if ( !transport ) {
- done( -1, "No Transport" );
- } else {
- jqXHR.readyState = 1;
-
- // Send global event
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
- }
-
- // If request was aborted inside ajaxSend, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // Timeout
- if ( s.async && s.timeout > 0 ) {
- timeoutTimer = window.setTimeout( function() {
- jqXHR.abort( "timeout" );
- }, s.timeout );
- }
-
- try {
- completed = false;
- transport.send( requestHeaders, done );
- } catch ( e ) {
-
- // Rethrow post-completion exceptions
- if ( completed ) {
- throw e;
- }
-
- // Propagate others as results
- done( -1, e );
- }
- }
-
- // Callback for when everything is done
- function done( status, nativeStatusText, responses, headers ) {
- var isSuccess, success, error, response, modified,
- statusText = nativeStatusText;
-
- // Ignore repeat invocations
- if ( completed ) {
- return;
- }
-
- completed = true;
-
- // Clear timeout if it exists
- if ( timeoutTimer ) {
- window.clearTimeout( timeoutTimer );
- }
-
- // Dereference transport for early garbage collection
- // (no matter how long the jqXHR object will be used)
- transport = undefined;
-
- // Cache response headers
- responseHeadersString = headers || "";
-
- // Set readyState
- jqXHR.readyState = status > 0 ? 4 : 0;
-
- // Determine if successful
- isSuccess = status >= 200 && status < 300 || status === 304;
-
- // Get response data
- if ( responses ) {
- response = ajaxHandleResponses( s, jqXHR, responses );
- }
-
- // Use a noop converter for missing script but not if jsonp
- if ( !isSuccess &&
- jQuery.inArray( "script", s.dataTypes ) > -1 &&
- jQuery.inArray( "json", s.dataTypes ) < 0 ) {
- s.converters[ "text script" ] = function() {};
- }
-
- // Convert no matter what (that way responseXXX fields are always set)
- response = ajaxConvert( s, response, jqXHR, isSuccess );
-
- // If successful, handle type chaining
- if ( isSuccess ) {
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- modified = jqXHR.getResponseHeader( "Last-Modified" );
- if ( modified ) {
- jQuery.lastModified[ cacheURL ] = modified;
- }
- modified = jqXHR.getResponseHeader( "etag" );
- if ( modified ) {
- jQuery.etag[ cacheURL ] = modified;
- }
- }
-
- // if no content
- if ( status === 204 || s.type === "HEAD" ) {
- statusText = "nocontent";
-
- // if not modified
- } else if ( status === 304 ) {
- statusText = "notmodified";
-
- // If we have data, let's convert it
- } else {
- statusText = response.state;
- success = response.data;
- error = response.error;
- isSuccess = !error;
- }
- } else {
-
- // Extract error from statusText and normalize for non-aborts
- error = statusText;
- if ( status || !statusText ) {
- statusText = "error";
- if ( status < 0 ) {
- status = 0;
- }
- }
- }
-
- // Set data for the fake xhr object
- jqXHR.status = status;
- jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
- // Success/Error
- if ( isSuccess ) {
- deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
- } else {
- deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
- }
-
- // Status-dependent callbacks
- jqXHR.statusCode( statusCode );
- statusCode = undefined;
-
- if ( fireGlobals ) {
- globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
- [ jqXHR, s, isSuccess ? success : error ] );
- }
-
- // Complete
- completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
- // Handle the global AJAX counter
- if ( !( --jQuery.active ) ) {
- jQuery.event.trigger( "ajaxStop" );
- }
- }
- }
-
- return jqXHR;
- },
-
- getJSON: function( url, data, callback ) {
- return jQuery.get( url, data, callback, "json" );
- },
-
- getScript: function( url, callback ) {
- return jQuery.get( url, undefined, callback, "script" );
- }
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
- jQuery[ method ] = function( url, data, callback, type ) {
-
- // Shift arguments if data argument was omitted
- if ( isFunction( data ) ) {
- type = type || callback;
- callback = data;
- data = undefined;
- }
-
- // The url can be an options object (which then must have .url)
- return jQuery.ajax( jQuery.extend( {
- url: url,
- type: method,
- dataType: type,
- data: data,
- success: callback
- }, jQuery.isPlainObject( url ) && url ) );
- };
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
- var i;
- for ( i in s.headers ) {
- if ( i.toLowerCase() === "content-type" ) {
- s.contentType = s.headers[ i ] || "";
- }
- }
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
- return jQuery.ajax( {
- url: url,
-
- // Make this explicit, since user can override this through ajaxSetup (#11264)
- type: "GET",
- dataType: "script",
- cache: true,
- async: false,
- global: false,
-
- // Only evaluate the response if it is successful (gh-4126)
- // dataFilter is not invoked for failure responses, so using it instead
- // of the default converter is kludgy but it works.
- converters: {
- "text script": function() {}
- },
- dataFilter: function( response ) {
- jQuery.globalEval( response, options, doc );
- }
- } );
-};
-
-
-jQuery.fn.extend( {
- wrapAll: function( html ) {
- var wrap;
-
- if ( this[ 0 ] ) {
- if ( isFunction( html ) ) {
- html = html.call( this[ 0 ] );
- }
-
- // The elements to wrap the target around
- wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
- if ( this[ 0 ].parentNode ) {
- wrap.insertBefore( this[ 0 ] );
- }
-
- wrap.map( function() {
- var elem = this;
-
- while ( elem.firstElementChild ) {
- elem = elem.firstElementChild;
- }
-
- return elem;
- } ).append( this );
- }
-
- return this;
- },
-
- wrapInner: function( html ) {
- if ( isFunction( html ) ) {
- return this.each( function( i ) {
- jQuery( this ).wrapInner( html.call( this, i ) );
- } );
- }
-
- return this.each( function() {
- var self = jQuery( this ),
- contents = self.contents();
-
- if ( contents.length ) {
- contents.wrapAll( html );
-
- } else {
- self.append( html );
- }
- } );
- },
-
- wrap: function( html ) {
- var htmlIsFunction = isFunction( html );
-
- return this.each( function( i ) {
- jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
- } );
- },
-
- unwrap: function( selector ) {
- this.parent( selector ).not( "body" ).each( function() {
- jQuery( this ).replaceWith( this.childNodes );
- } );
- return this;
- }
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
- return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
- return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
- try {
- return new window.XMLHttpRequest();
- } catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
- // File protocol always yields status code 0, assume 200
- 0: 200,
-
- // Support: IE <=9 only
- // #1450: sometimes IE returns 1223 when it should be 204
- 1223: 204
- },
- xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
- var callback, errorCallback;
-
- // Cross domain only allowed if supported through XMLHttpRequest
- if ( support.cors || xhrSupported && !options.crossDomain ) {
- return {
- send: function( headers, complete ) {
- var i,
- xhr = options.xhr();
-
- xhr.open(
- options.type,
- options.url,
- options.async,
- options.username,
- options.password
- );
-
- // Apply custom fields if provided
- if ( options.xhrFields ) {
- for ( i in options.xhrFields ) {
- xhr[ i ] = options.xhrFields[ i ];
- }
- }
-
- // Override mime type if needed
- if ( options.mimeType && xhr.overrideMimeType ) {
- xhr.overrideMimeType( options.mimeType );
- }
-
- // X-Requested-With header
- // For cross-domain requests, seeing as conditions for a preflight are
- // akin to a jigsaw puzzle, we simply never set it to be sure.
- // (it can always be set on a per-request basis or even using ajaxSetup)
- // For same-domain requests, won't change header if already provided.
- if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
- headers[ "X-Requested-With" ] = "XMLHttpRequest";
- }
-
- // Set headers
- for ( i in headers ) {
- xhr.setRequestHeader( i, headers[ i ] );
- }
-
- // Callback
- callback = function( type ) {
- return function() {
- if ( callback ) {
- callback = errorCallback = xhr.onload =
- xhr.onerror = xhr.onabort = xhr.ontimeout =
- xhr.onreadystatechange = null;
-
- if ( type === "abort" ) {
- xhr.abort();
- } else if ( type === "error" ) {
-
- // Support: IE <=9 only
- // On a manual native abort, IE9 throws
- // errors on any property access that is not readyState
- if ( typeof xhr.status !== "number" ) {
- complete( 0, "error" );
- } else {
- complete(
-
- // File: protocol always yields status 0; see #8605, #14207
- xhr.status,
- xhr.statusText
- );
- }
- } else {
- complete(
- xhrSuccessStatus[ xhr.status ] || xhr.status,
- xhr.statusText,
-
- // Support: IE <=9 only
- // IE9 has no XHR2 but throws on binary (trac-11426)
- // For XHR2 non-text, let the caller handle it (gh-2498)
- ( xhr.responseType || "text" ) !== "text" ||
- typeof xhr.responseText !== "string" ?
- { binary: xhr.response } :
- { text: xhr.responseText },
- xhr.getAllResponseHeaders()
- );
- }
- }
- };
- };
-
- // Listen to events
- xhr.onload = callback();
- errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
- // Support: IE 9 only
- // Use onreadystatechange to replace onabort
- // to handle uncaught aborts
- if ( xhr.onabort !== undefined ) {
- xhr.onabort = errorCallback;
- } else {
- xhr.onreadystatechange = function() {
-
- // Check readyState before timeout as it changes
- if ( xhr.readyState === 4 ) {
-
- // Allow onerror to be called first,
- // but that will not handle a native abort
- // Also, save errorCallback to a variable
- // as xhr.onerror cannot be accessed
- window.setTimeout( function() {
- if ( callback ) {
- errorCallback();
- }
- } );
- }
- };
- }
-
- // Create the abort callback
- callback = callback( "abort" );
-
- try {
-
- // Do send the request (this may raise an exception)
- xhr.send( options.hasContent && options.data || null );
- } catch ( e ) {
-
- // #14683: Only rethrow if this hasn't been notified as an error yet
- if ( callback ) {
- throw e;
- }
- }
- },
-
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
- if ( s.crossDomain ) {
- s.contents.script = false;
- }
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
- accepts: {
- script: "text/javascript, application/javascript, " +
- "application/ecmascript, application/x-ecmascript"
- },
- contents: {
- script: /\b(?:java|ecma)script\b/
- },
- converters: {
- "text script": function( text ) {
- jQuery.globalEval( text );
- return text;
- }
- }
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
- if ( s.cache === undefined ) {
- s.cache = false;
- }
- if ( s.crossDomain ) {
- s.type = "GET";
- }
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
- // This transport only deals with cross domain or forced-by-attrs requests
- if ( s.crossDomain || s.scriptAttrs ) {
- var script, callback;
- return {
- send: function( _, complete ) {
- script = jQuery( "<script>" )
- .attr( s.scriptAttrs || {} )
- .prop( { charset: s.scriptCharset, src: s.url } )
- .on( "load error", callback = function( evt ) {
- script.remove();
- callback = null;
- if ( evt ) {
- complete( evt.type === "error" ? 404 : 200, evt.type );
- }
- } );
-
- // Use native DOM manipulation to avoid our domManip AJAX trickery
- document.head.appendChild( script[ 0 ] );
- },
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-var oldCallbacks = [],
- rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
- jsonp: "callback",
- jsonpCallback: function() {
- var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
- this[ callback ] = true;
- return callback;
- }
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
- var callbackName, overwritten, responseContainer,
- jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
- "url" :
- typeof s.data === "string" &&
- ( s.contentType || "" )
- .indexOf( "application/x-www-form-urlencoded" ) === 0 &&
- rjsonp.test( s.data ) && "data"
- );
-
- // Handle iff the expected data type is "jsonp" or we have a parameter to set
- if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
- // Get callback name, remembering preexisting value associated with it
- callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
- s.jsonpCallback() :
- s.jsonpCallback;
-
- // Insert callback into url or form data
- if ( jsonProp ) {
- s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
- } else if ( s.jsonp !== false ) {
- s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
- }
-
- // Use data converter to retrieve json after script execution
- s.converters[ "script json" ] = function() {
- if ( !responseContainer ) {
- jQuery.error( callbackName + " was not called" );
- }
- return responseContainer[ 0 ];
- };
-
- // Force json dataType
- s.dataTypes[ 0 ] = "json";
-
- // Install callback
- overwritten = window[ callbackName ];
- window[ callbackName ] = function() {
- responseContainer = arguments;
- };
-
- // Clean-up function (fires after converters)
- jqXHR.always( function() {
-
- // If previous value didn't exist - remove it
- if ( overwritten === undefined ) {
- jQuery( window ).removeProp( callbackName );
-
- // Otherwise restore preexisting value
- } else {
- window[ callbackName ] = overwritten;
- }
-
- // Save back as free
- if ( s[ callbackName ] ) {
-
- // Make sure that re-using the options doesn't screw things around
- s.jsonpCallback = originalSettings.jsonpCallback;
-
- // Save the callback name for future use
- oldCallbacks.push( callbackName );
- }
-
- // Call if it was a function and we have a response
- if ( responseContainer && isFunction( overwritten ) ) {
- overwritten( responseContainer[ 0 ] );
- }
-
- responseContainer = overwritten = undefined;
- } );
-
- // Delegate to script
- return "script";
- }
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
- var body = document.implementation.createHTMLDocument( "" ).body;
- body.innerHTML = "<form></form><form></form>";
- return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
- if ( typeof data !== "string" ) {
- return [];
- }
- if ( typeof context === "boolean" ) {
- keepScripts = context;
- context = false;
- }
-
- var base, parsed, scripts;
-
- if ( !context ) {
-
- // Stop scripts or inline event handlers from being executed immediately
- // by using document.implementation
- if ( support.createHTMLDocument ) {
- context = document.implementation.createHTMLDocument( "" );
-
- // Set the base href for the created document
- // so any parsed elements with URLs
- // are based on the document's URL (gh-2965)
- base = context.createElement( "base" );
- base.href = document.location.href;
- context.head.appendChild( base );
- } else {
- context = document;
- }
- }
-
- parsed = rsingleTag.exec( data );
- scripts = !keepScripts && [];
-
- // Single tag
- if ( parsed ) {
- return [ context.createElement( parsed[ 1 ] ) ];
- }
-
- parsed = buildFragment( [ data ], context, scripts );
-
- if ( scripts && scripts.length ) {
- jQuery( scripts ).remove();
- }
-
- return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
- var selector, type, response,
- self = this,
- off = url.indexOf( " " );
-
- if ( off > -1 ) {
- selector = stripAndCollapse( url.slice( off ) );
- url = url.slice( 0, off );
- }
-
- // If it's a function
- if ( isFunction( params ) ) {
-
- // We assume that it's the callback
- callback = params;
- params = undefined;
-
- // Otherwise, build a param string
- } else if ( params && typeof params === "object" ) {
- type = "POST";
- }
-
- // If we have elements to modify, make the request
- if ( self.length > 0 ) {
- jQuery.ajax( {
- url: url,
-
- // If "type" variable is undefined, then "GET" method will be used.
- // Make value of this field explicit since
- // user can override it through ajaxSetup method
- type: type || "GET",
- dataType: "html",
- data: params
- } ).done( function( responseText ) {
-
- // Save response for use in complete callback
- response = arguments;
-
- self.html( selector ?
-
- // If a selector was specified, locate the right elements in a dummy div
- // Exclude scripts to avoid IE 'Permission Denied' errors
- jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
- // Otherwise use the full result
- responseText );
-
- // If the request succeeds, this function gets "data", "status", "jqXHR"
- // but they are ignored because response was set above.
- // If it fails, this function gets "jqXHR", "status", "error"
- } ).always( callback && function( jqXHR, status ) {
- self.each( function() {
- callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
- } );
- } );
- }
-
- return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
- return jQuery.grep( jQuery.timers, function( fn ) {
- return elem === fn.elem;
- } ).length;
-};
-
-
-
-
-jQuery.offset = {
- setOffset: function( elem, options, i ) {
- var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
- position = jQuery.css( elem, "position" ),
- curElem = jQuery( elem ),
- props = {};
-
- // Set position first, in-case top/left are set even on static elem
- if ( position === "static" ) {
- elem.style.position = "relative";
- }
-
- curOffset = curElem.offset();
- curCSSTop = jQuery.css( elem, "top" );
- curCSSLeft = jQuery.css( elem, "left" );
- calculatePosition = ( position === "absolute" || position === "fixed" ) &&
- ( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
- // Need to be able to calculate position if either
- // top or left is auto and position is either absolute or fixed
- if ( calculatePosition ) {
- curPosition = curElem.position();
- curTop = curPosition.top;
- curLeft = curPosition.left;
-
- } else {
- curTop = parseFloat( curCSSTop ) || 0;
- curLeft = parseFloat( curCSSLeft ) || 0;
- }
-
- if ( isFunction( options ) ) {
-
- // Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
- options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
- }
-
- if ( options.top != null ) {
- props.top = ( options.top - curOffset.top ) + curTop;
- }
- if ( options.left != null ) {
- props.left = ( options.left - curOffset.left ) + curLeft;
- }
-
- if ( "using" in options ) {
- options.using.call( elem, props );
-
- } else {
- curElem.css( props );
- }
- }
-};
-
-jQuery.fn.extend( {
-
- // offset() relates an element's border box to the document origin
- offset: function( options ) {
-
- // Preserve chaining for setter
- if ( arguments.length ) {
- return options === undefined ?
- this :
- this.each( function( i ) {
- jQuery.offset.setOffset( this, options, i );
- } );
- }
-
- var rect, win,
- elem = this[ 0 ];
-
- if ( !elem ) {
- return;
- }
-
- // Return zeros for disconnected and hidden (display: none) elements (gh-2310)
- // Support: IE <=11 only
- // Running getBoundingClientRect on a
- // disconnected node in IE throws an error
- if ( !elem.getClientRects().length ) {
- return { top: 0, left: 0 };
- }
-
- // Get document-relative position by adding viewport scroll to viewport-relative gBCR
- rect = elem.getBoundingClientRect();
- win = elem.ownerDocument.defaultView;
- return {
- top: rect.top + win.pageYOffset,
- left: rect.left + win.pageXOffset
- };
- },
-
- // position() relates an element's margin box to its offset parent's padding box
- // This corresponds to the behavior of CSS absolute positioning
- position: function() {
- if ( !this[ 0 ] ) {
- return;
- }
-
- var offsetParent, offset, doc,
- elem = this[ 0 ],
- parentOffset = { top: 0, left: 0 };
-
- // position:fixed elements are offset from the viewport, which itself always has zero offset
- if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
- // Assume position:fixed implies availability of getBoundingClientRect
- offset = elem.getBoundingClientRect();
-
- } else {
- offset = this.offset();
-
- // Account for the *real* offset parent, which can be the document or its root element
- // when a statically positioned element is identified
- doc = elem.ownerDocument;
- offsetParent = elem.offsetParent || doc.documentElement;
- while ( offsetParent &&
- ( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
- jQuery.css( offsetParent, "position" ) === "static" ) {
-
- offsetParent = offsetParent.parentNode;
- }
- if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
- // Incorporate borders into its offset, since they are outside its content origin
- parentOffset = jQuery( offsetParent ).offset();
- parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
- parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
- }
- }
-
- // Subtract parent offsets and element margins
- return {
- top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
- left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
- };
- },
-
- // This method will return documentElement in the following cases:
- // 1) For the element inside the iframe without offsetParent, this method will return
- // documentElement of the parent window
- // 2) For the hidden or detached element
- // 3) For body or html element, i.e. in case of the html node - it will return itself
- //
- // but those exceptions were never presented as a real life use-cases
- // and might be considered as more preferable results.
- //
- // This logic, however, is not guaranteed and can change at any point in the future
- offsetParent: function() {
- return this.map( function() {
- var offsetParent = this.offsetParent;
-
- while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
- offsetParent = offsetParent.offsetParent;
- }
-
- return offsetParent || documentElement;
- } );
- }
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
- var top = "pageYOffset" === prop;
-
- jQuery.fn[ method ] = function( val ) {
- return access( this, function( elem, method, val ) {
-
- // Coalesce documents and windows
- var win;
- if ( isWindow( elem ) ) {
- win = elem;
- } else if ( elem.nodeType === 9 ) {
- win = elem.defaultView;
- }
-
- if ( val === undefined ) {
- return win ? win[ prop ] : elem[ method ];
- }
-
- if ( win ) {
- win.scrollTo(
- !top ? val : win.pageXOffset,
- top ? val : win.pageYOffset
- );
-
- } else {
- elem[ method ] = val;
- }
- }, method, val, arguments.length );
- };
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
- jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
- function( elem, computed ) {
- if ( computed ) {
- computed = curCSS( elem, prop );
-
- // If curCSS returns percentage, fallback to offset
- return rnumnonpx.test( computed ) ?
- jQuery( elem ).position()[ prop ] + "px" :
- computed;
- }
- }
- );
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
- jQuery.each( {
- padding: "inner" + name,
- content: type,
- "": "outer" + name
- }, function( defaultExtra, funcName ) {
-
- // Margin is only for outerHeight, outerWidth
- jQuery.fn[ funcName ] = function( margin, value ) {
- var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
- extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
- return access( this, function( elem, type, value ) {
- var doc;
-
- if ( isWindow( elem ) ) {
-
- // $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
- return funcName.indexOf( "outer" ) === 0 ?
- elem[ "inner" + name ] :
- elem.document.documentElement[ "client" + name ];
- }
-
- // Get document width or height
- if ( elem.nodeType === 9 ) {
- doc = elem.documentElement;
-
- // Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
- // whichever is greatest
- return Math.max(
- elem.body[ "scroll" + name ], doc[ "scroll" + name ],
- elem.body[ "offset" + name ], doc[ "offset" + name ],
- doc[ "client" + name ]
- );
- }
-
- return value === undefined ?
-
- // Get width or height on the element, requesting but not forcing parseFloat
- jQuery.css( elem, type, extra ) :
-
- // Set width or height on the element
- jQuery.style( elem, type, value, extra );
- }, type, chainable ? margin : undefined, chainable );
- };
- } );
-} );
-
-
-jQuery.each( [
- "ajaxStart",
- "ajaxStop",
- "ajaxComplete",
- "ajaxError",
- "ajaxSuccess",
- "ajaxSend"
-], function( _i, type ) {
- jQuery.fn[ type ] = function( fn ) {
- return this.on( type, fn );
- };
-} );
-
-
-
-
-jQuery.fn.extend( {
-
- bind: function( types, data, fn ) {
- return this.on( types, null, data, fn );
- },
- unbind: function( types, fn ) {
- return this.off( types, null, fn );
- },
-
- delegate: function( selector, types, data, fn ) {
- return this.on( types, selector, data, fn );
- },
- undelegate: function( selector, types, fn ) {
-
- // ( namespace ) or ( selector, types [, fn] )
- return arguments.length === 1 ?
- this.off( selector, "**" ) :
- this.off( types, selector || "**", fn );
- },
-
- hover: function( fnOver, fnOut ) {
- return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
- }
-} );
-
-jQuery.each(
- ( "blur focus focusin focusout resize scroll click dblclick " +
- "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
- "change select submit keydown keypress keyup contextmenu" ).split( " " ),
- function( _i, name ) {
-
- // Handle event binding
- jQuery.fn[ name ] = function( data, fn ) {
- return arguments.length > 0 ?
- this.on( name, null, data, fn ) :
- this.trigger( name );
- };
- }
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
- var tmp, args, proxy;
-
- if ( typeof context === "string" ) {
- tmp = fn[ context ];
- context = fn;
- fn = tmp;
- }
-
- // Quick check to determine if target is callable, in the spec
- // this throws a TypeError, but we will just return undefined.
- if ( !isFunction( fn ) ) {
- return undefined;
- }
-
- // Simulated bind
- args = slice.call( arguments, 2 );
- proxy = function() {
- return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
- };
-
- // Set the guid of unique handler to the same of original handler, so it can be removed
- proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
- return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
- if ( hold ) {
- jQuery.readyWait++;
- } else {
- jQuery.ready( true );
- }
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
- // As of jQuery 3.0, isNumeric is limited to
- // strings and numbers (primitives or objects)
- // that can be coerced to finite numbers (gh-2662)
- var type = jQuery.type( obj );
- return ( type === "number" || type === "string" ) &&
-
- // parseFloat NaNs numeric-cast false positives ("")
- // ...but misinterprets leading-number strings, particularly hex literals ("0x...")
- // subtraction forces infinities to NaN
- !isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
- define( "jquery", [], function() {
- return jQuery;
- } );
-}
-
-
-
-
-var
-
- // Map over jQuery in case of overwrite
- _jQuery = window.jQuery,
-
- // Map over the $ in case of overwrite
- _$ = window.$;
-
-jQuery.noConflict = function( deep ) {
- if ( window.$ === jQuery ) {
- window.$ = _$;
- }
-
- if ( deep && window.jQuery === jQuery ) {
- window.jQuery = _jQuery;
- }
-
- return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
- window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/sphinx/_static/jquery.js b/sphinx/_static/jquery.js
deleted file mode 100644
index c4c6022f2982e8dae64cebd6b9a2b59f2547faad..0000000000000000000000000000000000000000
--- a/sphinx/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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index 2e22b06ab13bec689de4d1530b8b625bc6d69ae8..0000000000000000000000000000000000000000
--- a/sphinx/_static/language_data.js
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-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
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-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
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diff --git a/sphinx/_static/pygments.css b/sphinx/_static/pygments.css
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\ No newline at end of file
diff --git a/sphinx/_static/scripts/furo.js.LICENSE.txt b/sphinx/_static/scripts/furo.js.LICENSE.txt
deleted file mode 100644
index 1632189c7e0cd1a17e452d2d0b24dfa741c7ea46..0000000000000000000000000000000000000000
--- a/sphinx/_static/scripts/furo.js.LICENSE.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * gumshoejs v5.1.2 (patched by @pradyunsg)
- * A simple, framework-agnostic scrollspy script.
- * (c) 2019 Chris Ferdinandi
- * MIT License
- * http://github.com/cferdinandi/gumshoe
- */
diff --git a/sphinx/_static/scripts/furo.js.map b/sphinx/_static/scripts/furo.js.map
deleted file mode 100644
index 7ed2be87dcf113b9f4c1be32b92aae905f5bb488..0000000000000000000000000000000000000000
--- a/sphinx/_static/scripts/furo.js.map
+++ /dev/null
@@ -1 +0,0 @@
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calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader();\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 0.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/sphinx/_static/searchtools.js b/sphinx/_static/searchtools.js
deleted file mode 100644
index e89e34d4e7729c9cf60e90e97ecc0507ebc398ec..0000000000000000000000000000000000000000
--- a/sphinx/_static/searchtools.js
+++ /dev/null
@@ -1,566 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
- const [docName, title, anchor, descr, score, _filename] = item;
-
- let listItem = document.createElement("li");
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = docUrlRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = docUrlRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = title;
- if (descr)
- listItem.appendChild(document.createElement("span")).innerHTML =
- " (" + descr + ")";
- else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms)
- );
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = _(
- `Search finished, found ${resultCount} page(s) matching the search query.`
- );
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent !== undefined) return docContent.textContent;
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- query: (query) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- // array of [docname, title, anchor, descr, score, filename]
- let results = [];
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- let score = Math.round(100 * queryLower.length / title.length)
- results.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score,
- filenames[file],
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id] of foundEntries) {
- let score = Math.round(100 * queryLower.length / entry.length)
- results.push([
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- ]);
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- results.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
- // now sort the results by score (in opposite order of appearance, since the
- // display function below uses pop() to retrieve items) and then
- // alphabetically
- results.sort((a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
- });
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- results = results.reverse();
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord) && !terms[word])
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord) && !titleTerms[word])
- arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
- });
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
- fileMap.get(file).push(word);
- else fileMap.set(file, [word]);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords) => {
- const text = Search.htmlToText(htmlText);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/sphinx/_static/skeleton.css b/sphinx/_static/skeleton.css
deleted file mode 100644
index 467c878c62025811f065562a1bcc5bf152d2d0c0..0000000000000000000000000000000000000000
--- a/sphinx/_static/skeleton.css
+++ /dev/null
@@ -1,296 +0,0 @@
-/* Some sane resets. */
-html {
- height: 100%;
-}
-
-body {
- margin: 0;
- min-height: 100%;
-}
-
-/* All the flexbox magic! */
-body,
-.sb-announcement,
-.sb-content,
-.sb-main,
-.sb-container,
-.sb-container__inner,
-.sb-article-container,
-.sb-footer-content,
-.sb-header,
-.sb-header-secondary,
-.sb-footer {
- display: flex;
-}
-
-/* These order things vertically */
-body,
-.sb-main,
-.sb-article-container {
- flex-direction: column;
-}
-
-/* Put elements in the center */
-.sb-header,
-.sb-header-secondary,
-.sb-container,
-.sb-content,
-.sb-footer,
-.sb-footer-content {
- justify-content: center;
-}
-/* Put elements at the ends */
-.sb-article-container {
- justify-content: space-between;
-}
-
-/* These elements grow. */
-.sb-main,
-.sb-content,
-.sb-container,
-article {
- flex-grow: 1;
-}
-
-/* Because padding making this wider is not fun */
-article {
- box-sizing: border-box;
-}
-
-/* The announcements element should never be wider than the page. */
-.sb-announcement {
- max-width: 100%;
-}
-
-.sb-sidebar-primary,
-.sb-sidebar-secondary {
- flex-shrink: 0;
- width: 17rem;
-}
-
-.sb-announcement__inner {
- justify-content: center;
-
- box-sizing: border-box;
- height: 3rem;
-
- overflow-x: auto;
- white-space: nowrap;
-}
-
-/* Sidebars, with checkbox-based toggle */
-.sb-sidebar-primary,
-.sb-sidebar-secondary {
- position: fixed;
- height: 100%;
- top: 0;
-}
-
-.sb-sidebar-primary {
- left: -17rem;
- transition: left 250ms ease-in-out;
-}
-.sb-sidebar-secondary {
- right: -17rem;
- transition: right 250ms ease-in-out;
-}
-
-.sb-sidebar-toggle {
- display: none;
-}
-.sb-sidebar-overlay {
- position: fixed;
- top: 0;
- width: 0;
- height: 0;
-
- transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease;
-
- opacity: 0;
- background-color: rgba(0, 0, 0, 0.54);
-}
-
-#sb-sidebar-toggle--primary:checked
- ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"],
-#sb-sidebar-toggle--secondary:checked
- ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] {
- width: 100%;
- height: 100%;
- opacity: 1;
- transition: width 0ms ease, height 0ms ease, opacity 250ms ease;
-}
-
-#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary {
- left: 0;
-}
-#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary {
- right: 0;
-}
-
-/* Full-width mode */
-.drop-secondary-sidebar-for-full-width-content
- .hide-when-secondary-sidebar-shown {
- display: none !important;
-}
-.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary {
- display: none !important;
-}
-
-/* Mobile views */
-.sb-page-width {
- width: 100%;
-}
-
-.sb-article-container,
-.sb-footer-content__inner,
-.drop-secondary-sidebar-for-full-width-content .sb-article,
-.drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 100vw;
-}
-
-.sb-article,
-.match-content-width {
- padding: 0 1rem;
- box-sizing: border-box;
-}
-
-@media (min-width: 32rem) {
- .sb-article,
- .match-content-width {
- padding: 0 2rem;
- }
-}
-
-/* Tablet views */
-@media (min-width: 42rem) {
- .sb-article-container {
- width: auto;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 42rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-@media (min-width: 46rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 46rem;
- }
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
-}
-@media (min-width: 50rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 50rem;
- }
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
-}
-
-/* Tablet views */
-@media (min-width: 59rem) {
- .sb-sidebar-secondary {
- position: static;
- }
- .hide-when-secondary-sidebar-shown {
- display: none !important;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 59rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-@media (min-width: 63rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 63rem;
- }
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
-}
-@media (min-width: 67rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
-}
-
-/* Desktop views */
-@media (min-width: 76rem) {
- .sb-sidebar-primary {
- position: static;
- }
- .hide-when-primary-sidebar-shown {
- display: none !important;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 59rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-
-/* Full desktop views */
-@media (min-width: 80rem) {
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 63rem;
- }
-}
-
-@media (min-width: 84rem) {
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
-}
-
-@media (min-width: 88rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
- .sb-page-width {
- width: 88rem;
- }
-}
diff --git a/sphinx/_static/sphinx_highlight.js b/sphinx/_static/sphinx_highlight.js
deleted file mode 100644
index aae669d7ea6b1dc6edcb5c651f5d095f7a2dc6e6..0000000000000000000000000000000000000000
--- a/sphinx/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,144 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- parent.insertBefore(
- span,
- parent.insertBefore(
- document.createTextNode(val.substr(pos + text.length)),
- node.nextSibling
- )
- );
- node.nodeValue = val.substr(0, pos);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '<p class="highlight-link">' +
- '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
- _("Hide Search Matches") +
- "</a></p>"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
diff --git a/sphinx/_static/styles/furo-extensions.css b/sphinx/_static/styles/furo-extensions.css
deleted file mode 100644
index bc447f228f5247cc450a0e8b1f41173d3a264f5a..0000000000000000000000000000000000000000
--- a/sphinx/_static/styles/furo-extensions.css
+++ /dev/null
@@ -1,2 +0,0 @@
-#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout a{color:var(--color-foreground-secondary)!important}#furo-readthedocs-versions{background:transparent;display:block;position:static;width:100%}#furo-readthedocs-versions .rst-versions{background:#1a1c1e}#furo-readthedocs-versions .rst-current-version{background:var(--color-sidebar-item-background);cursor:unset}#furo-readthedocs-versions .rst-current-version:hover{background:var(--color-sidebar-item-background)}#furo-readthedocs-versions .rst-current-version .fa-book{color:var(--color-foreground-primary)}#furo-readthedocs-versions>.rst-other-versions{padding:0}#furo-readthedocs-versions>.rst-other-versions small{opacity:1}#furo-readthedocs-versions .injected .rst-versions{position:unset}#furo-readthedocs-versions:focus-within,#furo-readthedocs-versions:hover{box-shadow:0 0 0 1px var(--color-sidebar-background-border)}#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:hover .rst-current-version{background:#1a1c1e;font-size:inherit;height:auto;line-height:inherit;padding:12px;text-align:right}#furo-readthedocs-versions:focus-within .rst-current-version .fa-book,#furo-readthedocs-versions:hover .rst-current-version .fa-book{color:#fff;float:left}#furo-readthedocs-versions:focus-within .fa-caret-down,#furo-readthedocs-versions:hover .fa-caret-down{display:none}#furo-readthedocs-versions:focus-within .injected,#furo-readthedocs-versions:focus-within 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normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */\n\n/* Document\n ========================================================================== */\n\n/**\n * 1. Correct the line height in all browsers.\n * 2. Prevent adjustments of font size after orientation changes in iOS.\n */\n\nhtml {\n line-height: 1.15; /* 1 */\n -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/* Sections\n ========================================================================== */\n\n/**\n * Remove the margin in all browsers.\n */\n\nbody {\n margin: 0;\n}\n\n/**\n * Render the `main` element consistently in IE.\n */\n\nmain {\n display: block;\n}\n\n/**\n * Correct the font size and margin on `h1` elements within `section` and\n * `article` contexts in Chrome, Firefox, and Safari.\n */\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n/* Grouping content\n ========================================================================== */\n\n/**\n * 1. Add the correct box sizing in Firefox.\n * 2. Show the overflow in Edge and IE.\n */\n\nhr {\n box-sizing: content-box; /* 1 */\n height: 0; /* 1 */\n overflow: visible; /* 2 */\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\npre {\n font-family: monospace, monospace; /* 1 */\n font-size: 1em; /* 2 */\n}\n\n/* Text-level semantics\n ========================================================================== */\n\n/**\n * Remove the gray background on active links in IE 10.\n */\n\na {\n background-color: transparent;\n}\n\n/**\n * 1. Remove the bottom border in Chrome 57-\n * 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n */\n\nabbr[title] {\n border-bottom: none; /* 1 */\n text-decoration: underline; /* 2 */\n text-decoration: underline dotted; /* 2 */\n}\n\n/**\n * Add the correct font weight in Chrome, Edge, and Safari.\n */\n\nb,\nstrong {\n font-weight: bolder;\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\ncode,\nkbd,\nsamp {\n font-family: monospace, monospace; /* 1 */\n font-size: 1em; /* 2 */\n}\n\n/**\n * Add the correct font size in all browsers.\n */\n\nsmall {\n font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` elements from affecting the line height in\n * all browsers.\n */\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsub {\n bottom: -0.25em;\n}\n\nsup {\n top: -0.5em;\n}\n\n/* Embedded content\n ========================================================================== */\n\n/**\n * Remove the border on images inside links in IE 10.\n */\n\nimg {\n border-style: none;\n}\n\n/* Forms\n ========================================================================== */\n\n/**\n * 1. Change the font styles in all browsers.\n * 2. Remove the margin in Firefox and Safari.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n font-family: inherit; /* 1 */\n font-size: 100%; /* 1 */\n line-height: 1.15; /* 1 */\n margin: 0; /* 2 */\n}\n\n/**\n * Show the overflow in IE.\n * 1. Show the overflow in Edge.\n */\n\nbutton,\ninput { /* 1 */\n overflow: visible;\n}\n\n/**\n * Remove the inheritance of text transform in Edge, Firefox, and IE.\n * 1. Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.1)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.1)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.1\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-secondary);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// </debugging things>\n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$full-width: $content-width + 2 * ($content-padding + $sidebar-width);\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\np.admonition-title, p.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\n // Break words when they're too long\n span.pre\n overflow-wrap: anywhere\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n overflow: auto\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","//\n// Search Page Listing\n//\nul.search\n padding-left: 0\n list-style: none\n\n li\n padding: 1rem 0\n border-bottom: 1px solid var(--color-background-border)\n\n//\n// Highlighted by links in search page\n//\n[role=main] .highlighted\n background-color: var(--color-highlighted-background)\n color: var(--color-highlighted-text)\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the <label>\n li.has-children\n > .reference\n padding-right: var(--sidebar-expander-width)\n\n // Colorize the top-level list items and icon.\n .toctree-l1\n & > .reference,\n & > label .icon\n color: var(--color-sidebar-link-text--top-level)\n\n // Color changes on hover\n label\n background: var(--color-sidebar-item-expander-background)\n &:hover\n background: var(--color-sidebar-item-expander-background--hover)\n\n .current > .reference\n background: var(--color-sidebar-item-background--current)\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n position: absolute\n display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n ~ ul\n display: none\n\n ~ label .icon svg\n transform: rotate(90deg)\n\n.toctree-checkbox:checked\n ~ ul\n display: block\n\n ~ label .icon svg\n transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n padding: var(--toc-title-padding)\n padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n color: var(--color-toc-title-text)\n font-size: var(--toc-title-font-size)\n padding-left: var(--toc-spacing-horizontal)\n text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n display: none\n\n.toc-tree-container\n padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n font-size: var(--toc-font-size)\n line-height: 1.3\n border-left: 1px solid var(--color-background-border)\n\n padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n // Hide the first \"top level\" bullet.\n > ul > li:first-child\n padding-top: 0\n & > ul\n padding-left: 0\n & > a\n display: none\n\n ul\n list-style-type: none\n margin-top: 0\n margin-bottom: 0\n padding-left: var(--toc-item-spacing-horizontal)\n li\n padding-top: var(--toc-item-spacing-vertical)\n\n &.scroll-current >.reference\n color: var(--color-toc-item-text--active)\n font-weight: bold\n\n .reference\n color: var(--color-toc-item-text)\n text-decoration: none\n overflow-wrap: anywhere\n\n.toc-scroll\n max-height: 100vh\n overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n color: var(--color-problematic)\n background: rgba(255, 0, 0, 0.25)\n &::before\n content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling.Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n text-align: left\n\n.text-align\\:center > p\n text-align: center\n\n.text-align\\:right > p\n text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/sphinx/_static/tabs.css b/sphinx/_static/tabs.css
deleted file mode 100644
index 13042e567208006f540374a7d89a606c063a39bf..0000000000000000000000000000000000000000
--- a/sphinx/_static/tabs.css
+++ /dev/null
@@ -1,110 +0,0 @@
-/* body[data-theme] { */
-:root {
- --tabs--label-text: #4b5563;
- --tabs--label-text--hover: #4b5563;
- --tabs--label-text--active: #0ea5e9;
- --tabs--label-text--active--hover: #0ea5e9;
- --tabs--label-background: transparent;
- --tabs--label-background--hover: transparent;
- --tabs--label-background--active: transparent;
- --tabs--label-background--active--hover: transparent;
- --tabs--label-border: transparent;
- --tabs--label-border--hover: #d1d5db;
- --tabs--label-border--active: #0ea5e9;
- --tabs--label-border--active--hover: #0ea5e9;
- --tabs--padding-x: 1.25em;
- --tabs--margin-x: 0;
- --tabs--border: #e6e6e6;
-}
-
-/* Hide radio buttons */
-.tab-set > input {
- position: absolute;
- opacity: 0;
-}
-
-/* Tab set container */
-.tab-set {
- border-radius: 2px;
- display: flex;
- flex-wrap: wrap;
- margin: 0.75em 0;
- position: relative;
-}
-
-/* Tab label */
-.tab-set > label {
- z-index: 1;
-
- width: auto;
- border-bottom: 2px solid var(--tabs--label-border);
- padding: 1em var(--tabs--padding-x) 0.5em;
- margin-left: var(--tabs--margin-x);
-
- color: var(--tabs--label-text);
- background: var(--tabs--label-background);
-
- transition: color 250ms;
-
- cursor: pointer;
-
- font-size: 0.875em;
- font-weight: 700;
-}
-.tab-set > label:nth-child(2) {
- margin-left: 0;
-}
-
-/* Hovered label */
-.tab-set > label:hover {
- color: var(--tabs--label-text--hover);
- background: var(--tabs--label-background--hover);
- border-color: var(--tabs--label-border--hover);
-}
-
-/* Active tab label */
-.tab-set > input:checked + label {
- color: var(--tabs--label-text--active);
- background: var(--tabs--label-background--active);
- border-color: var(--tabs--label-border--active);
-}
-.tab-set > input:checked + label:hover {
- color: var(--tabs--label-text--active--hover);
- background: var(--tabs--label-background--active--hover);
- border-color: var(--tabs--label-border--active--hover);
-}
-
-/* Tab content */
-.tab-content {
- order: 99;
- display: none;
- width: 100%;
- box-shadow: 0 -0.0625rem var(--tabs--border);
-}
-
-/* Show content, when input is checked. */
-.tab-set > input:checked + label + .tab-content {
- display: block;
-}
-.tab-content > p:first-child {
- margin-top: 0.75rem;
-}
-/* Remove the top border on first code block */
-.tab-content > [class^="highlight-"]:first-child .highlight {
- border-top: none;
- border-top-left-radius: 0;
- border-top-right-radius: 0;
-}
-
-/* Remove margins on children */
-.tab-content > *:first-child {
- margin-top: 0;
-}
-.tab-content > *:last-child {
- margin-bottom: 0;
-}
-
-/* Remove margins on nested tabs */
-.tab-content > .tab-set {
- margin: 0;
-}
diff --git a/sphinx/_static/tabs.js b/sphinx/_static/tabs.js
deleted file mode 100644
index d15e04544f9d94793b062cbc77c1a76d2cab19af..0000000000000000000000000000000000000000
--- a/sphinx/_static/tabs.js
+++ /dev/null
@@ -1,21 +0,0 @@
-var labels_by_text = {};
-
-function ready() {
- var li = document.getElementsByClassName("tab-label");
- for (const label of li) {
- label.onclick = onLabelClick;
- const text = label.textContent;
- if (!labels_by_text[text]) {
- labels_by_text[text] = [];
- }
- labels_by_text[text].push(label);
- }
-}
-
-function onLabelClick() {
- // Activate other labels with the same text.
- for (label of labels_by_text[this.textContent]) {
- label.previousSibling.checked = true;
- }
-}
-document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/sphinx/_static/underscore-1.13.1.js b/sphinx/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af9648a47d389f2d6976d4aa1c44d7ce7ce..0000000000000000000000000000000000000000
--- a/sphinx/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
- typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
- typeof define === 'function' && define.amd ? define('underscore', factory) :
- (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
- var current = global._;
- var exports = global._ = factory();
- exports.noConflict = function () { global._ = current; return exports; };
- }()));
-}(this, (function () {
- // Underscore.js 1.13.1
- // https://underscorejs.org
- // (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
- // Underscore may be freely distributed under the MIT license.
-
- // Current version.
- var VERSION = '1.13.1';
-
- // Establish the root object, `window` (`self`) in the browser, `global`
- // on the server, or `this` in some virtual machines. We use `self`
- // instead of `window` for `WebWorker` support.
- var root = typeof self == 'object' && self.self === self && self ||
- typeof global == 'object' && global.global === global && global ||
- Function('return this')() ||
- {};
-
- // Save bytes in the minified (but not gzipped) version:
- var ArrayProto = Array.prototype, ObjProto = Object.prototype;
- var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
- // Create quick reference variables for speed access to core prototypes.
- var push = ArrayProto.push,
- slice = ArrayProto.slice,
- toString = ObjProto.toString,
- hasOwnProperty = ObjProto.hasOwnProperty;
-
- // Modern feature detection.
- var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
- supportsDataView = typeof DataView !== 'undefined';
-
- // All **ECMAScript 5+** native function implementations that we hope to use
- // are declared here.
- var nativeIsArray = Array.isArray,
- nativeKeys = Object.keys,
- nativeCreate = Object.create,
- nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
- // Create references to these builtin functions because we override them.
- var _isNaN = isNaN,
- _isFinite = isFinite;
-
- // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
- var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
- var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
- 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
- // The largest integer that can be represented exactly.
- var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
- // Some functions take a variable number of arguments, or a few expected
- // arguments at the beginning and then a variable number of values to operate
- // on. This helper accumulates all remaining arguments past the function’s
- // argument length (or an explicit `startIndex`), into an array that becomes
- // the last argument. Similar to ES6’s "rest parameter".
- function restArguments(func, startIndex) {
- startIndex = startIndex == null ? func.length - 1 : +startIndex;
- return function() {
- var length = Math.max(arguments.length - startIndex, 0),
- rest = Array(length),
- index = 0;
- for (; index < length; index++) {
- rest[index] = arguments[index + startIndex];
- }
- switch (startIndex) {
- case 0: return func.call(this, rest);
- case 1: return func.call(this, arguments[0], rest);
- case 2: return func.call(this, arguments[0], arguments[1], rest);
- }
- var args = Array(startIndex + 1);
- for (index = 0; index < startIndex; index++) {
- args[index] = arguments[index];
- }
- args[startIndex] = rest;
- return func.apply(this, args);
- };
- }
-
- // Is a given variable an object?
- function isObject(obj) {
- var type = typeof obj;
- return type === 'function' || type === 'object' && !!obj;
- }
-
- // Is a given value equal to null?
- function isNull(obj) {
- return obj === null;
- }
-
- // Is a given variable undefined?
- function isUndefined(obj) {
- return obj === void 0;
- }
-
- // Is a given value a boolean?
- function isBoolean(obj) {
- return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
- }
-
- // Is a given value a DOM element?
- function isElement(obj) {
- return !!(obj && obj.nodeType === 1);
- }
-
- // Internal function for creating a `toString`-based type tester.
- function tagTester(name) {
- var tag = '[object ' + name + ']';
- return function(obj) {
- return toString.call(obj) === tag;
- };
- }
-
- var isString = tagTester('String');
-
- var isNumber = tagTester('Number');
-
- var isDate = tagTester('Date');
-
- var isRegExp = tagTester('RegExp');
-
- var isError = tagTester('Error');
-
- var isSymbol = tagTester('Symbol');
-
- var isArrayBuffer = tagTester('ArrayBuffer');
-
- var isFunction = tagTester('Function');
-
- // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
- // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
- var nodelist = root.document && root.document.childNodes;
- if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
- isFunction = function(obj) {
- return typeof obj == 'function' || false;
- };
- }
-
- var isFunction$1 = isFunction;
-
- var hasObjectTag = tagTester('Object');
-
- // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
- // In IE 11, the most common among them, this problem also applies to
- // `Map`, `WeakMap` and `Set`.
- var hasStringTagBug = (
- supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
- ),
- isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
- var isDataView = tagTester('DataView');
-
- // In IE 10 - Edge 13, we need a different heuristic
- // to determine whether an object is a `DataView`.
- function ie10IsDataView(obj) {
- return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
- }
-
- var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
- // Is a given value an array?
- // Delegates to ECMA5's native `Array.isArray`.
- var isArray = nativeIsArray || tagTester('Array');
-
- // Internal function to check whether `key` is an own property name of `obj`.
- function has$1(obj, key) {
- return obj != null && hasOwnProperty.call(obj, key);
- }
-
- var isArguments = tagTester('Arguments');
-
- // Define a fallback version of the method in browsers (ahem, IE < 9), where
- // there isn't any inspectable "Arguments" type.
- (function() {
- if (!isArguments(arguments)) {
- isArguments = function(obj) {
- return has$1(obj, 'callee');
- };
- }
- }());
-
- var isArguments$1 = isArguments;
-
- // Is a given object a finite number?
- function isFinite$1(obj) {
- return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
- }
-
- // Is the given value `NaN`?
- function isNaN$1(obj) {
- return isNumber(obj) && _isNaN(obj);
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function constant(value) {
- return function() {
- return value;
- };
- }
-
- // Common internal logic for `isArrayLike` and `isBufferLike`.
- function createSizePropertyCheck(getSizeProperty) {
- return function(collection) {
- var sizeProperty = getSizeProperty(collection);
- return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
- }
- }
-
- // Internal helper to generate a function to obtain property `key` from `obj`.
- function shallowProperty(key) {
- return function(obj) {
- return obj == null ? void 0 : obj[key];
- };
- }
-
- // Internal helper to obtain the `byteLength` property of an object.
- var getByteLength = shallowProperty('byteLength');
-
- // Internal helper to determine whether we should spend extensive checks against
- // `ArrayBuffer` et al.
- var isBufferLike = createSizePropertyCheck(getByteLength);
-
- // Is a given value a typed array?
- var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
- function isTypedArray(obj) {
- // `ArrayBuffer.isView` is the most future-proof, so use it when available.
- // Otherwise, fall back on the above regular expression.
- return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
- isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
- }
-
- var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
- // Internal helper to obtain the `length` property of an object.
- var getLength = shallowProperty('length');
-
- // Internal helper to create a simple lookup structure.
- // `collectNonEnumProps` used to depend on `_.contains`, but this led to
- // circular imports. `emulatedSet` is a one-off solution that only works for
- // arrays of strings.
- function emulatedSet(keys) {
- var hash = {};
- for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
- return {
- contains: function(key) { return hash[key]; },
- push: function(key) {
- hash[key] = true;
- return keys.push(key);
- }
- };
- }
-
- // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
- // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
- // needed.
- function collectNonEnumProps(obj, keys) {
- keys = emulatedSet(keys);
- var nonEnumIdx = nonEnumerableProps.length;
- var constructor = obj.constructor;
- var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
- // Constructor is a special case.
- var prop = 'constructor';
- if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
- while (nonEnumIdx--) {
- prop = nonEnumerableProps[nonEnumIdx];
- if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
- keys.push(prop);
- }
- }
- }
-
- // Retrieve the names of an object's own properties.
- // Delegates to **ECMAScript 5**'s native `Object.keys`.
- function keys(obj) {
- if (!isObject(obj)) return [];
- if (nativeKeys) return nativeKeys(obj);
- var keys = [];
- for (var key in obj) if (has$1(obj, key)) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Is a given array, string, or object empty?
- // An "empty" object has no enumerable own-properties.
- function isEmpty(obj) {
- if (obj == null) return true;
- // Skip the more expensive `toString`-based type checks if `obj` has no
- // `.length`.
- var length = getLength(obj);
- if (typeof length == 'number' && (
- isArray(obj) || isString(obj) || isArguments$1(obj)
- )) return length === 0;
- return getLength(keys(obj)) === 0;
- }
-
- // Returns whether an object has a given set of `key:value` pairs.
- function isMatch(object, attrs) {
- var _keys = keys(attrs), length = _keys.length;
- if (object == null) return !length;
- var obj = Object(object);
- for (var i = 0; i < length; i++) {
- var key = _keys[i];
- if (attrs[key] !== obj[key] || !(key in obj)) return false;
- }
- return true;
- }
-
- // If Underscore is called as a function, it returns a wrapped object that can
- // be used OO-style. This wrapper holds altered versions of all functions added
- // through `_.mixin`. Wrapped objects may be chained.
- function _$1(obj) {
- if (obj instanceof _$1) return obj;
- if (!(this instanceof _$1)) return new _$1(obj);
- this._wrapped = obj;
- }
-
- _$1.VERSION = VERSION;
-
- // Extracts the result from a wrapped and chained object.
- _$1.prototype.value = function() {
- return this._wrapped;
- };
-
- // Provide unwrapping proxies for some methods used in engine operations
- // such as arithmetic and JSON stringification.
- _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
- _$1.prototype.toString = function() {
- return String(this._wrapped);
- };
-
- // Internal function to wrap or shallow-copy an ArrayBuffer,
- // typed array or DataView to a new view, reusing the buffer.
- function toBufferView(bufferSource) {
- return new Uint8Array(
- bufferSource.buffer || bufferSource,
- bufferSource.byteOffset || 0,
- getByteLength(bufferSource)
- );
- }
-
- // We use this string twice, so give it a name for minification.
- var tagDataView = '[object DataView]';
-
- // Internal recursive comparison function for `_.isEqual`.
- function eq(a, b, aStack, bStack) {
- // Identical objects are equal. `0 === -0`, but they aren't identical.
- // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
- if (a === b) return a !== 0 || 1 / a === 1 / b;
- // `null` or `undefined` only equal to itself (strict comparison).
- if (a == null || b == null) return false;
- // `NaN`s are equivalent, but non-reflexive.
- if (a !== a) return b !== b;
- // Exhaust primitive checks
- var type = typeof a;
- if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
- return deepEq(a, b, aStack, bStack);
- }
-
- // Internal recursive comparison function for `_.isEqual`.
- function deepEq(a, b, aStack, bStack) {
- // Unwrap any wrapped objects.
- if (a instanceof _$1) a = a._wrapped;
- if (b instanceof _$1) b = b._wrapped;
- // Compare `[[Class]]` names.
- var className = toString.call(a);
- if (className !== toString.call(b)) return false;
- // Work around a bug in IE 10 - Edge 13.
- if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
- if (!isDataView$1(b)) return false;
- className = tagDataView;
- }
- switch (className) {
- // These types are compared by value.
- case '[object RegExp]':
- // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
- case '[object String]':
- // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
- // equivalent to `new String("5")`.
- return '' + a === '' + b;
- case '[object Number]':
- // `NaN`s are equivalent, but non-reflexive.
- // Object(NaN) is equivalent to NaN.
- if (+a !== +a) return +b !== +b;
- // An `egal` comparison is performed for other numeric values.
- return +a === 0 ? 1 / +a === 1 / b : +a === +b;
- case '[object Date]':
- case '[object Boolean]':
- // Coerce dates and booleans to numeric primitive values. Dates are compared by their
- // millisecond representations. Note that invalid dates with millisecond representations
- // of `NaN` are not equivalent.
- return +a === +b;
- case '[object Symbol]':
- return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
- case '[object ArrayBuffer]':
- case tagDataView:
- // Coerce to typed array so we can fall through.
- return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
- }
-
- var areArrays = className === '[object Array]';
- if (!areArrays && isTypedArray$1(a)) {
- var byteLength = getByteLength(a);
- if (byteLength !== getByteLength(b)) return false;
- if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
- areArrays = true;
- }
- if (!areArrays) {
- if (typeof a != 'object' || typeof b != 'object') return false;
-
- // Objects with different constructors are not equivalent, but `Object`s or `Array`s
- // from different frames are.
- var aCtor = a.constructor, bCtor = b.constructor;
- if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
- isFunction$1(bCtor) && bCtor instanceof bCtor)
- && ('constructor' in a && 'constructor' in b)) {
- return false;
- }
- }
- // Assume equality for cyclic structures. The algorithm for detecting cyclic
- // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
- // Initializing stack of traversed objects.
- // It's done here since we only need them for objects and arrays comparison.
- aStack = aStack || [];
- bStack = bStack || [];
- var length = aStack.length;
- while (length--) {
- // Linear search. Performance is inversely proportional to the number of
- // unique nested structures.
- if (aStack[length] === a) return bStack[length] === b;
- }
-
- // Add the first object to the stack of traversed objects.
- aStack.push(a);
- bStack.push(b);
-
- // Recursively compare objects and arrays.
- if (areArrays) {
- // Compare array lengths to determine if a deep comparison is necessary.
- length = a.length;
- if (length !== b.length) return false;
- // Deep compare the contents, ignoring non-numeric properties.
- while (length--) {
- if (!eq(a[length], b[length], aStack, bStack)) return false;
- }
- } else {
- // Deep compare objects.
- var _keys = keys(a), key;
- length = _keys.length;
- // Ensure that both objects contain the same number of properties before comparing deep equality.
- if (keys(b).length !== length) return false;
- while (length--) {
- // Deep compare each member
- key = _keys[length];
- if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
- }
- }
- // Remove the first object from the stack of traversed objects.
- aStack.pop();
- bStack.pop();
- return true;
- }
-
- // Perform a deep comparison to check if two objects are equal.
- function isEqual(a, b) {
- return eq(a, b);
- }
-
- // Retrieve all the enumerable property names of an object.
- function allKeys(obj) {
- if (!isObject(obj)) return [];
- var keys = [];
- for (var key in obj) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Since the regular `Object.prototype.toString` type tests don't work for
- // some types in IE 11, we use a fingerprinting heuristic instead, based
- // on the methods. It's not great, but it's the best we got.
- // The fingerprint method lists are defined below.
- function ie11fingerprint(methods) {
- var length = getLength(methods);
- return function(obj) {
- if (obj == null) return false;
- // `Map`, `WeakMap` and `Set` have no enumerable keys.
- var keys = allKeys(obj);
- if (getLength(keys)) return false;
- for (var i = 0; i < length; i++) {
- if (!isFunction$1(obj[methods[i]])) return false;
- }
- // If we are testing against `WeakMap`, we need to ensure that
- // `obj` doesn't have a `forEach` method in order to distinguish
- // it from a regular `Map`.
- return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
- };
- }
-
- // In the interest of compact minification, we write
- // each string in the fingerprints only once.
- var forEachName = 'forEach',
- hasName = 'has',
- commonInit = ['clear', 'delete'],
- mapTail = ['get', hasName, 'set'];
-
- // `Map`, `WeakMap` and `Set` each have slightly different
- // combinations of the above sublists.
- var mapMethods = commonInit.concat(forEachName, mapTail),
- weakMapMethods = commonInit.concat(mapTail),
- setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
- var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
- var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
- var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
- var isWeakSet = tagTester('WeakSet');
-
- // Retrieve the values of an object's properties.
- function values(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var values = Array(length);
- for (var i = 0; i < length; i++) {
- values[i] = obj[_keys[i]];
- }
- return values;
- }
-
- // Convert an object into a list of `[key, value]` pairs.
- // The opposite of `_.object` with one argument.
- function pairs(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var pairs = Array(length);
- for (var i = 0; i < length; i++) {
- pairs[i] = [_keys[i], obj[_keys[i]]];
- }
- return pairs;
- }
-
- // Invert the keys and values of an object. The values must be serializable.
- function invert(obj) {
- var result = {};
- var _keys = keys(obj);
- for (var i = 0, length = _keys.length; i < length; i++) {
- result[obj[_keys[i]]] = _keys[i];
- }
- return result;
- }
-
- // Return a sorted list of the function names available on the object.
- function functions(obj) {
- var names = [];
- for (var key in obj) {
- if (isFunction$1(obj[key])) names.push(key);
- }
- return names.sort();
- }
-
- // An internal function for creating assigner functions.
- function createAssigner(keysFunc, defaults) {
- return function(obj) {
- var length = arguments.length;
- if (defaults) obj = Object(obj);
- if (length < 2 || obj == null) return obj;
- for (var index = 1; index < length; index++) {
- var source = arguments[index],
- keys = keysFunc(source),
- l = keys.length;
- for (var i = 0; i < l; i++) {
- var key = keys[i];
- if (!defaults || obj[key] === void 0) obj[key] = source[key];
- }
- }
- return obj;
- };
- }
-
- // Extend a given object with all the properties in passed-in object(s).
- var extend = createAssigner(allKeys);
-
- // Assigns a given object with all the own properties in the passed-in
- // object(s).
- // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
- var extendOwn = createAssigner(keys);
-
- // Fill in a given object with default properties.
- var defaults = createAssigner(allKeys, true);
-
- // Create a naked function reference for surrogate-prototype-swapping.
- function ctor() {
- return function(){};
- }
-
- // An internal function for creating a new object that inherits from another.
- function baseCreate(prototype) {
- if (!isObject(prototype)) return {};
- if (nativeCreate) return nativeCreate(prototype);
- var Ctor = ctor();
- Ctor.prototype = prototype;
- var result = new Ctor;
- Ctor.prototype = null;
- return result;
- }
-
- // Creates an object that inherits from the given prototype object.
- // If additional properties are provided then they will be added to the
- // created object.
- function create(prototype, props) {
- var result = baseCreate(prototype);
- if (props) extendOwn(result, props);
- return result;
- }
-
- // Create a (shallow-cloned) duplicate of an object.
- function clone(obj) {
- if (!isObject(obj)) return obj;
- return isArray(obj) ? obj.slice() : extend({}, obj);
- }
-
- // Invokes `interceptor` with the `obj` and then returns `obj`.
- // The primary purpose of this method is to "tap into" a method chain, in
- // order to perform operations on intermediate results within the chain.
- function tap(obj, interceptor) {
- interceptor(obj);
- return obj;
- }
-
- // Normalize a (deep) property `path` to array.
- // Like `_.iteratee`, this function can be customized.
- function toPath$1(path) {
- return isArray(path) ? path : [path];
- }
- _$1.toPath = toPath$1;
-
- // Internal wrapper for `_.toPath` to enable minification.
- // Similar to `cb` for `_.iteratee`.
- function toPath(path) {
- return _$1.toPath(path);
- }
-
- // Internal function to obtain a nested property in `obj` along `path`.
- function deepGet(obj, path) {
- var length = path.length;
- for (var i = 0; i < length; i++) {
- if (obj == null) return void 0;
- obj = obj[path[i]];
- }
- return length ? obj : void 0;
- }
-
- // Get the value of the (deep) property on `path` from `object`.
- // If any property in `path` does not exist or if the value is
- // `undefined`, return `defaultValue` instead.
- // The `path` is normalized through `_.toPath`.
- function get(object, path, defaultValue) {
- var value = deepGet(object, toPath(path));
- return isUndefined(value) ? defaultValue : value;
- }
-
- // Shortcut function for checking if an object has a given property directly on
- // itself (in other words, not on a prototype). Unlike the internal `has`
- // function, this public version can also traverse nested properties.
- function has(obj, path) {
- path = toPath(path);
- var length = path.length;
- for (var i = 0; i < length; i++) {
- var key = path[i];
- if (!has$1(obj, key)) return false;
- obj = obj[key];
- }
- return !!length;
- }
-
- // Keep the identity function around for default iteratees.
- function identity(value) {
- return value;
- }
-
- // Returns a predicate for checking whether an object has a given set of
- // `key:value` pairs.
- function matcher(attrs) {
- attrs = extendOwn({}, attrs);
- return function(obj) {
- return isMatch(obj, attrs);
- };
- }
-
- // Creates a function that, when passed an object, will traverse that object’s
- // properties down the given `path`, specified as an array of keys or indices.
- function property(path) {
- path = toPath(path);
- return function(obj) {
- return deepGet(obj, path);
- };
- }
-
- // Internal function that returns an efficient (for current engines) version
- // of the passed-in callback, to be repeatedly applied in other Underscore
- // functions.
- function optimizeCb(func, context, argCount) {
- if (context === void 0) return func;
- switch (argCount == null ? 3 : argCount) {
- case 1: return function(value) {
- return func.call(context, value);
- };
- // The 2-argument case is omitted because we’re not using it.
- case 3: return function(value, index, collection) {
- return func.call(context, value, index, collection);
- };
- case 4: return function(accumulator, value, index, collection) {
- return func.call(context, accumulator, value, index, collection);
- };
- }
- return function() {
- return func.apply(context, arguments);
- };
- }
-
- // An internal function to generate callbacks that can be applied to each
- // element in a collection, returning the desired result — either `_.identity`,
- // an arbitrary callback, a property matcher, or a property accessor.
- function baseIteratee(value, context, argCount) {
- if (value == null) return identity;
- if (isFunction$1(value)) return optimizeCb(value, context, argCount);
- if (isObject(value) && !isArray(value)) return matcher(value);
- return property(value);
- }
-
- // External wrapper for our callback generator. Users may customize
- // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
- // This abstraction hides the internal-only `argCount` argument.
- function iteratee(value, context) {
- return baseIteratee(value, context, Infinity);
- }
- _$1.iteratee = iteratee;
-
- // The function we call internally to generate a callback. It invokes
- // `_.iteratee` if overridden, otherwise `baseIteratee`.
- function cb(value, context, argCount) {
- if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
- return baseIteratee(value, context, argCount);
- }
-
- // Returns the results of applying the `iteratee` to each element of `obj`.
- // In contrast to `_.map` it returns an object.
- function mapObject(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = keys(obj),
- length = _keys.length,
- results = {};
- for (var index = 0; index < length; index++) {
- var currentKey = _keys[index];
- results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function noop(){}
-
- // Generates a function for a given object that returns a given property.
- function propertyOf(obj) {
- if (obj == null) return noop;
- return function(path) {
- return get(obj, path);
- };
- }
-
- // Run a function **n** times.
- function times(n, iteratee, context) {
- var accum = Array(Math.max(0, n));
- iteratee = optimizeCb(iteratee, context, 1);
- for (var i = 0; i < n; i++) accum[i] = iteratee(i);
- return accum;
- }
-
- // Return a random integer between `min` and `max` (inclusive).
- function random(min, max) {
- if (max == null) {
- max = min;
- min = 0;
- }
- return min + Math.floor(Math.random() * (max - min + 1));
- }
-
- // A (possibly faster) way to get the current timestamp as an integer.
- var now = Date.now || function() {
- return new Date().getTime();
- };
-
- // Internal helper to generate functions for escaping and unescaping strings
- // to/from HTML interpolation.
- function createEscaper(map) {
- var escaper = function(match) {
- return map[match];
- };
- // Regexes for identifying a key that needs to be escaped.
- var source = '(?:' + keys(map).join('|') + ')';
- var testRegexp = RegExp(source);
- var replaceRegexp = RegExp(source, 'g');
- return function(string) {
- string = string == null ? '' : '' + string;
- return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
- };
- }
-
- // Internal list of HTML entities for escaping.
- var escapeMap = {
- '&': '&',
- '<': '<',
- '>': '>',
- '"': '"',
- "'": ''',
- '`': '`'
- };
-
- // Function for escaping strings to HTML interpolation.
- var _escape = createEscaper(escapeMap);
-
- // Internal list of HTML entities for unescaping.
- var unescapeMap = invert(escapeMap);
-
- // Function for unescaping strings from HTML interpolation.
- var _unescape = createEscaper(unescapeMap);
-
- // By default, Underscore uses ERB-style template delimiters. Change the
- // following template settings to use alternative delimiters.
- var templateSettings = _$1.templateSettings = {
- evaluate: /<%([\s\S]+?)%>/g,
- interpolate: /<%=([\s\S]+?)%>/g,
- escape: /<%-([\s\S]+?)%>/g
- };
-
- // When customizing `_.templateSettings`, if you don't want to define an
- // interpolation, evaluation or escaping regex, we need one that is
- // guaranteed not to match.
- var noMatch = /(.)^/;
-
- // Certain characters need to be escaped so that they can be put into a
- // string literal.
- var escapes = {
- "'": "'",
- '\\': '\\',
- '\r': 'r',
- '\n': 'n',
- '\u2028': 'u2028',
- '\u2029': 'u2029'
- };
-
- var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
- function escapeChar(match) {
- return '\\' + escapes[match];
- }
-
- // In order to prevent third-party code injection through
- // `_.templateSettings.variable`, we test it against the following regular
- // expression. It is intentionally a bit more liberal than just matching valid
- // identifiers, but still prevents possible loopholes through defaults or
- // destructuring assignment.
- var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
- // JavaScript micro-templating, similar to John Resig's implementation.
- // Underscore templating handles arbitrary delimiters, preserves whitespace,
- // and correctly escapes quotes within interpolated code.
- // NB: `oldSettings` only exists for backwards compatibility.
- function template(text, settings, oldSettings) {
- if (!settings && oldSettings) settings = oldSettings;
- settings = defaults({}, settings, _$1.templateSettings);
-
- // Combine delimiters into one regular expression via alternation.
- var matcher = RegExp([
- (settings.escape || noMatch).source,
- (settings.interpolate || noMatch).source,
- (settings.evaluate || noMatch).source
- ].join('|') + '|$', 'g');
-
- // Compile the template source, escaping string literals appropriately.
- var index = 0;
- var source = "__p+='";
- text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
- source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
- index = offset + match.length;
-
- if (escape) {
- source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
- } else if (interpolate) {
- source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
- } else if (evaluate) {
- source += "';\n" + evaluate + "\n__p+='";
- }
-
- // Adobe VMs need the match returned to produce the correct offset.
- return match;
- });
- source += "';\n";
-
- var argument = settings.variable;
- if (argument) {
- // Insure against third-party code injection. (CVE-2021-23358)
- if (!bareIdentifier.test(argument)) throw new Error(
- 'variable is not a bare identifier: ' + argument
- );
- } else {
- // If a variable is not specified, place data values in local scope.
- source = 'with(obj||{}){\n' + source + '}\n';
- argument = 'obj';
- }
-
- source = "var __t,__p='',__j=Array.prototype.join," +
- "print=function(){__p+=__j.call(arguments,'');};\n" +
- source + 'return __p;\n';
-
- var render;
- try {
- render = new Function(argument, '_', source);
- } catch (e) {
- e.source = source;
- throw e;
- }
-
- var template = function(data) {
- return render.call(this, data, _$1);
- };
-
- // Provide the compiled source as a convenience for precompilation.
- template.source = 'function(' + argument + '){\n' + source + '}';
-
- return template;
- }
-
- // Traverses the children of `obj` along `path`. If a child is a function, it
- // is invoked with its parent as context. Returns the value of the final
- // child, or `fallback` if any child is undefined.
- function result(obj, path, fallback) {
- path = toPath(path);
- var length = path.length;
- if (!length) {
- return isFunction$1(fallback) ? fallback.call(obj) : fallback;
- }
- for (var i = 0; i < length; i++) {
- var prop = obj == null ? void 0 : obj[path[i]];
- if (prop === void 0) {
- prop = fallback;
- i = length; // Ensure we don't continue iterating.
- }
- obj = isFunction$1(prop) ? prop.call(obj) : prop;
- }
- return obj;
- }
-
- // Generate a unique integer id (unique within the entire client session).
- // Useful for temporary DOM ids.
- var idCounter = 0;
- function uniqueId(prefix) {
- var id = ++idCounter + '';
- return prefix ? prefix + id : id;
- }
-
- // Start chaining a wrapped Underscore object.
- function chain(obj) {
- var instance = _$1(obj);
- instance._chain = true;
- return instance;
- }
-
- // Internal function to execute `sourceFunc` bound to `context` with optional
- // `args`. Determines whether to execute a function as a constructor or as a
- // normal function.
- function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
- if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
- var self = baseCreate(sourceFunc.prototype);
- var result = sourceFunc.apply(self, args);
- if (isObject(result)) return result;
- return self;
- }
-
- // Partially apply a function by creating a version that has had some of its
- // arguments pre-filled, without changing its dynamic `this` context. `_` acts
- // as a placeholder by default, allowing any combination of arguments to be
- // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
- var partial = restArguments(function(func, boundArgs) {
- var placeholder = partial.placeholder;
- var bound = function() {
- var position = 0, length = boundArgs.length;
- var args = Array(length);
- for (var i = 0; i < length; i++) {
- args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
- }
- while (position < arguments.length) args.push(arguments[position++]);
- return executeBound(func, bound, this, this, args);
- };
- return bound;
- });
-
- partial.placeholder = _$1;
-
- // Create a function bound to a given object (assigning `this`, and arguments,
- // optionally).
- var bind = restArguments(function(func, context, args) {
- if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
- var bound = restArguments(function(callArgs) {
- return executeBound(func, bound, context, this, args.concat(callArgs));
- });
- return bound;
- });
-
- // Internal helper for collection methods to determine whether a collection
- // should be iterated as an array or as an object.
- // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
- // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
- var isArrayLike = createSizePropertyCheck(getLength);
-
- // Internal implementation of a recursive `flatten` function.
- function flatten$1(input, depth, strict, output) {
- output = output || [];
- if (!depth && depth !== 0) {
- depth = Infinity;
- } else if (depth <= 0) {
- return output.concat(input);
- }
- var idx = output.length;
- for (var i = 0, length = getLength(input); i < length; i++) {
- var value = input[i];
- if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
- // Flatten current level of array or arguments object.
- if (depth > 1) {
- flatten$1(value, depth - 1, strict, output);
- idx = output.length;
- } else {
- var j = 0, len = value.length;
- while (j < len) output[idx++] = value[j++];
- }
- } else if (!strict) {
- output[idx++] = value;
- }
- }
- return output;
- }
-
- // Bind a number of an object's methods to that object. Remaining arguments
- // are the method names to be bound. Useful for ensuring that all callbacks
- // defined on an object belong to it.
- var bindAll = restArguments(function(obj, keys) {
- keys = flatten$1(keys, false, false);
- var index = keys.length;
- if (index < 1) throw new Error('bindAll must be passed function names');
- while (index--) {
- var key = keys[index];
- obj[key] = bind(obj[key], obj);
- }
- return obj;
- });
-
- // Memoize an expensive function by storing its results.
- function memoize(func, hasher) {
- var memoize = function(key) {
- var cache = memoize.cache;
- var address = '' + (hasher ? hasher.apply(this, arguments) : key);
- if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
- return cache[address];
- };
- memoize.cache = {};
- return memoize;
- }
-
- // Delays a function for the given number of milliseconds, and then calls
- // it with the arguments supplied.
- var delay = restArguments(function(func, wait, args) {
- return setTimeout(function() {
- return func.apply(null, args);
- }, wait);
- });
-
- // Defers a function, scheduling it to run after the current call stack has
- // cleared.
- var defer = partial(delay, _$1, 1);
-
- // Returns a function, that, when invoked, will only be triggered at most once
- // during a given window of time. Normally, the throttled function will run
- // as much as it can, without ever going more than once per `wait` duration;
- // but if you'd like to disable the execution on the leading edge, pass
- // `{leading: false}`. To disable execution on the trailing edge, ditto.
- function throttle(func, wait, options) {
- var timeout, context, args, result;
- var previous = 0;
- if (!options) options = {};
-
- var later = function() {
- previous = options.leading === false ? 0 : now();
- timeout = null;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- };
-
- var throttled = function() {
- var _now = now();
- if (!previous && options.leading === false) previous = _now;
- var remaining = wait - (_now - previous);
- context = this;
- args = arguments;
- if (remaining <= 0 || remaining > wait) {
- if (timeout) {
- clearTimeout(timeout);
- timeout = null;
- }
- previous = _now;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- } else if (!timeout && options.trailing !== false) {
- timeout = setTimeout(later, remaining);
- }
- return result;
- };
-
- throttled.cancel = function() {
- clearTimeout(timeout);
- previous = 0;
- timeout = context = args = null;
- };
-
- return throttled;
- }
-
- // When a sequence of calls of the returned function ends, the argument
- // function is triggered. The end of a sequence is defined by the `wait`
- // parameter. If `immediate` is passed, the argument function will be
- // triggered at the beginning of the sequence instead of at the end.
- function debounce(func, wait, immediate) {
- var timeout, previous, args, result, context;
-
- var later = function() {
- var passed = now() - previous;
- if (wait > passed) {
- timeout = setTimeout(later, wait - passed);
- } else {
- timeout = null;
- if (!immediate) result = func.apply(context, args);
- // This check is needed because `func` can recursively invoke `debounced`.
- if (!timeout) args = context = null;
- }
- };
-
- var debounced = restArguments(function(_args) {
- context = this;
- args = _args;
- previous = now();
- if (!timeout) {
- timeout = setTimeout(later, wait);
- if (immediate) result = func.apply(context, args);
- }
- return result;
- });
-
- debounced.cancel = function() {
- clearTimeout(timeout);
- timeout = args = context = null;
- };
-
- return debounced;
- }
-
- // Returns the first function passed as an argument to the second,
- // allowing you to adjust arguments, run code before and after, and
- // conditionally execute the original function.
- function wrap(func, wrapper) {
- return partial(wrapper, func);
- }
-
- // Returns a negated version of the passed-in predicate.
- function negate(predicate) {
- return function() {
- return !predicate.apply(this, arguments);
- };
- }
-
- // Returns a function that is the composition of a list of functions, each
- // consuming the return value of the function that follows.
- function compose() {
- var args = arguments;
- var start = args.length - 1;
- return function() {
- var i = start;
- var result = args[start].apply(this, arguments);
- while (i--) result = args[i].call(this, result);
- return result;
- };
- }
-
- // Returns a function that will only be executed on and after the Nth call.
- function after(times, func) {
- return function() {
- if (--times < 1) {
- return func.apply(this, arguments);
- }
- };
- }
-
- // Returns a function that will only be executed up to (but not including) the
- // Nth call.
- function before(times, func) {
- var memo;
- return function() {
- if (--times > 0) {
- memo = func.apply(this, arguments);
- }
- if (times <= 1) func = null;
- return memo;
- };
- }
-
- // Returns a function that will be executed at most one time, no matter how
- // often you call it. Useful for lazy initialization.
- var once = partial(before, 2);
-
- // Returns the first key on an object that passes a truth test.
- function findKey(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = keys(obj), key;
- for (var i = 0, length = _keys.length; i < length; i++) {
- key = _keys[i];
- if (predicate(obj[key], key, obj)) return key;
- }
- }
-
- // Internal function to generate `_.findIndex` and `_.findLastIndex`.
- function createPredicateIndexFinder(dir) {
- return function(array, predicate, context) {
- predicate = cb(predicate, context);
- var length = getLength(array);
- var index = dir > 0 ? 0 : length - 1;
- for (; index >= 0 && index < length; index += dir) {
- if (predicate(array[index], index, array)) return index;
- }
- return -1;
- };
- }
-
- // Returns the first index on an array-like that passes a truth test.
- var findIndex = createPredicateIndexFinder(1);
-
- // Returns the last index on an array-like that passes a truth test.
- var findLastIndex = createPredicateIndexFinder(-1);
-
- // Use a comparator function to figure out the smallest index at which
- // an object should be inserted so as to maintain order. Uses binary search.
- function sortedIndex(array, obj, iteratee, context) {
- iteratee = cb(iteratee, context, 1);
- var value = iteratee(obj);
- var low = 0, high = getLength(array);
- while (low < high) {
- var mid = Math.floor((low + high) / 2);
- if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
- }
- return low;
- }
-
- // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
- function createIndexFinder(dir, predicateFind, sortedIndex) {
- return function(array, item, idx) {
- var i = 0, length = getLength(array);
- if (typeof idx == 'number') {
- if (dir > 0) {
- i = idx >= 0 ? idx : Math.max(idx + length, i);
- } else {
- length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
- }
- } else if (sortedIndex && idx && length) {
- idx = sortedIndex(array, item);
- return array[idx] === item ? idx : -1;
- }
- if (item !== item) {
- idx = predicateFind(slice.call(array, i, length), isNaN$1);
- return idx >= 0 ? idx + i : -1;
- }
- for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
- if (array[idx] === item) return idx;
- }
- return -1;
- };
- }
-
- // Return the position of the first occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- // If the array is large and already in sort order, pass `true`
- // for **isSorted** to use binary search.
- var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
- // Return the position of the last occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
- // Return the first value which passes a truth test.
- function find(obj, predicate, context) {
- var keyFinder = isArrayLike(obj) ? findIndex : findKey;
- var key = keyFinder(obj, predicate, context);
- if (key !== void 0 && key !== -1) return obj[key];
- }
-
- // Convenience version of a common use case of `_.find`: getting the first
- // object containing specific `key:value` pairs.
- function findWhere(obj, attrs) {
- return find(obj, matcher(attrs));
- }
-
- // The cornerstone for collection functions, an `each`
- // implementation, aka `forEach`.
- // Handles raw objects in addition to array-likes. Treats all
- // sparse array-likes as if they were dense.
- function each(obj, iteratee, context) {
- iteratee = optimizeCb(iteratee, context);
- var i, length;
- if (isArrayLike(obj)) {
- for (i = 0, length = obj.length; i < length; i++) {
- iteratee(obj[i], i, obj);
- }
- } else {
- var _keys = keys(obj);
- for (i = 0, length = _keys.length; i < length; i++) {
- iteratee(obj[_keys[i]], _keys[i], obj);
- }
- }
- return obj;
- }
-
- // Return the results of applying the iteratee to each element.
- function map(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- results = Array(length);
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- results[index] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Internal helper to create a reducing function, iterating left or right.
- function createReduce(dir) {
- // Wrap code that reassigns argument variables in a separate function than
- // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
- var reducer = function(obj, iteratee, memo, initial) {
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- index = dir > 0 ? 0 : length - 1;
- if (!initial) {
- memo = obj[_keys ? _keys[index] : index];
- index += dir;
- }
- for (; index >= 0 && index < length; index += dir) {
- var currentKey = _keys ? _keys[index] : index;
- memo = iteratee(memo, obj[currentKey], currentKey, obj);
- }
- return memo;
- };
-
- return function(obj, iteratee, memo, context) {
- var initial = arguments.length >= 3;
- return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
- };
- }
-
- // **Reduce** builds up a single result from a list of values, aka `inject`,
- // or `foldl`.
- var reduce = createReduce(1);
-
- // The right-associative version of reduce, also known as `foldr`.
- var reduceRight = createReduce(-1);
-
- // Return all the elements that pass a truth test.
- function filter(obj, predicate, context) {
- var results = [];
- predicate = cb(predicate, context);
- each(obj, function(value, index, list) {
- if (predicate(value, index, list)) results.push(value);
- });
- return results;
- }
-
- // Return all the elements for which a truth test fails.
- function reject(obj, predicate, context) {
- return filter(obj, negate(cb(predicate)), context);
- }
-
- // Determine whether all of the elements pass a truth test.
- function every(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (!predicate(obj[currentKey], currentKey, obj)) return false;
- }
- return true;
- }
-
- // Determine if at least one element in the object passes a truth test.
- function some(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (predicate(obj[currentKey], currentKey, obj)) return true;
- }
- return false;
- }
-
- // Determine if the array or object contains a given item (using `===`).
- function contains(obj, item, fromIndex, guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- if (typeof fromIndex != 'number' || guard) fromIndex = 0;
- return indexOf(obj, item, fromIndex) >= 0;
- }
-
- // Invoke a method (with arguments) on every item in a collection.
- var invoke = restArguments(function(obj, path, args) {
- var contextPath, func;
- if (isFunction$1(path)) {
- func = path;
- } else {
- path = toPath(path);
- contextPath = path.slice(0, -1);
- path = path[path.length - 1];
- }
- return map(obj, function(context) {
- var method = func;
- if (!method) {
- if (contextPath && contextPath.length) {
- context = deepGet(context, contextPath);
- }
- if (context == null) return void 0;
- method = context[path];
- }
- return method == null ? method : method.apply(context, args);
- });
- });
-
- // Convenience version of a common use case of `_.map`: fetching a property.
- function pluck(obj, key) {
- return map(obj, property(key));
- }
-
- // Convenience version of a common use case of `_.filter`: selecting only
- // objects containing specific `key:value` pairs.
- function where(obj, attrs) {
- return filter(obj, matcher(attrs));
- }
-
- // Return the maximum element (or element-based computation).
- function max(obj, iteratee, context) {
- var result = -Infinity, lastComputed = -Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value > result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Return the minimum element (or element-based computation).
- function min(obj, iteratee, context) {
- var result = Infinity, lastComputed = Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value < result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed < lastComputed || computed === Infinity && result === Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Sample **n** random values from a collection using the modern version of the
- // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
- // If **n** is not specified, returns a single random element.
- // The internal `guard` argument allows it to work with `_.map`.
- function sample(obj, n, guard) {
- if (n == null || guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- return obj[random(obj.length - 1)];
- }
- var sample = isArrayLike(obj) ? clone(obj) : values(obj);
- var length = getLength(sample);
- n = Math.max(Math.min(n, length), 0);
- var last = length - 1;
- for (var index = 0; index < n; index++) {
- var rand = random(index, last);
- var temp = sample[index];
- sample[index] = sample[rand];
- sample[rand] = temp;
- }
- return sample.slice(0, n);
- }
-
- // Shuffle a collection.
- function shuffle(obj) {
- return sample(obj, Infinity);
- }
-
- // Sort the object's values by a criterion produced by an iteratee.
- function sortBy(obj, iteratee, context) {
- var index = 0;
- iteratee = cb(iteratee, context);
- return pluck(map(obj, function(value, key, list) {
- return {
- value: value,
- index: index++,
- criteria: iteratee(value, key, list)
- };
- }).sort(function(left, right) {
- var a = left.criteria;
- var b = right.criteria;
- if (a !== b) {
- if (a > b || a === void 0) return 1;
- if (a < b || b === void 0) return -1;
- }
- return left.index - right.index;
- }), 'value');
- }
-
- // An internal function used for aggregate "group by" operations.
- function group(behavior, partition) {
- return function(obj, iteratee, context) {
- var result = partition ? [[], []] : {};
- iteratee = cb(iteratee, context);
- each(obj, function(value, index) {
- var key = iteratee(value, index, obj);
- behavior(result, value, key);
- });
- return result;
- };
- }
-
- // Groups the object's values by a criterion. Pass either a string attribute
- // to group by, or a function that returns the criterion.
- var groupBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key].push(value); else result[key] = [value];
- });
-
- // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
- // when you know that your index values will be unique.
- var indexBy = group(function(result, value, key) {
- result[key] = value;
- });
-
- // Counts instances of an object that group by a certain criterion. Pass
- // either a string attribute to count by, or a function that returns the
- // criterion.
- var countBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key]++; else result[key] = 1;
- });
-
- // Split a collection into two arrays: one whose elements all pass the given
- // truth test, and one whose elements all do not pass the truth test.
- var partition = group(function(result, value, pass) {
- result[pass ? 0 : 1].push(value);
- }, true);
-
- // Safely create a real, live array from anything iterable.
- var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
- function toArray(obj) {
- if (!obj) return [];
- if (isArray(obj)) return slice.call(obj);
- if (isString(obj)) {
- // Keep surrogate pair characters together.
- return obj.match(reStrSymbol);
- }
- if (isArrayLike(obj)) return map(obj, identity);
- return values(obj);
- }
-
- // Return the number of elements in a collection.
- function size(obj) {
- if (obj == null) return 0;
- return isArrayLike(obj) ? obj.length : keys(obj).length;
- }
-
- // Internal `_.pick` helper function to determine whether `key` is an enumerable
- // property name of `obj`.
- function keyInObj(value, key, obj) {
- return key in obj;
- }
-
- // Return a copy of the object only containing the allowed properties.
- var pick = restArguments(function(obj, keys) {
- var result = {}, iteratee = keys[0];
- if (obj == null) return result;
- if (isFunction$1(iteratee)) {
- if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
- keys = allKeys(obj);
- } else {
- iteratee = keyInObj;
- keys = flatten$1(keys, false, false);
- obj = Object(obj);
- }
- for (var i = 0, length = keys.length; i < length; i++) {
- var key = keys[i];
- var value = obj[key];
- if (iteratee(value, key, obj)) result[key] = value;
- }
- return result;
- });
-
- // Return a copy of the object without the disallowed properties.
- var omit = restArguments(function(obj, keys) {
- var iteratee = keys[0], context;
- if (isFunction$1(iteratee)) {
- iteratee = negate(iteratee);
- if (keys.length > 1) context = keys[1];
- } else {
- keys = map(flatten$1(keys, false, false), String);
- iteratee = function(value, key) {
- return !contains(keys, key);
- };
- }
- return pick(obj, iteratee, context);
- });
-
- // Returns everything but the last entry of the array. Especially useful on
- // the arguments object. Passing **n** will return all the values in
- // the array, excluding the last N.
- function initial(array, n, guard) {
- return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
- }
-
- // Get the first element of an array. Passing **n** will return the first N
- // values in the array. The **guard** check allows it to work with `_.map`.
- function first(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[0];
- return initial(array, array.length - n);
- }
-
- // Returns everything but the first entry of the `array`. Especially useful on
- // the `arguments` object. Passing an **n** will return the rest N values in the
- // `array`.
- function rest(array, n, guard) {
- return slice.call(array, n == null || guard ? 1 : n);
- }
-
- // Get the last element of an array. Passing **n** will return the last N
- // values in the array.
- function last(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[array.length - 1];
- return rest(array, Math.max(0, array.length - n));
- }
-
- // Trim out all falsy values from an array.
- function compact(array) {
- return filter(array, Boolean);
- }
-
- // Flatten out an array, either recursively (by default), or up to `depth`.
- // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
- function flatten(array, depth) {
- return flatten$1(array, depth, false);
- }
-
- // Take the difference between one array and a number of other arrays.
- // Only the elements present in just the first array will remain.
- var difference = restArguments(function(array, rest) {
- rest = flatten$1(rest, true, true);
- return filter(array, function(value){
- return !contains(rest, value);
- });
- });
-
- // Return a version of the array that does not contain the specified value(s).
- var without = restArguments(function(array, otherArrays) {
- return difference(array, otherArrays);
- });
-
- // Produce a duplicate-free version of the array. If the array has already
- // been sorted, you have the option of using a faster algorithm.
- // The faster algorithm will not work with an iteratee if the iteratee
- // is not a one-to-one function, so providing an iteratee will disable
- // the faster algorithm.
- function uniq(array, isSorted, iteratee, context) {
- if (!isBoolean(isSorted)) {
- context = iteratee;
- iteratee = isSorted;
- isSorted = false;
- }
- if (iteratee != null) iteratee = cb(iteratee, context);
- var result = [];
- var seen = [];
- for (var i = 0, length = getLength(array); i < length; i++) {
- var value = array[i],
- computed = iteratee ? iteratee(value, i, array) : value;
- if (isSorted && !iteratee) {
- if (!i || seen !== computed) result.push(value);
- seen = computed;
- } else if (iteratee) {
- if (!contains(seen, computed)) {
- seen.push(computed);
- result.push(value);
- }
- } else if (!contains(result, value)) {
- result.push(value);
- }
- }
- return result;
- }
-
- // Produce an array that contains the union: each distinct element from all of
- // the passed-in arrays.
- var union = restArguments(function(arrays) {
- return uniq(flatten$1(arrays, true, true));
- });
-
- // Produce an array that contains every item shared between all the
- // passed-in arrays.
- function intersection(array) {
- var result = [];
- var argsLength = arguments.length;
- for (var i = 0, length = getLength(array); i < length; i++) {
- var item = array[i];
- if (contains(result, item)) continue;
- var j;
- for (j = 1; j < argsLength; j++) {
- if (!contains(arguments[j], item)) break;
- }
- if (j === argsLength) result.push(item);
- }
- return result;
- }
-
- // Complement of zip. Unzip accepts an array of arrays and groups
- // each array's elements on shared indices.
- function unzip(array) {
- var length = array && max(array, getLength).length || 0;
- var result = Array(length);
-
- for (var index = 0; index < length; index++) {
- result[index] = pluck(array, index);
- }
- return result;
- }
-
- // Zip together multiple lists into a single array -- elements that share
- // an index go together.
- var zip = restArguments(unzip);
-
- // Converts lists into objects. Pass either a single array of `[key, value]`
- // pairs, or two parallel arrays of the same length -- one of keys, and one of
- // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
- function object(list, values) {
- var result = {};
- for (var i = 0, length = getLength(list); i < length; i++) {
- if (values) {
- result[list[i]] = values[i];
- } else {
- result[list[i][0]] = list[i][1];
- }
- }
- return result;
- }
-
- // Generate an integer Array containing an arithmetic progression. A port of
- // the native Python `range()` function. See
- // [the Python documentation](https://docs.python.org/library/functions.html#range).
- function range(start, stop, step) {
- if (stop == null) {
- stop = start || 0;
- start = 0;
- }
- if (!step) {
- step = stop < start ? -1 : 1;
- }
-
- var length = Math.max(Math.ceil((stop - start) / step), 0);
- var range = Array(length);
-
- for (var idx = 0; idx < length; idx++, start += step) {
- range[idx] = start;
- }
-
- return range;
- }
-
- // Chunk a single array into multiple arrays, each containing `count` or fewer
- // items.
- function chunk(array, count) {
- if (count == null || count < 1) return [];
- var result = [];
- var i = 0, length = array.length;
- while (i < length) {
- result.push(slice.call(array, i, i += count));
- }
- return result;
- }
-
- // Helper function to continue chaining intermediate results.
- function chainResult(instance, obj) {
- return instance._chain ? _$1(obj).chain() : obj;
- }
-
- // Add your own custom functions to the Underscore object.
- function mixin(obj) {
- each(functions(obj), function(name) {
- var func = _$1[name] = obj[name];
- _$1.prototype[name] = function() {
- var args = [this._wrapped];
- push.apply(args, arguments);
- return chainResult(this, func.apply(_$1, args));
- };
- });
- return _$1;
- }
-
- // Add all mutator `Array` functions to the wrapper.
- each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) {
- method.apply(obj, arguments);
- if ((name === 'shift' || name === 'splice') && obj.length === 0) {
- delete obj[0];
- }
- }
- return chainResult(this, obj);
- };
- });
-
- // Add all accessor `Array` functions to the wrapper.
- each(['concat', 'join', 'slice'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) obj = method.apply(obj, arguments);
- return chainResult(this, obj);
- };
- });
-
- // Named Exports
-
- var allExports = {
- __proto__: null,
- VERSION: VERSION,
- restArguments: restArguments,
- isObject: isObject,
- isNull: isNull,
- isUndefined: isUndefined,
- isBoolean: isBoolean,
- isElement: isElement,
- isString: isString,
- isNumber: isNumber,
- isDate: isDate,
- isRegExp: isRegExp,
- isError: isError,
- isSymbol: isSymbol,
- isArrayBuffer: isArrayBuffer,
- isDataView: isDataView$1,
- isArray: isArray,
- isFunction: isFunction$1,
- isArguments: isArguments$1,
- isFinite: isFinite$1,
- isNaN: isNaN$1,
- isTypedArray: isTypedArray$1,
- isEmpty: isEmpty,
- isMatch: isMatch,
- isEqual: isEqual,
- isMap: isMap,
- isWeakMap: isWeakMap,
- isSet: isSet,
- isWeakSet: isWeakSet,
- keys: keys,
- allKeys: allKeys,
- values: values,
- pairs: pairs,
- invert: invert,
- functions: functions,
- methods: functions,
- extend: extend,
- extendOwn: extendOwn,
- assign: extendOwn,
- defaults: defaults,
- create: create,
- clone: clone,
- tap: tap,
- get: get,
- has: has,
- mapObject: mapObject,
- identity: identity,
- constant: constant,
- noop: noop,
- toPath: toPath$1,
- property: property,
- propertyOf: propertyOf,
- matcher: matcher,
- matches: matcher,
- times: times,
- random: random,
- now: now,
- escape: _escape,
- unescape: _unescape,
- templateSettings: templateSettings,
- template: template,
- result: result,
- uniqueId: uniqueId,
- chain: chain,
- iteratee: iteratee,
- partial: partial,
- bind: bind,
- bindAll: bindAll,
- memoize: memoize,
- delay: delay,
- defer: defer,
- throttle: throttle,
- debounce: debounce,
- wrap: wrap,
- negate: negate,
- compose: compose,
- after: after,
- before: before,
- once: once,
- findKey: findKey,
- findIndex: findIndex,
- findLastIndex: findLastIndex,
- sortedIndex: sortedIndex,
- indexOf: indexOf,
- lastIndexOf: lastIndexOf,
- find: find,
- detect: find,
- findWhere: findWhere,
- each: each,
- forEach: each,
- map: map,
- collect: map,
- reduce: reduce,
- foldl: reduce,
- inject: reduce,
- reduceRight: reduceRight,
- foldr: reduceRight,
- filter: filter,
- select: filter,
- reject: reject,
- every: every,
- all: every,
- some: some,
- any: some,
- contains: contains,
- includes: contains,
- include: contains,
- invoke: invoke,
- pluck: pluck,
- where: where,
- max: max,
- min: min,
- shuffle: shuffle,
- sample: sample,
- sortBy: sortBy,
- groupBy: groupBy,
- indexBy: indexBy,
- countBy: countBy,
- partition: partition,
- toArray: toArray,
- size: size,
- pick: pick,
- omit: omit,
- first: first,
- head: first,
- take: first,
- initial: initial,
- last: last,
- rest: rest,
- tail: rest,
- drop: rest,
- compact: compact,
- flatten: flatten,
- without: without,
- uniq: uniq,
- unique: uniq,
- union: union,
- intersection: intersection,
- difference: difference,
- unzip: unzip,
- transpose: unzip,
- zip: zip,
- object: object,
- range: range,
- chunk: chunk,
- mixin: mixin,
- 'default': _$1
- };
-
- // Default Export
-
- // Add all of the Underscore functions to the wrapper object.
- var _ = mixin(allExports);
- // Legacy Node.js API.
- _._ = _;
-
- return _;
-
-})));
-//# sourceMappingURL=underscore-umd.js.map
diff --git a/sphinx/_static/underscore.js b/sphinx/_static/underscore.js
deleted file mode 100644
index cf177d4285ab55fbc16406a5ec827b80e7eecd53..0000000000000000000000000000000000000000
--- a/sphinx/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
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- <section id="python-api">
-<span id="ptbench-api"></span><h1>Python API<a class="headerlink" href="#python-api" title="Permalink to this heading">#</a></h1>
-<p>This section includes information for using the Python API of
-<code class="docutils literal notranslate"><span class="pre">ptbench</span></code>.</p>
-<section id="data-methods">
-<span id="ptbench-api-data"></span><h2>Data Methods<a class="headerlink" href="#data-methods" title="Permalink to this heading">#</a></h2>
-<p>Auxiliary classes and methods to define raw dataset iterators.</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/ptbench.data.sample.html#module-ptbench.data.sample" title="ptbench.data.sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.sample</span></code></a></p></td>
-<td><p>Base definition of sample.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/ptbench.data.dataset.html#module-ptbench.data.dataset" title="ptbench.data.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.dataset</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/ptbench.data.utils.html#module-ptbench.data.utils" title="ptbench.data.utils"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.utils</span></code></a></p></td>
-<td><p>Common utilities.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/ptbench.data.loader.html#module-ptbench.data.loader" title="ptbench.data.loader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.loader</span></code></a></p></td>
-<td><p>Data loading code.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/ptbench.data.transforms.html#module-ptbench.data.transforms" title="ptbench.data.transforms"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.transforms</span></code></a></p></td>
-<td><p>Image transformations for our pipelines.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/ptbench.configs.datasets.html#module-ptbench.configs.datasets" title="ptbench.configs.datasets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="raw-dataset-access">
-<span id="ptbench-api-data-raw"></span><h2>Raw Dataset Access<a class="headerlink" href="#raw-dataset-access" title="Permalink to this heading">#</a></h2>
-<p>Direct data-access through iterators.</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.hivtb_RS.html#module-ptbench.data.hivtb_RS" title="ptbench.data.hivtb_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.hivtb_RS</span></code></a></p></td>
-<td><p>HIV-TB dataset for computer-aided diagnosis (only BMP files)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc" title="ptbench.data.tbpoc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.tbpoc</span></code></a></p></td>
-<td><p>TB-POC dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.montgomery_RS.html#module-ptbench.data.montgomery_RS" title="ptbench.data.montgomery_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.montgomery_RS</span></code></a></p></td>
-<td><p>Extended Montgomery dataset for computer-aided diagnosis (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest" title="ptbench.data.padchest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.padchest</span></code></a></p></td>
-<td><p>Padchest dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb" title="ptbench.data.hivtb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.hivtb</span></code></a></p></td>
-<td><p>HIV-TB dataset for computer-aided diagnosis (only BMP files)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.indian_RS.html#module-ptbench.data.indian_RS" title="ptbench.data.indian_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.indian_RS</span></code></a></p></td>
-<td><p>Indian collection dataset for computer-aided diagnosis (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.shenzhen_RS.html#module-ptbench.data.shenzhen_RS" title="ptbench.data.shenzhen_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.shenzhen_RS</span></code></a></p></td>
-<td><p>Shenzhen dataset for computer-aided diagnosis (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.tbpoc_RS.html#module-ptbench.data.tbpoc_RS" title="ptbench.data.tbpoc_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.tbpoc_RS</span></code></a></p></td>
-<td><p>TB-POC dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a></p></td>
-<td><p>Shenzhen dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a></p></td>
-<td><p>Montgomery dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a></p></td>
-<td><p>Indian collection dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re" title="ptbench.data.nih_cxr14_re"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.nih_cxr14_re</span></code></a></p></td>
-<td><p>NIH CXR14 (relabeled) dataset for computer-aided diagnosis.</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="api/data/raw/ptbench.data.padchest_RS.html#module-ptbench.data.padchest_RS" title="ptbench.data.padchest_RS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.data.padchest_RS</span></code></a></p></td>
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-<span id="ptbench-configs-datasets-hivtb-fold-0"></span><h1>ptbench.configs.datasets.hivtb.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.hivtb.fold_0" title="Permalink to this heading">#</a></h1>
-<p>HIV-TB dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb" title="ptbench.data.hivtb"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.hivtb</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-hivtb-fold-0-rgb"></span><h1>ptbench.configs.datasets.hivtb.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.hivtb.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>HIV-TB dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb" title="ptbench.data.hivtb"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.hivtb</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html
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-<span id="ptbench-configs-datasets-hivtb-rs-fold-0"></span><h1>ptbench.configs.datasets.hivtb_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.hivtb_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>HIV-TB dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb" title="ptbench.data.hivtb"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.hivtb</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-indian-default"></span><h1>ptbench.configs.datasets.indian.default<a class="headerlink" href="#module-ptbench.configs.datasets.indian.default" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (default protocol)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-indian-fold-0"></span><h1>ptbench.configs.datasets.indian.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.indian.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-indian-fold-0-rgb"></span><h1>ptbench.configs.datasets.indian.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.indian.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (cross validation fold 0, RGB)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-indian-rgb"></span><h1>ptbench.configs.datasets.indian.rgb<a class="headerlink" href="#module-ptbench.configs.datasets.indian.rgb" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (default protocol, converted in RGB)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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- <section id="module-ptbench.configs.datasets.indian_RS.default">
-<span id="ptbench-configs-datasets-indian-rs-default"></span><h1>ptbench.configs.datasets.indian_RS.default<a class="headerlink" href="#module-ptbench.configs.datasets.indian_RS.default" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (default protocol) (extended with DensenetRS
-predictions)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-indian-rs-fold-0"></span><h1>ptbench.configs.datasets.indian_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.indian_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Indian dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.indian.html#module-ptbench.data.indian" title="ptbench.data.indian"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.indian</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-mc-ch-default"></span><h1>ptbench.configs.datasets.mc_ch.default<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch.default" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery and Shenzhen datasets.</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 0)</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html
deleted file mode 100644
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-<span id="ptbench-configs-datasets-mc-ch-fold-0-rgb"></span><h1>ptbench.configs.datasets.mc_ch.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 0, RGB)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0_rgb"</span><span class="p">)</span>
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deleted file mode 100644
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-<p>Aggregated dataset composed of Montgomery and Shenzhen (RGB) datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"rgb"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery and Shenzhen datasets.</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html
deleted file mode 100644
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- <section id="module-ptbench.configs.datasets.mc_ch_RS.fold_0">
-<span id="ptbench-configs-datasets-mc-ch-rs-fold-0"></span><h1>ptbench.configs.datasets.mc_ch_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 0)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets.</p>
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-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html
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-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 0)</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 0, RGB)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0_rgb"</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html
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-<span id="ptbench-configs-datasets-mc-ch-in-rgb"></span><h1>ptbench.configs.datasets.mc_ch_in.rgb<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch_in.rgb" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian (RGB)
-datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"rgb"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-mc-ch-in-rs-default"></span><h1>ptbench.configs.datasets.mc_ch_in_RS.default<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch_in_RS.default" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 0)</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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deleted file mode 100644
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- <section id="module-ptbench.configs.datasets.mc_ch_in_pc.default">
-<span id="ptbench-configs-datasets-mc-ch-in-pc-default"></span><h1>ptbench.configs.datasets.mc_ch_in_pc.default<a class="headerlink" href="#module-ptbench.configs.datasets.mc_ch_in_pc.default" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
-datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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-<p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
-(RGB) datasets.</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"rgb"</span><span class="p">)</span>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html
deleted file mode 100644
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-<p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and PadChest
-(TB) datasets.</p>
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-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-montgomery-default"></span><h1>ptbench.configs.datasets.montgomery.default<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery.default" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (default protocol)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-montgomery-fold-0"></span><h1>ptbench.configs.datasets.montgomery.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-montgomery-fold-0-rgb"></span><h1>ptbench.configs.datasets.montgomery.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (cross validation fold 0, RGB)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-montgomery-rgb"></span><h1>ptbench.configs.datasets.montgomery.rgb<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery.rgb" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (default protocol, converted in RGB)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-montgomery-rs-default"></span><h1>ptbench.configs.datasets.montgomery_RS.default<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery_RS.default" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (default protocol) (extended with
-DensenetRS predictions)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-montgomery-rs-fold-0"></span><h1>ptbench.configs.datasets.montgomery_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.montgomery_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery" title="ptbench.data.montgomery"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.montgomery</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html
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-<span id="ptbench-configs-datasets-nih-cxr14-re-cardiomegaly"></span><h1>ptbench.configs.datasets.nih_cxr14_re.cardiomegaly<a class="headerlink" href="#module-ptbench.configs.datasets.nih_cxr14_re.cardiomegaly" title="Permalink to this heading">#</a></h1>
-<p>NIH CXR14 dataset for computer-aided diagnosis.</p>
-<p>First 40 images with cardiomegaly.</p>
-<ul class="simple">
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re" title="ptbench.data.nih_cxr14_re"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.nih_cxr14_re</span></code></a> for split details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re" title="ptbench.data.nih_cxr14_re"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.nih_cxr14_re</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"cardiomegaly"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-nih-cxr14-re-default"></span><h1>ptbench.configs.datasets.nih_cxr14_re.default<a class="headerlink" href="#module-ptbench.configs.datasets.nih_cxr14_re.default" title="Permalink to this heading">#</a></h1>
-<p>NIH CXR14 (relabeled) dataset for computer-aided diagnosis (default
-protocol)</p>
-<ul class="simple">
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re" title="ptbench.data.nih_cxr14_re"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.nih_cxr14_re</span></code></a> for split details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re" title="ptbench.data.nih_cxr14_re"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.nih_cxr14_re</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-nih-cxr14-re-pc-idiap"></span><h1>ptbench.configs.datasets.nih_cxr14_re_pc.idiap<a class="headerlink" href="#module-ptbench.configs.datasets.nih_cxr14_re_pc.idiap" title="Permalink to this heading">#</a></h1>
-<p>Aggregated dataset composed of NIH CXR14 relabeld and PadChest (normalized)
-datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"idiap"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-padchest-cardiomegaly-idiap"></span><h1>ptbench.configs.datasets.padchest.cardiomegaly_idiap<a class="headerlink" href="#module-ptbench.configs.datasets.padchest.cardiomegaly_idiap" title="Permalink to this heading">#</a></h1>
-<p>Padchest cardiomegaly (idiap protocol) dataset for computer-aided diagnosis.</p>
-<p>The first 40 images with cardiomegaly.
-parameters: Label = “Normalâ€, MethodLabel = “Physicianâ€, Projection = “PAâ€</p>
-<ul class="simple">
-<li><p>Split reference: first 100% of cardiomegaly for “trainâ€</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest" title="ptbench.data.padchest"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.padchest</span></code></a> for dataset details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"cardiomegaly_idiap"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-padchest-idiap"></span><h1>ptbench.configs.datasets.padchest.idiap<a class="headerlink" href="#module-ptbench.configs.datasets.padchest.idiap" title="Permalink to this heading">#</a></h1>
-<p>Padchest (idiap protocol) dataset for computer-aided diagnosis.</p>
-<ul class="simple">
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest" title="ptbench.data.padchest"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.padchest</span></code></a> for dataset details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"idiap"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-padchest-no-tb-idiap"></span><h1>ptbench.configs.datasets.padchest.no_tb_idiap<a class="headerlink" href="#module-ptbench.configs.datasets.padchest.no_tb_idiap" title="Permalink to this heading">#</a></h1>
-<p>Padchest tuberculosis (no TB idiap protocol) dataset for computer-aided
-diagnosis.</p>
-<ul class="simple">
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">no</span> <span class="pre">TB</span> <span class="pre">idiap</span></code>:</p>
-<ul>
-<li><p>Training samples: 20’126</p></li>
-<li><p>Validation samples: 1’500</p></li>
-<li><p>Test samples: 0</p></li>
-</ul>
-</li>
-<li><p>Images path adapted to Idiap infrastructure</p></li>
-<li><p>Labels:
-cardiomegaly, emphysema, effusion, hernia, infiltration,
-mass, nodule, atelectasis, pneumothorax, pleural thickening, pneumonia,
-fibrosis, edema and consolidation</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"no_tb_idiap"</span><span class="p">)</span>
-</pre></div>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html
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-<span id="ptbench-configs-datasets-padchest-tb-idiap"></span><h1>ptbench.configs.datasets.padchest.tb_idiap<a class="headerlink" href="#module-ptbench.configs.datasets.padchest.tb_idiap" title="Permalink to this heading">#</a></h1>
-<p>Padchest tuberculosis (idiap protocol) dataset for computer-aided diagnosis.</p>
-<p>The 125 healthy images are the first 125 padchest images with the following
-parameters: Label = “Normalâ€, MethodLabel = “Physicianâ€, Projection = “PAâ€</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest" title="ptbench.data.padchest"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.padchest</span></code></a> for dataset details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"tb_idiap"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-padchest-tb-idiap-rgb"></span><h1>ptbench.configs.datasets.padchest.tb_idiap_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.padchest.tb_idiap_rgb" title="Permalink to this heading">#</a></h1>
-<p>Padchest tuberculosis (idiap protocol, rgb) dataset for computer-aided
-diagnosis.</p>
-<p>The 125 healthy images are the first 125 padchest images with the following
-parameters: Label = “Normalâ€, MethodLabel = “Physicianâ€, Projection = “PAâ€</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest" title="ptbench.data.padchest"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.padchest</span></code></a> for dataset details</p></li>
-<li><p>This configuration resolution: 224 x 224 (default)</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"tb_idiap"</span><span class="p">,</span> <span class="n">resize_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cc_size</span><span class="o">=</span><span class="mi">224</span><span class="p">)</span>
-</pre></div>
-</div>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html
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-<span id="ptbench-configs-datasets-padchest-rs-tb-idiap"></span><h1>ptbench.configs.datasets.padchest_RS.tb_idiap<a class="headerlink" href="#module-ptbench.configs.datasets.padchest_RS.tb_idiap" title="Permalink to this heading">#</a></h1>
-<p>Extended Padchest TB dataset for TB detection (default protocol) (extended
-with DensenetRS predictions)</p>
-<ul class="simple">
-<li><p>Split reference: 64%/16%/20%</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.padchest_RS.html#module-ptbench.data.padchest_RS" title="ptbench.data.padchest_RS"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.padchest_RS</span></code></a> for dataset details</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"tb_idiap"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-shenzhen-default"></span><h1>ptbench.configs.datasets.shenzhen.default<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen.default" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (default protocol)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-shenzhen-fold-0"></span><h1>ptbench.configs.datasets.shenzhen.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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diff --git a/sphinx/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html b/sphinx/api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html
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-<span id="ptbench-configs-datasets-shenzhen-fold-0-rgb"></span><h1>ptbench.configs.datasets.shenzhen.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (cross validation fold 0, RGB)</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-shenzhen-rgb"></span><h1>ptbench.configs.datasets.shenzhen.rgb<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen.rgb" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (default protocol, converted in RGB)</p>
-<ul class="simple">
-<li><p>Split reference: first 64% of TB and healthy CXR for “train†16% for</p></li>
-<li><p>“validationâ€, 20% for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-shenzhen-rs-default"></span><h1>ptbench.configs.datasets.shenzhen_RS.default<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen_RS.default" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (default protocol) (extended with
-DensenetRS predictions)</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"default"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-shenzhen-rs-fold-0"></span><h1>ptbench.configs.datasets.shenzhen_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.shenzhen_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: first 80% of TB and healthy CXR for “trainâ€, rest for “testâ€</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen" title="ptbench.data.shenzhen"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.shenzhen</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
-</pre></div>
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-<span id="ptbench-configs-datasets-tbpoc-fold-0"></span><h1>ptbench.configs.datasets.tbpoc.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.tbpoc.fold_0" title="Permalink to this heading">#</a></h1>
-<p>TB-POC dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc" title="ptbench.data.tbpoc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.tbpoc</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-tbpoc-fold-0-rgb"></span><h1>ptbench.configs.datasets.tbpoc.fold_0_rgb<a class="headerlink" href="#module-ptbench.configs.datasets.tbpoc.fold_0_rgb" title="Permalink to this heading">#</a></h1>
-<p>TB-POC dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc" title="ptbench.data.tbpoc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.tbpoc</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">,</span> <span class="n">RGB</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-<span id="ptbench-configs-datasets-tbpoc-rs-fold-0"></span><h1>ptbench.configs.datasets.tbpoc_RS.fold_0<a class="headerlink" href="#module-ptbench.configs.datasets.tbpoc_RS.fold_0" title="Permalink to this heading">#</a></h1>
-<p>TB-POC dataset for TB detection (cross validation fold 0)</p>
-<ul class="simple">
-<li><p>Split reference: none (stratified kfolding)</p></li>
-<li><p>This configuration resolution: 512 x 512 (default)</p></li>
-<li><p>See <a class="reference internal" href="../../data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc" title="ptbench.data.tbpoc"><code class="xref py py-mod docutils literal notranslate"><span class="pre">ptbench.data.tbpoc</span></code></a> for dataset details</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_maker</span>
-
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">_maker</span><span class="p">(</span><span class="s2">"fold_0"</span><span class="p">)</span>
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-<span id="ptbench-configs-models-alexnet"></span><h1>ptbench.configs.models.alexnet<a class="headerlink" href="#module-ptbench.configs.models.alexnet" title="Permalink to this heading">#</a></h1>
-<p>AlexNet.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">SGD</span>
-
-<span class="kn">from</span> <span class="nn">...models.alexnet</span> <span class="kn">import</span> <span class="n">build_alexnet</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">0.01</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-<span id="ptbench-configs-models-alexnet-pretrained"></span><h1>ptbench.configs.models.alexnet_pretrained<a class="headerlink" href="#module-ptbench.configs.models.alexnet_pretrained" title="Permalink to this heading">#</a></h1>
-<p>AlexNet.</p>
-<p>Pretrained AlexNet</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">SGD</span>
-
-<span class="kn">from</span> <span class="nn">...models.alexnet</span> <span class="kn">import</span> <span class="n">build_alexnet</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-<span id="ptbench-configs-models-densenet"></span><h1>ptbench.configs.models.densenet<a class="headerlink" href="#module-ptbench.configs.models.densenet" title="Permalink to this heading">#</a></h1>
-<p>DenseNet.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.densenet</span> <span class="kn">import</span> <span class="n">build_densenet</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">0.0001</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_densenet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-<span id="ptbench-configs-models-densenet-pretrained"></span><h1>ptbench.configs.models.densenet_pretrained<a class="headerlink" href="#module-ptbench.configs.models.densenet_pretrained" title="Permalink to this heading">#</a></h1>
-<p>DenseNet.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.densenet</span> <span class="kn">import</span> <span class="n">build_densenet</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">0.01</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_densenet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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- <section id="module-ptbench.configs.models.logistic_regression">
-<span id="ptbench-configs-models-logistic-regression"></span><h1>ptbench.configs.models.logistic_regression<a class="headerlink" href="#module-ptbench.configs.models.logistic_regression" title="Permalink to this heading">#</a></h1>
-<p>Feedforward network for Tuberculosis Detection.</p>
-<p>Simple feedforward network taking radiological signs in output and
-predicting tuberculosis presence in output.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.logistic_regression</span> <span class="kn">import</span> <span class="n">build_logistic_regression</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-2</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_logistic_regression</span><span class="p">(</span><span class="mi">14</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-<span id="ptbench-configs-models-pasa"></span><h1>ptbench.configs.models.pasa<a class="headerlink" href="#module-ptbench.configs.models.pasa" title="Permalink to this heading">#</a></h1>
-<p>CNN for Tuberculosis Detection.</p>
-<p>Implementation of the model architecture proposed by F. Pasa in the article
-“Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis
-Screening and Visualizationâ€.</p>
-<p>Reference: <a class="reference internal" href="../../../references.html#pasa-2019" id="id1"><span>[PASA-2019]</span></a></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.pasa</span> <span class="kn">import</span> <span class="n">build_pasa</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">8e-5</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_pasa</span><span class="p">()</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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- <section id="module-ptbench.configs.models.signs_to_tb">
-<span id="ptbench-configs-models-signs-to-tb"></span><h1>ptbench.configs.models.signs_to_tb<a class="headerlink" href="#module-ptbench.configs.models.signs_to_tb" title="Permalink to this heading">#</a></h1>
-<p>Feedforward network for Tuberculosis Detection.</p>
-<p>Simple feedforward network taking radiological signs in output and
-predicting tuberculosis presence in output.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.signs_to_tb</span> <span class="kn">import</span> <span class="n">build_signs_to_tb</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-2</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_signs_to_tb</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-<span id="ptbench-configs-models-datasets-densenet-rs"></span><h1>ptbench.configs.models_datasets.densenet_rs<a class="headerlink" href="#module-ptbench.configs.models_datasets.densenet_rs" title="Permalink to this heading">#</a></h1>
-<p>CNN for radiological findings detection.</p>
-<p>A Densenet121 model for radiological extraction</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">BCEWithLogitsLoss</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
-
-<span class="kn">from</span> <span class="nn">...models.densenet_rs</span> <span class="kn">import</span> <span class="n">build_densenetrs</span>
-
-<span class="c1"># Import the default protocol if none is available</span>
-<span class="k">if</span> <span class="s2">"dataset"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">():</span>
- <span class="kn">from</span> <span class="nn">..datasets.nih_cxr14_re</span> <span class="kn">import</span> <span class="n">default</span>
-
- <span class="n">dataset</span> <span class="o">=</span> <span class="n">default</span><span class="o">.</span><span class="n">dataset</span>
-
-<span class="c1"># config</span>
-<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-4</span>
-
-<span class="c1"># model</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">build_densenetrs</span><span class="p">()</span>
-
-<span class="c1"># optimizer</span>
-<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span>
- <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">model_ft</span><span class="o">.</span><span class="n">parameters</span><span class="p">()),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span>
-<span class="p">)</span>
-
-<span class="c1"># criterion</span>
-<span class="n">criterion</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-<span class="n">criterion_valid</span> <span class="o">=</span> <span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
-</pre></div>
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-</section>
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- <section id="module-ptbench.configs.datasets">
-<span id="ptbench-configs-datasets"></span><h1>ptbench.configs.datasets<a class="headerlink" href="#module-ptbench.configs.datasets" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.configs.datasets.RANDOM_ROTATION" title="ptbench.configs.datasets.RANDOM_ROTATION"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RANDOM_ROTATION</span></code></a></p></td>
-<td><p>Shared data augmentation based on random rotation only.</p></td>
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-<p class="rubric">Functions</p>
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-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.configs.datasets.get_positive_weights" title="ptbench.configs.datasets.get_positive_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_positive_weights</span></code></a>(dataset)</p></td>
-<td><p>Compute the positive weights of each class of the dataset to balance the BCEWithLogitsLoss criterion.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.configs.datasets.get_samples_weights" title="ptbench.configs.datasets.get_samples_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_samples_weights</span></code></a>(dataset)</p></td>
-<td><p>Compute the weights of all the samples of the dataset to balance it using the sampler of the dataloader.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.configs.datasets.make_dataset" title="ptbench.configs.datasets.make_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_dataset</span></code></a>(subsets_groups[, transforms, ...])</p></td>
-<td><p>Creates a new configuration dataset from a list of dictionaries and transforms.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.configs.datasets.make_subset" title="ptbench.configs.datasets.make_subset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_subset</span></code></a>(samples[, transforms, prefixes, ...])</p></td>
-<td><p>Creates a new data set, applying transforms.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.configs.datasets.RANDOM_ROTATION">
-<span class="sig-prename descclassname"><span class="pre">ptbench.configs.datasets.</span></span><span class="sig-name descname"><span class="pre">RANDOM_ROTATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[RandomRotation(degrees=[-15.0,</span> <span class="pre">15.0],</span> <span class="pre">interpolation=nearest,</span> <span class="pre">expand=False,</span> <span class="pre">fill=0)]</span></em><a class="headerlink" href="#ptbench.configs.datasets.RANDOM_ROTATION" title="Permalink to this definition">#</a></dt>
-<dd><p>Shared data augmentation based on random rotation only.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.configs.datasets.make_subset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.configs.datasets.</span></span><span class="sig-name descname"><span class="pre">make_subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/configs/datasets.html#make_subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.configs.datasets.make_subset" title="Permalink to this definition">#</a></dt>
-<dd><p>Creates a new data set, applying transforms.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This is a convenience function for our own dataset definitions inside
-this module, guaranteeting homogenity between dataset definitions
-provided in this package. It assumes certain strategies for data
-augmentation that may not be translatable to other applications.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – List of delayed samples</p></li>
-<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of transforms that needs to be applied to all samples in the set</p></li>
-<li><p><strong>prefixes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of data augmentation operations that needs to be applied
-<strong>before</strong> the transforms above</p></li>
-<li><p><strong>suffixes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of data augmentation operations that needs to be applied
-<strong>after</strong> the transforms above</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>subset</strong> (<a class="reference internal" href="ptbench.data.utils.html#ptbench.data.utils.SampleListDataset" title="ptbench.data.utils.SampleListDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.data.utils.SampleListDataset</span></code></a>) – A pre-formatted dataset that can be fed to one of our engines</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.configs.datasets.make_dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.configs.datasets.</span></span><span class="sig-name descname"><span class="pre">make_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subsets_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/configs/datasets.html#make_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.configs.datasets.make_dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Creates a new configuration dataset from a list of dictionaries and
-transforms.</p>
-<p>This function takes as input a list of dictionaries as those that can be
-returned by <a class="reference internal" href="ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset.subsets" title="ptbench.data.dataset.JSONDataset.subsets"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ptbench.data.dataset.JSONDataset.subsets()</span></code></a>
-mapping protocol names (such as <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">dev</span></code> and <code class="docutils literal notranslate"><span class="pre">test</span></code>) to
-<a class="reference internal" href="ptbench.data.sample.html#ptbench.data.sample.DelayedSample" title="ptbench.data.sample.DelayedSample"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.data.sample.DelayedSample</span></code></a> lists, and a set of
-transforms, and returns a dictionary applying
-<a class="reference internal" href="ptbench.data.utils.html#ptbench.data.utils.SampleListDataset" title="ptbench.data.utils.SampleListDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.data.utils.SampleListDataset</span></code></a> to these
-lists, and our standard data augmentation if a <code class="docutils literal notranslate"><span class="pre">train</span></code> set exists.</p>
-<p>For example, if <code class="docutils literal notranslate"><span class="pre">subsets</span></code> is composed of two sets named <code class="docutils literal notranslate"><span class="pre">train</span></code> and
-<code class="docutils literal notranslate"><span class="pre">test</span></code>, this function will yield a dictionary with the following entries:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">__train__</span></code>: Wraps the <code class="docutils literal notranslate"><span class="pre">train</span></code> subset, includes data augmentation
-(note: datasets with names starting with <code class="docutils literal notranslate"><span class="pre">_</span></code> (underscore) are excluded
-from prediction and evaluation by default, as they contain data
-augmentation transformations.)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">train</span></code>: Wraps the <code class="docutils literal notranslate"><span class="pre">train</span></code> subset, <strong>without</strong> data augmentation</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">test</span></code>: Wraps the <code class="docutils literal notranslate"><span class="pre">test</span></code> subset, <strong>without</strong> data augmentation</p></li>
-</ul>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This is a convenience function for our own dataset definitions inside
-this module, guaranteeting homogenity between dataset definitions
-provided in this package. It assumes certain strategies for data
-augmentation that may not be translatable to other applications.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>subsets</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of dictionaries that contains the delayed sample lists
-for a number of named lists. The subsets will be aggregated in one
-final subset. If one of the keys is <code class="docutils literal notranslate"><span class="pre">train</span></code>, our standard dataset
-augmentation transforms are appended to the definition of that subset.
-All other subsets remain un-augmented.</p></li>
-<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of transforms that needs to be applied to all samples in the set</p></li>
-<li><p><strong>t_transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of transforms that needs to be applied to the train samples</p></li>
-<li><p><strong>post_transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of transforms that needs to be applied to all samples in the set
-after all the other transforms</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>dataset</strong> (<em>dict</em>) – A pre-formatted dataset that can be fed to one of our engines. It maps
-string names to <a class="reference internal" href="ptbench.data.utils.html#ptbench.data.utils.SampleListDataset" title="ptbench.data.utils.SampleListDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.data.utils.SampleListDataset</span></code></a>’s.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.configs.datasets.get_samples_weights">
-<span class="sig-prename descclassname"><span class="pre">ptbench.configs.datasets.</span></span><span class="sig-name descname"><span class="pre">get_samples_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/configs/datasets.html#get_samples_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.configs.datasets.get_samples_weights" title="Permalink to this definition">#</a></dt>
-<dd><p>Compute the weights of all the samples of the dataset to balance it
-using the sampler of the dataloader.</p>
-<p>This function takes as input a <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.dataset.Dataset</span></code></a>
-and computes the weights to balance each class in the dataset and the
-datasets themselves if we have a ConcatDataset.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v1.13)"><em>torch.utils.data.dataset.Dataset</em></a>) – An instance of torch.utils.data.dataset.Dataset
-ConcatDataset are supported</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>samples_weights</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>) – the weights for all the samples in the dataset given as input</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.configs.datasets.get_positive_weights">
-<span class="sig-prename descclassname"><span class="pre">ptbench.configs.datasets.</span></span><span class="sig-name descname"><span class="pre">get_positive_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/configs/datasets.html#get_positive_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.configs.datasets.get_positive_weights" title="Permalink to this definition">#</a></dt>
-<dd><p>Compute the positive weights of each class of the dataset to balance the
-BCEWithLogitsLoss criterion.</p>
-<p>This function takes as input a <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.dataset.Dataset</span></code></a>
-and computes the positive weights of each class to use them to have
-a balanced loss.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v1.13)"><em>torch.utils.data.dataset.Dataset</em></a>) – An instance of torch.utils.data.dataset.Dataset
-ConcatDataset are supported</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>positive_weights</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>) – the positive weight of each class in the dataset given as input</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-
- </article>
- </div>
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-<li><a class="reference internal" href="#">ptbench.configs.datasets</a><ul>
-<li><a class="reference internal" href="#ptbench.configs.datasets.RANDOM_ROTATION"><code class="docutils literal notranslate"><span class="pre">RANDOM_ROTATION</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.configs.datasets.make_subset"><code class="docutils literal notranslate"><span class="pre">make_subset()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.configs.datasets.make_dataset"><code class="docutils literal notranslate"><span class="pre">make_dataset()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.configs.datasets.get_samples_weights"><code class="docutils literal notranslate"><span class="pre">get_samples_weights()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.configs.datasets.get_positive_weights"><code class="docutils literal notranslate"><span class="pre">get_positive_weights()</span></code></a></li>
-</ul>
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- <section id="module-ptbench.data.dataset">
-<span id="ptbench-data-dataset"></span><h1>ptbench.data.dataset<a class="headerlink" href="#module-ptbench.data.dataset" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.dataset.CSVDataset" title="ptbench.data.dataset.CSVDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CSVDataset</span></code></a>(subsets, fieldnames, loader)</p></td>
-<td><p>Generic multi-subset filelist dataset that yields samples.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.data.dataset.JSONDataset" title="ptbench.data.dataset.JSONDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JSONDataset</span></code></a>(protocols, fieldnames, loader)</p></td>
-<td><p>Generic multi-protocol/subset filelist dataset that yields samples.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.dataset.JSONDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.dataset.</span></span><span class="sig-name descname"><span class="pre">JSONDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protocols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fieldnames</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#JSONDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.JSONDataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Generic multi-protocol/subset filelist dataset that yields samples.</p>
-<p>To create a new dataset, you need to provide one or more JSON formatted
-filelists (one per protocol) with the following contents:</p>
-<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
-<span class="w"> </span><span class="nt">"subset1"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
-<span class="w"> </span><span class="p">[</span>
-<span class="w"> </span><span class="s2">"value1"</span><span class="p">,</span>
-<span class="w"> </span><span class="s2">"value2"</span><span class="p">,</span>
-<span class="w"> </span><span class="s2">"value3"</span>
-<span class="w"> </span><span class="p">],</span>
-<span class="w"> </span><span class="p">[</span>
-<span class="w"> </span><span class="s2">"value4"</span><span class="p">,</span>
-<span class="w"> </span><span class="s2">"value5"</span><span class="p">,</span>
-<span class="w"> </span><span class="s2">"value6"</span>
-<span class="w"> </span><span class="p">]</span>
-<span class="w"> </span><span class="p">],</span>
-<span class="w"> </span><span class="nt">"subset2"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
-<span class="w"> </span><span class="p">]</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>Your dataset many contain any number of subsets, but all sample entries
-must contain the same number of fields.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>protocols</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – Paths to one or more JSON formatted files containing the various
-protocols to be recognized by this dataset, or a dictionary, mapping
-protocol names to paths (or opened file objects) of CSV files.
-Internally, we save a dictionary where keys default to the basename of
-paths (list input).</p></li>
-<li><p><strong>fieldnames</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><em>tuple</em></a>) – An iterable over the field names (strings) to assign to each entry in
-the JSON file. It should have as many items as fields in each entry of
-the JSON file.</p></li>
-<li><p><strong>loader</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – <p>A function that receives as input, a context dictionary (with at least
-a “protocol†and “subset†keys indicating which protocol and subset are
-being served), and a dictionary with <code class="docutils literal notranslate"><span class="pre">{fieldname:</span> <span class="pre">value}</span></code> entries,
-and returns an object with at least 2 attributes:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">key</span></code>: which must be a unique string for every sample across
-subsets in a protocol, and</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">data</span></code>: which contains the data associated witht this sample</p></li>
-</ul>
-</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.dataset.JSONDataset.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#JSONDataset.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.JSONDataset.check" title="Permalink to this definition">#</a></dt>
-<dd><p>For each protocol, check if all data can be correctly accessed.</p>
-<p>This function assumes each sample has a <code class="docutils literal notranslate"><span class="pre">data</span></code> and a <code class="docutils literal notranslate"><span class="pre">key</span></code>
-attribute. The <code class="docutils literal notranslate"><span class="pre">key</span></code> attribute should be a string, or representable
-as such.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – Maximum number of samples to check (in each protocol/subset
-combination) in this dataset. If set to zero, then check
-everything.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>errors</strong> (<em>int</em>) – Number of errors found</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.dataset.JSONDataset.subsets">
-<span class="sig-name descname"><span class="pre">subsets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protocol</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#JSONDataset.subsets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.JSONDataset.subsets" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns all subsets in a protocol.</p>
-<p>This method will load JSON information for a given protocol and return
-all subsets of the given protocol after converting each entry through
-the loader function.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>protocol</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – Name of the protocol data to load</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>subsets</strong> (<em>dict</em>) – A dictionary mapping subset names to lists of objects (respecting
-the <code class="docutils literal notranslate"><span class="pre">key</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> interface).</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.dataset.CSVDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.dataset.</span></span><span class="sig-name descname"><span class="pre">CSVDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subsets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fieldnames</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#CSVDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.CSVDataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Generic multi-subset filelist dataset that yields samples.</p>
-<p>To create a new dataset, you only need to provide a CSV formatted filelist
-using any separator (e.g. comma, space, semi-colon) with the following
-information:</p>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>value1,value2,value3
-value4,value5,value6
-...
-</pre></div>
-</div>
-<p>Notice that all rows must have the same number of entries.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>subsets</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – Paths to one or more CSV formatted files containing the various subsets
-to be recognized by this dataset, or a dictionary, mapping subset names
-to paths (or opened file objects) of CSV files. Internally, we save a
-dictionary where keys default to the basename of paths (list input).</p></li>
-<li><p><strong>fieldnames</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><em>tuple</em></a>) – An iterable over the field names (strings) to assign to each column in
-the CSV file. It should have as many items as fields in each row of
-the CSV file(s).</p></li>
-<li><p><strong>loader</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – A function that receives as input, a context dictionary (with, at
-least, a “subset†key indicating which subset is being served), and a
-dictionary with <code class="docutils literal notranslate"><span class="pre">{key:</span> <span class="pre">path}</span></code> entries, and returns a dictionary with
-the loaded data.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.dataset.CSVDataset.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#CSVDataset.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.CSVDataset.check" title="Permalink to this definition">#</a></dt>
-<dd><p>For each subset, check if all data can be correctly accessed.</p>
-<p>This function assumes each sample has a <code class="docutils literal notranslate"><span class="pre">data</span></code> and a <code class="docutils literal notranslate"><span class="pre">key</span></code>
-attribute. The <code class="docutils literal notranslate"><span class="pre">key</span></code> attribute should be a string, or representable
-as such.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – Maximum number of samples to check (in each protocol/subset
-combination) in this dataset. If set to zero, then check
-everything.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>errors</strong> (<em>int</em>) – Number of errors found</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.dataset.CSVDataset.subsets">
-<span class="sig-name descname"><span class="pre">subsets</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#CSVDataset.subsets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.CSVDataset.subsets" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns all available subsets at once.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>subsets</strong> (<em>dict</em>) – A dictionary mapping subset names to lists of objects (respecting
-the <code class="docutils literal notranslate"><span class="pre">key</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> interface).</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.dataset.CSVDataset.samples">
-<span class="sig-name descname"><span class="pre">samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/dataset.html#CSVDataset.samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.dataset.CSVDataset.samples" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns all samples in a subset.</p>
-<p>This method will load CSV information for a given subset and return
-all samples of the given subset after passing each entry through the
-loading function.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>subset</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – Name of the subset data to load</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>subset</strong> (<em>list</em>) – A lists of objects (respecting the <code class="docutils literal notranslate"><span class="pre">key</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> interface).</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
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-
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-<li><a class="reference internal" href="#ptbench.data.dataset.JSONDataset"><code class="docutils literal notranslate"><span class="pre">JSONDataset</span></code></a><ul>
-<li><a class="reference internal" href="#ptbench.data.dataset.JSONDataset.check"><code class="docutils literal notranslate"><span class="pre">JSONDataset.check()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.data.dataset.JSONDataset.subsets"><code class="docutils literal notranslate"><span class="pre">JSONDataset.subsets()</span></code></a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#ptbench.data.dataset.CSVDataset"><code class="docutils literal notranslate"><span class="pre">CSVDataset</span></code></a><ul>
-<li><a class="reference internal" href="#ptbench.data.dataset.CSVDataset.check"><code class="docutils literal notranslate"><span class="pre">CSVDataset.check()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.data.dataset.CSVDataset.subsets"><code class="docutils literal notranslate"><span class="pre">CSVDataset.subsets()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.data.dataset.CSVDataset.samples"><code class="docutils literal notranslate"><span class="pre">CSVDataset.samples()</span></code></a></li>
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-<span id="ptbench-data-loader"></span><h1>ptbench.data.loader<a class="headerlink" href="#module-ptbench.data.loader" title="Permalink to this heading">#</a></h1>
-<p>Data loading code.</p>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.loader.load_pil" title="ptbench.data.loader.load_pil"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_pil</span></code></a>(path)</p></td>
-<td><p>Loads a sample data.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.data.loader.load_pil_baw" title="ptbench.data.loader.load_pil_baw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_pil_baw</span></code></a>(path)</p></td>
-<td><p>Loads a sample data.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.loader.load_pil_rgb" title="ptbench.data.loader.load_pil_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_pil_rgb</span></code></a>(path)</p></td>
-<td><p>Loads a sample data.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.data.loader.make_delayed" title="ptbench.data.loader.make_delayed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_delayed</span></code></a>(sample, loader[, key])</p></td>
-<td><p>Returns a delayed-loading Sample object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.data.loader.load_pil">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.loader.</span></span><span class="sig-name descname"><span class="pre">load_pil</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/loader.html#load_pil"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.loader.load_pil" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads a sample data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The full path leading to the image to be loaded</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>image</strong> (<em>PIL.Image.Image</em>) – A PIL image</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.data.loader.load_pil_baw">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.loader.</span></span><span class="sig-name descname"><span class="pre">load_pil_baw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/loader.html#load_pil_baw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.loader.load_pil_baw" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads a sample data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The full path leading to the image to be loaded</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>image</strong> (<em>PIL.Image.Image</em>) – A PIL image in grayscale mode</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.data.loader.load_pil_rgb">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.loader.</span></span><span class="sig-name descname"><span class="pre">load_pil_rgb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/loader.html#load_pil_rgb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.loader.load_pil_rgb" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads a sample data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The full path leading to the image to be loaded</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>image</strong> (<em>PIL.Image.Image</em>) – A PIL image in RGB mode</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.data.loader.make_delayed">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.loader.</span></span><span class="sig-name descname"><span class="pre">make_delayed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/loader.html#make_delayed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.loader.make_delayed" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns a delayed-loading Sample object.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – A dictionary that maps field names to sample data values (e.g. paths)</p></li>
-<li><p><strong>loader</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – A function that inputs <code class="docutils literal notranslate"><span class="pre">sample</span></code> dictionaries and returns the loaded
-data.</p></li>
-<li><p><strong>key</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – A unique key identifier for this sample. If not provided, assumes
-<code class="docutils literal notranslate"><span class="pre">sample</span></code> is a dictionary with a <code class="docutils literal notranslate"><span class="pre">data</span></code> entry and uses its path as
-key.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>sample</strong> (<em>ptbench.data.sample.DelayedSample</em>) – In which <code class="docutils literal notranslate"><span class="pre">key</span></code> is as provided and <code class="docutils literal notranslate"><span class="pre">data</span></code> can be accessed to trigger
-sample loading.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
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-<li><a class="reference internal" href="#ptbench.data.loader.load_pil"><code class="docutils literal notranslate"><span class="pre">load_pil()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.data.loader.load_pil_baw"><code class="docutils literal notranslate"><span class="pre">load_pil_baw()</span></code></a></li>
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- <article role="main">
- <section id="module-ptbench.data.sample">
-<span id="ptbench-data-sample"></span><h1>ptbench.data.sample<a class="headerlink" href="#module-ptbench.data.sample" title="Permalink to this heading">#</a></h1>
-<p>Base definition of sample.</p>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.sample.DelayedSample" title="ptbench.data.sample.DelayedSample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DelayedSample</span></code></a>(load[, parent])</p></td>
-<td><p>Representation of sample that can be loaded via a callable.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.data.sample.Sample" title="ptbench.data.sample.Sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sample</span></code></a>(data[, parent])</p></td>
-<td><p>Representation of sample that is sufficient for the blocks in this module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.sample.DelayedSample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.sample.</span></span><span class="sig-name descname"><span class="pre">DelayedSample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">load</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/sample.html#DelayedSample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.sample.DelayedSample" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Representation of sample that can be loaded via a callable.</p>
-<p>The optional <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> argument allows you to attach more attributes to
-this sample instance.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>load</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – A python function that can be called parameterlessly, to load the
-sample in question from whatever medium</p></li>
-<li><p><strong>parent</strong> (<a class="reference internal" href="#ptbench.data.sample.DelayedSample" title="ptbench.data.sample.DelayedSample"><code class="xref py py-class docutils literal notranslate"><span class="pre">DelayedSample</span></code></a>, <a class="reference internal" href="#ptbench.data.sample.Sample" title="ptbench.data.sample.Sample"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sample</span></code></a>, None) – If passed, consider this as a parent of this sample, to copy
-information</p></li>
-<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – Further attributes of this sample, to be stored and eventually
-transmitted to transformed versions of the sample</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py property">
-<dt class="sig sig-object py" id="ptbench.data.sample.DelayedSample.data">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#ptbench.data.sample.DelayedSample.data" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads the data from the disk file.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.sample.Sample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.sample.</span></span><span class="sig-name descname"><span class="pre">Sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/sample.html#Sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.sample.Sample" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Representation of sample that is sufficient for the blocks in this
-module.</p>
-<p>Each sample must have the following attributes:</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>attribute <code class="docutils literal notranslate"><span class="pre">data</span></code>: Contains the data for this sample</p></li>
-</ul>
-</div></blockquote>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – Object representing the data to initialize this sample with.</p></li>
-<li><p><strong>parent</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><em>object</em></a>) – A parent object from which to inherit all other attributes (except
-<code class="docutils literal notranslate"><span class="pre">data</span></code>)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
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- <section id="module-ptbench.data.transforms">
-<span id="ptbench-data-transforms"></span><h1>ptbench.data.transforms<a class="headerlink" href="#module-ptbench.data.transforms" title="Permalink to this heading">#</a></h1>
-<p>Image transformations for our pipelines.</p>
-<p>Differences between methods here and those from
-<a class="reference external" href="https://pytorch.org/vision/stable/transforms.html" title="(in extras vstable)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torchvision.transforms</span></code></a> is that these support multiple
-simultaneous image inputs, which are required to feed segmentation
-networks (e.g. image and labels or masks). We also take care of data
-augmentations, in which random flipping and rotation needs to be applied
-across all input images, but color jittering, for example, only on the
-input image.</p>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.transforms.ElasticDeformation" title="ptbench.data.transforms.ElasticDeformation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticDeformation</span></code></a>([alpha, sigma, ...])</p></td>
-<td><p>Elastic deformation of 2D image slightly adapted from <a class="reference internal" href="../../references.html#simard-2003" id="id1"><span>[SIMARD-2003]</span></a>.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.data.transforms.RemoveBlackBorders" title="ptbench.data.transforms.RemoveBlackBorders"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RemoveBlackBorders</span></code></a>([threshold])</p></td>
-<td><p>Remove black borders of CXR.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.transforms.SingleAutoLevel16to8" title="ptbench.data.transforms.SingleAutoLevel16to8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SingleAutoLevel16to8</span></code></a>()</p></td>
-<td><p>Converts a 16-bit image to 8-bit representation using "auto-level".</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.transforms.SingleAutoLevel16to8">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.transforms.</span></span><span class="sig-name descname"><span class="pre">SingleAutoLevel16to8</span></span><a class="reference internal" href="../../_modules/ptbench/data/transforms.html#SingleAutoLevel16to8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.transforms.SingleAutoLevel16to8" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Converts a 16-bit image to 8-bit representation using “auto-levelâ€.</p>
-<p>This transform assumes that the input image is gray-scaled.</p>
-<p>To auto-level, we calculate the maximum and the minimum of the
-image, and
-consider such a range should be mapped to the [0,255] range of the
-destination image.</p>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.transforms.RemoveBlackBorders">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.transforms.</span></span><span class="sig-name descname"><span class="pre">RemoveBlackBorders</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/transforms.html#RemoveBlackBorders"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.transforms.RemoveBlackBorders" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Remove black borders of CXR.</p>
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-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.transforms.</span></span><span class="sig-name descname"><span class="pre">ElasticDeformation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha=1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spline_order=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode='nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state=<module</span> <span class="pre">'numpy.random'</span> <span class="pre">from</span> <span class="pre">'/remote/idiap.svm/temp.biosignal01/ogueler/mambaforge/envs/dev/lib/python3.10/site-packages/numpy/random/__init__.py'></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p=1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/transforms.html#ElasticDeformation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.transforms.ElasticDeformation" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Elastic deformation of 2D image slightly adapted from <a class="reference internal" href="../../references.html#simard-2003" id="id2"><span>[SIMARD-2003]</span></a>.</p>
-<p>Source: <a class="reference external" href="https://gist.github.com/oeway/2e3b989e0343f0884388ed7ed82eb3b0">https://gist.github.com/oeway/2e3b989e0343f0884388ed7ed82eb3b0</a></p>
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- <section id="module-ptbench.data.utils">
-<span id="ptbench-data-utils"></span><h1>ptbench.data.utils<a class="headerlink" href="#module-ptbench.data.utils" title="Permalink to this heading">#</a></h1>
-<p>Common utilities.</p>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.utils.SampleListDataset" title="ptbench.data.utils.SampleListDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SampleListDataset</span></code></a>(samples[, transforms])</p></td>
-<td><p>PyTorch dataset wrapper around Sample lists.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.data.utils.SampleListDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.data.utils.</span></span><span class="sig-name descname"><span class="pre">SampleListDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/utils.html#SampleListDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.utils.SampleListDataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a></p>
-<p>PyTorch dataset wrapper around Sample lists.</p>
-<p>A transform object can be passed that will be applied to the image, ground
-truth and mask (if present).</p>
-<p>It supports indexing such that dataset[i] can be used to get ith sample.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of <a class="reference internal" href="ptbench.data.sample.html#ptbench.data.sample.Sample" title="ptbench.data.sample.Sample"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.data.sample.Sample</span></code></a> objects</p></li>
-<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>, Optional) – a list of transformations to be applied to <strong>both</strong> image and
-ground-truth data. Notice a last transform
-(<a class="reference external" href="https://pytorch.org/vision/stable/generated/torchvision.transforms.ToTensor.html#torchvision.transforms.ToTensor" title="(in Torchvision vmain (0.14.1a0+5e8e2f1 ))"><code class="xref py py-class docutils literal notranslate"><span class="pre">torchvision.transforms.transforms.ToTensor</span></code></a>) is always
-applied - you do not need to add that.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py property">
-<dt class="sig sig-object py" id="ptbench.data.utils.SampleListDataset.transforms">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transforms</span></span><a class="headerlink" href="#ptbench.data.utils.SampleListDataset.transforms" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.utils.SampleListDataset.copy">
-<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/utils.html#SampleListDataset.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.utils.SampleListDataset.copy" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns a deep copy of itself, optionally resetting transforms.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>, Optional) – An optional list of transforms to set in the copy. If not
-specified, use <code class="docutils literal notranslate"><span class="pre">self.transforms</span></code>.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.data.utils.SampleListDataset.random_permute">
-<span class="sig-name descname"><span class="pre">random_permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">feature</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/data/utils.html#SampleListDataset.random_permute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.data.utils.SampleListDataset.random_permute" title="Permalink to this definition">#</a></dt>
-<dd><p>Randomly permute feature values from all samples.</p>
-<p>Useful for permutation feature importance computation</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>feature</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – The position of the feature</p>
-</dd>
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-<li><a class="reference internal" href="#ptbench.data.utils.SampleListDataset"><code class="docutils literal notranslate"><span class="pre">SampleListDataset</span></code></a><ul>
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-<span id="ptbench-data-hivtb"></span><h1>ptbench.data.hivtb<a class="headerlink" href="#module-ptbench.data.hivtb" title="Permalink to this heading">#</a></h1>
-<p>HIV-TB dataset for computer-aided diagnosis (only BMP files)</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#hiv-tb-2019" id="id1"><span>[HIV-TB-2019]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 2048 x 2500</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Stratified kfold protocol:</p>
-<ul>
-<li><p>Training samples: 72% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 18% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 10% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.hivtb.dataset" title="ptbench.data.hivtb.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>HIV-TB dataset object.</p></td>
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-<span id="ptbench-data-hivtb-rs"></span><h1>ptbench.data.hivtb_RS<a class="headerlink" href="#module-ptbench.data.hivtb_RS" title="Permalink to this heading">#</a></h1>
-<p>HIV-TB dataset for computer-aided diagnosis (only BMP files)</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#hiv-tb-2019" id="id1"><span>[HIV-TB-2019]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 2048 x 2500</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Stratified kfold protocol:</p>
-<ul>
-<li><p>Training samples: 72% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 18% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 10% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.hivtb_RS.dataset" title="ptbench.data.hivtb_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Extended HIV-TB dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.hivtb_RS.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.hivtb_RS.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.hivtb_RS.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Extended HIV-TB dataset object.</p>
-</dd></dl>
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-<span id="ptbench-data-indian"></span><h1>ptbench.data.indian<a class="headerlink" href="#module-ptbench.data.indian" title="Permalink to this heading">#</a></h1>
-<p>Indian collection dataset for computer-aided diagnosis.</p>
-<p>The Indian collection database has been established to foster research
-in computer-aided diagnosis of pulmonary diseases with a special
-focus on pulmonary tuberculosis (TB).</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): more than 1024 x 1024</p></li>
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id2"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.indian.dataset" title="ptbench.data.indian.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Indian dataset object.</p></td>
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- <section id="module-ptbench.data.indian_RS">
-<span id="ptbench-data-indian-rs"></span><h1>ptbench.data.indian_RS<a class="headerlink" href="#module-ptbench.data.indian_RS" title="Permalink to this heading">#</a></h1>
-<p>Indian collection dataset for computer-aided diagnosis (extended with
-DensenetRS predictions)</p>
-<p>The Indian collection database has been established to foster research
-in computer-aided diagnosis of pulmonary diseases with a special
-focus on pulmonary tuberculosis (TB).</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id1"><span>[INDIAN-2013]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): more than 1024 x 1024</p></li>
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#indian-2013" id="id2"><span>[INDIAN-2013]</span></a> with 20% of train set for the validation set</p></li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.indian_RS.dataset" title="ptbench.data.indian_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Extended Indian dataset object.</p></td>
-</tr>
-</tbody>
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-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.indian_RS.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.indian_RS.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.indian_RS.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Extended Indian dataset object.</p>
-</dd></dl>
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-<span id="ptbench-data-montgomery"></span><h1>ptbench.data.montgomery<a class="headerlink" href="#module-ptbench.data.montgomery" title="Permalink to this heading">#</a></h1>
-<p>Montgomery dataset for computer-aided diagnosis.</p>
-<p>The Montgomery database has been established to foster research
-in computer-aided diagnosis of pulmonary diseases with a special
-focus on pulmonary tuberculosis (TB).</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#montgomery-shenzhen-2014" id="id1"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 4020 x 4892</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 64% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 16% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 20% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.montgomery.dataset" title="ptbench.data.montgomery.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Montgomery dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.montgomery.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.montgomery.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.montgomery.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Montgomery dataset object.</p>
-</dd></dl>
-
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- <section id="module-ptbench.data.montgomery_RS">
-<span id="ptbench-data-montgomery-rs"></span><h1>ptbench.data.montgomery_RS<a class="headerlink" href="#module-ptbench.data.montgomery_RS" title="Permalink to this heading">#</a></h1>
-<p>Extended Montgomery dataset for computer-aided diagnosis (extended with
-DensenetRS predictions)</p>
-<p>The Montgomery database has been established to foster research
-in computer-aided diagnosis of pulmonary diseases with a special
-focus on pulmonary tuberculosis (TB).</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#montgomery-shenzhen-2014" id="id1"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 4020 x 4892</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 64% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 16% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 20% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.montgomery_RS.dataset" title="ptbench.data.montgomery_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Extended Montgomery dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.montgomery_RS.dataset">
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-<span id="ptbench-data-nih-cxr14-re"></span><h1>ptbench.data.nih_cxr14_re<a class="headerlink" href="#module-ptbench.data.nih_cxr14_re" title="Permalink to this heading">#</a></h1>
-<p>NIH CXR14 (relabeled) dataset for computer-aided diagnosis.</p>
-<p>This dataset was extracted from the clinical PACS database at the National
-Institutes of Health Clinical Center (USA) and represents 60% of all
-their radiographs. It contains labels for fourteen common radiological
-signs in this order: cardiomegaly, emphysema, effusion, hernia, infiltration,
-mass, nodule, atelectasis, pneumothorax, pleural thickening, pneumonia,
-fibrosis, edema and consolidation.
-This is the relabeled version created in the CheXNeXt study.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#nih-cxr14-2017" id="id1"><span>[NIH-CXR14-2017]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 1024 x 1024</p></li>
-<li><p>Labels: <a class="reference internal" href="../../../references.html#chexnext-2018" id="id2"><span>[CHEXNEXT-2018]</span></a></p></li>
-<li><p>Split reference: <a class="reference internal" href="../../../references.html#chexnext-2018" id="id3"><span>[CHEXNEXT-2018]</span></a></p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 98’637 (including labels)</p></li>
-<li><p>Validation samples: 6’350 (including labels)</p></li>
-<li><p>Test samples: 0</p></li>
-</ul>
-</li>
-<li><p>Protocol <cite>ìdiap`</cite>:
-* Images path adapted to Idiap infrastructure</p></li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.nih_cxr14_re.dataset" title="ptbench.data.nih_cxr14_re.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>NIH CXR14 (relabeled) dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.nih_cxr14_re.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.nih_cxr14_re.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.nih_cxr14_re.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>NIH CXR14 (relabeled) dataset object.</p>
-</dd></dl>
-
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-
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- <article role="main">
- <section id="module-ptbench.data.padchest">
-<span id="ptbench-data-padchest"></span><h1>ptbench.data.padchest<a class="headerlink" href="#module-ptbench.data.padchest" title="Permalink to this heading">#</a></h1>
-<p>Padchest dataset for computer-aided diagnosis.</p>
-<p>A large chest x-ray image dataset with multi-label annotated reports.
-This dataset includes more than 160,000 images from 67,000 patients that were
-interpreted and reported by radiologists at Hospital San Juan (Spain) from 2009
-to 2017, covering six different position views and additional information on
-image acquisition and patient demography.</p>
-<p>We keep only “PA†images here.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#padchest-2019" id="id1"><span>[PADCHEST-2019]</span></a></p></li>
-<li><p>Original resolution: variable, original size</p></li>
-<li><p>Labels: <a class="reference internal" href="../../../references.html#padchest-2019" id="id2"><span>[PADCHEST-2019]</span></a></p></li>
-<li><p>Split reference: no split</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 96’269</p></li>
-<li><p>Validation samples: 0</p></li>
-<li><p>Test samples: 0</p></li>
-</ul>
-</li>
-<li><p>Protocol <cite>ìdiap`</cite>:
-* Images path adapted to Idiap infrastructure</p></li>
-<li><p>Labels:</p></li>
-</ul>
-<p>[‘COPD signs’,
-‘Chilaiditi sign’,
-‘NSG tube’,
-‘abnormal foreign body’,
-‘abscess’,
-‘adenopathy’,
-‘air bronchogram’,
-‘air fluid level’,
-‘air trapping’,
-‘alveolar pattern’,
-‘aortic aneurysm’,
-‘aortic atheromatosis’,
-‘aortic button enlargement’,
-‘aortic elongation’,
-‘aortic endoprosthesis’,
-‘apical pleural thickening’,
-‘artificial aortic heart valve’,
-‘artificial heart valve’,
-‘artificial mitral heart valve’,
-‘asbestosis signs’,
-‘ascendent aortic elongation’,
-‘atelectasis’,
-‘atelectasis basal’,
-‘atypical pneumonia’,
-‘axial hyperostosis’,
-‘azygoesophageal recess shift’,
-‘azygos lobe’,
-‘blastic bone lesion’,
-‘bone cement’,
-‘bone metastasis’,
-‘breast mass’,
-‘bronchiectasis’,
-‘bronchovascular markings’,
-‘bullas’,
-‘calcified adenopathy’,
-‘calcified densities’,
-‘calcified fibroadenoma’,
-‘calcified granuloma’,
-‘calcified mediastinal adenopathy’,
-‘calcified pleural plaques’,
-‘calcified pleural thickening’,
-‘callus rib fracture’,
-‘cardiomegaly’,
-‘catheter’,
-‘cavitation’,
-‘central vascular redistribution’,
-‘central venous catheter’,
-‘central venous catheter via jugular vein’,
-‘central venous catheter via subclavian vein’,
-‘central venous catheter via umbilical vein’,
-‘cervical rib’,
-‘chest drain tube’,
-‘chronic changes’,
-‘clavicle fracture’,
-‘consolidation’,
-‘costochondral junction hypertrophy’,
-‘costophrenic angle blunting’,
-‘cyst’,
-‘dai’,
-‘descendent aortic elongation’,
-‘dextrocardia’,
-‘diaphragmatic eventration’,
-‘double J stent’,
-‘dual chamber device’,
-‘electrical device’,
-‘emphysema’,
-‘empyema’,
-‘end on vessel’,
-‘endoprosthesis’,
-‘endotracheal tube’,
-‘esophagic dilatation’,
-‘exclude’,
-‘external foreign body’,
-‘fibrotic band’,
-‘fissure thickening’,
-‘flattened diaphragm’,
-‘fracture’,
-‘gastrostomy tube’,
-‘goiter’,
-‘granuloma’,
-‘ground glass pattern’,
-‘gynecomastia’,
-‘heart insufficiency’,
-‘heart valve calcified’,
-‘hemidiaphragm elevation’,
-‘hiatal hernia’,
-‘hilar congestion’,
-‘hilar enlargement’,
-‘humeral fracture’,
-‘humeral prosthesis’,
-‘hydropneumothorax’,
-‘hyperinflated lung’,
-‘hypoexpansion’,
-‘hypoexpansion basal’,
-‘increased density’,
-‘infiltrates’,
-‘interstitial pattern’,
-‘kerley lines’,
-‘kyphosis’,
-‘laminar atelectasis’,
-‘lepidic adenocarcinoma’,
-‘lipomatosis’,
-‘lobar atelectasis’,
-‘loculated fissural effusion’,
-‘loculated pleural effusion’,
-‘lung metastasis’,
-‘lung vascular paucity’,
-‘lymphangitis carcinomatosa’,
-‘lytic bone lesion’,
-‘major fissure thickening’,
-‘mammary prosthesis’,
-‘mass’,
-‘mastectomy’,
-‘mediastinal enlargement’,
-‘mediastinal mass’,
-‘mediastinal shift’,
-‘mediastinic lipomatosis’,
-‘metal’,
-‘miliary opacities’,
-‘minor fissure thickening’,
-‘multiple nodules’,
-‘nephrostomy tube’,
-‘nipple shadow’,
-‘nodule’,
-‘non axial articular degenerative changes’,
-‘normal’,
-‘obesity’,
-‘osteopenia’,
-‘osteoporosis’,
-‘osteosynthesis material’,
-‘pacemaker’,
-‘pectum carinatum’,
-‘pectum excavatum’,
-‘pericardial effusion’,
-‘pleural effusion’,
-‘pleural mass’,
-‘pleural plaques’,
-‘pleural thickening’,
-‘pneumomediastinum’,
-‘pneumonia’,
-‘pneumoperitoneo’,
-‘pneumothorax’,
-‘post radiotherapy changes’,
-‘prosthesis’,
-‘pseudonodule’,
-‘pulmonary artery enlargement’,
-‘pulmonary artery hypertension’,
-‘pulmonary edema’,
-‘pulmonary fibrosis’,
-‘pulmonary hypertension’,
-‘pulmonary mass’,
-‘pulmonary venous hypertension’,
-‘reservoir central venous catheter’,
-‘respiratory distress’,
-‘reticular interstitial pattern’,
-‘reticulonodular interstitial pattern’,
-‘rib fracture’,
-‘right sided aortic arch’,
-‘round atelectasis’,
-‘sclerotic bone lesion’,
-‘scoliosis’,
-‘segmental atelectasis’,
-‘single chamber device’,
-‘soft tissue mass’,
-‘sternoclavicular junction hypertrophy’,
-‘sternotomy’,
-‘subacromial space narrowing’,
-‘subcutaneous emphysema’,
-‘suboptimal study’,
-‘superior mediastinal enlargement’,
-‘supra aortic elongation’,
-‘surgery’,
-‘surgery breast’,
-‘surgery heart’,
-‘surgery humeral’,
-‘surgery lung’,
-‘surgery neck’,
-‘suture material’,
-‘thoracic cage deformation’,
-‘total atelectasis’,
-‘tracheal shift’,
-‘tracheostomy tube’,
-‘tuberculosis’,
-‘tuberculosis sequelae’,
-‘unchanged’,
-‘vascular hilar enlargement’,
-‘vascular redistribution’,
-‘ventriculoperitoneal drain tube’,
-‘vertebral anterior compression’,
-‘vertebral compression’,
-‘vertebral degenerative changes’,
-‘vertebral fracture’,
-‘volume loss’]</p>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.padchest.dataset" title="ptbench.data.padchest.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Padchest dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.padchest.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.padchest.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.padchest.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Padchest dataset object.</p>
-</dd></dl>
-
-</section>
-
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- <section id="module-ptbench.data.padchest_RS">
-<span id="ptbench-data-padchest-rs"></span><h1>ptbench.data.padchest_RS<a class="headerlink" href="#module-ptbench.data.padchest_RS" title="Permalink to this heading">#</a></h1>
-<p>Padchest TB dataset for computer-aided diagnosis.</p>
-<p>A large chest x-ray image dataset with multi-label annotated reports.
-This dataset includes more than 160,000 images from 67,000 patients that were
-interpreted and reported by radiologists at Hospital San Juan (Spain) from 2009
-to 2017, covering six different position views and additional information on
-image acquisition and patient demography.</p>
-<p>We keep only “PA†images here and only the “Tuberculosis†subset with an
-equivalent number of “normal†images.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#padchest-2019" id="id1"><span>[PADCHEST-2019]</span></a></p></li>
-<li><p>Original resolution: variable, original size</p></li>
-<li><p>Labels: <a class="reference internal" href="../../../references.html#padchest-2019" id="id2"><span>[PADCHEST-2019]</span></a></p></li>
-<li><p>Split reference: 64%/16%/20%</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 160</p></li>
-<li><p>Validation samples: 40</p></li>
-<li><p>Test samples: 50</p></li>
-</ul>
-</li>
-<li><p>Protocol <cite>ìdiap`</cite>:
-* Images path adapted to Idiap infrastructure</p></li>
-<li><p>Labels: DensenetRS predictions</p></li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.padchest_RS.dataset" title="ptbench.data.padchest_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Padchest dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.padchest_RS.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.padchest_RS.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.padchest_RS.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Padchest dataset object.</p>
-</dd></dl>
-
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- <section id="module-ptbench.data.shenzhen">
-<span id="ptbench-data-shenzhen"></span><h1>ptbench.data.shenzhen<a class="headerlink" href="#module-ptbench.data.shenzhen" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for computer-aided diagnosis.</p>
-<p>The standard digital image database for Tuberculosis is created by the
-National Library of Medicine, Maryland, USA in collaboration with Shenzhen
-No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China.
-The Chest X-rays are from out-patient clinics, and were captured as part of
-the daily routine using Philips DR Digital Diagnose systems.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#montgomery-shenzhen-2014" id="id1"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 3000 x 3000 or less</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 64% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 16% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 20% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.shenzhen.dataset" title="ptbench.data.shenzhen.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Shenzhen dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.shenzhen.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.shenzhen.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.shenzhen.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Shenzhen dataset object.</p>
-</dd></dl>
-
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- <section id="module-ptbench.data.shenzhen_RS">
-<span id="ptbench-data-shenzhen-rs"></span><h1>ptbench.data.shenzhen_RS<a class="headerlink" href="#module-ptbench.data.shenzhen_RS" title="Permalink to this heading">#</a></h1>
-<p>Shenzhen dataset for computer-aided diagnosis (extended with DensenetRS
-predictions)</p>
-<p>The standard digital image database for Tuberculosis is created by the
-National Library of Medicine, Maryland, USA in collaboration with Shenzhen
-No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China.
-The Chest X-rays are from out-patient clinics, and were captured as part of
-the daily routine using Philips DR Digital Diagnose systems.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#montgomery-shenzhen-2014" id="id1"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 3000 x 3000 or less</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Protocol <code class="docutils literal notranslate"><span class="pre">default</span></code>:</p>
-<ul>
-<li><p>Training samples: 64% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 16% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 20% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.shenzhen_RS.dataset" title="ptbench.data.shenzhen_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Extended Shenzhen dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.shenzhen_RS.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.shenzhen_RS.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.shenzhen_RS.dataset" title="Permalink to this definition">#</a></dt>
-<dd><p>Extended Shenzhen dataset object.</p>
-</dd></dl>
-
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-<span id="ptbench-data-tbpoc"></span><h1>ptbench.data.tbpoc<a class="headerlink" href="#module-ptbench.data.tbpoc" title="Permalink to this heading">#</a></h1>
-<p>TB-POC dataset for computer-aided diagnosis.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#tb-poc-2018" id="id1"><span>[TB-POC-2018]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 2048 x 2500</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Stratified kfold protocol:</p>
-<ul>
-<li><p>Training samples: 72% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 18% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 10% of TB and healthy CXR (including labels)</p></li>
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-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
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-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.tbpoc.dataset" title="ptbench.data.tbpoc.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>TB-POC dataset object.</p></td>
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- <section id="module-ptbench.data.tbpoc_RS">
-<span id="ptbench-data-tbpoc-rs"></span><h1>ptbench.data.tbpoc_RS<a class="headerlink" href="#module-ptbench.data.tbpoc_RS" title="Permalink to this heading">#</a></h1>
-<p>TB-POC dataset for computer-aided diagnosis.</p>
-<ul class="simple">
-<li><p>Reference: <a class="reference internal" href="../../../references.html#tb-poc-2018" id="id1"><span>[TB-POC-2018]</span></a></p></li>
-<li><p>Original resolution (height x width or width x height): 2048 x 2500</p></li>
-<li><p>Split reference: none</p></li>
-<li><p>Stratified kfold protocol:</p>
-<ul>
-<li><p>Training samples: 72% of TB and healthy CXR (including labels)</p></li>
-<li><p>Validation samples: 18% of TB and healthy CXR (including labels)</p></li>
-<li><p>Test samples: 10% of TB and healthy CXR (including labels)</p></li>
-</ul>
-</li>
-</ul>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.data.tbpoc_RS.dataset" title="ptbench.data.tbpoc_RS.dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code></a></p></td>
-<td><p>Extended TB-POC dataset object.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.data.tbpoc_RS.dataset">
-<span class="sig-prename descclassname"><span class="pre">ptbench.data.tbpoc_RS.</span></span><span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre"><ptbench.data.dataset.JSONDataset</span> <span class="pre">object></span></em><a class="headerlink" href="#ptbench.data.tbpoc_RS.dataset" title="Permalink to this definition">#</a></dt>
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- <section id="module-ptbench.engine.evaluator">
-<span id="ptbench-engine-evaluator"></span><h1>ptbench.engine.evaluator<a class="headerlink" href="#module-ptbench.engine.evaluator" title="Permalink to this heading">#</a></h1>
-<p>Defines functionality for the evaluation of predictions.</p>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.evaluator.eer_threshold" title="ptbench.engine.evaluator.eer_threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eer_threshold</span></code></a>(neg, pos)</p></td>
-<td><p>Evaluates the EER threshold from negative and positive scores.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.evaluator.posneg" title="ptbench.engine.evaluator.posneg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">posneg</span></code></a>(pred, gt, threshold)</p></td>
-<td><p>Calculates true and false positives and negatives.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.evaluator.run" title="ptbench.engine.evaluator.run"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run</span></code></a>(dataset, name, predictions_folder[, ...])</p></td>
-<td><p>Runs inference and calculates measures.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.evaluator.sample_measures_for_threshold" title="ptbench.engine.evaluator.sample_measures_for_threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_measures_for_threshold</span></code></a>(pred, gt, ...)</p></td>
-<td><p>Calculates measures on one single sample, for a specific threshold.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.evaluator.eer_threshold">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.evaluator.</span></span><span class="sig-name descname"><span class="pre">eer_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/evaluator.html#eer_threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.evaluator.eer_threshold" title="Permalink to this definition">#</a></dt>
-<dd><p>Evaluates the EER threshold from negative and positive scores.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.11)"><em>Iterable</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a><em>]</em>) – Negative scores</p></li>
-<li><p><strong>pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.11)"><em>Iterable</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a><em>]</em>) – Positive scores</p></li>
-</ul>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
-</dd>
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>Threshold</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.evaluator.posneg">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.evaluator.</span></span><span class="sig-name descname"><span class="pre">posneg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/evaluator.html#posneg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.evaluator.posneg" title="Permalink to this definition">#</a></dt>
-<dd><p>Calculates true and false positives and negatives.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.evaluator.sample_measures_for_threshold">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.evaluator.</span></span><span class="sig-name descname"><span class="pre">sample_measures_for_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/evaluator.html#sample_measures_for_threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.evaluator.sample_measures_for_threshold" title="Permalink to this definition">#</a></dt>
-<dd><p>Calculates measures on one single sample, for a specific threshold.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>pred</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><em>torch.Tensor</em></a>) – pixel-wise predictions</p></li>
-<li><p><strong>gt</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><em>torch.Tensor</em></a>) – ground-truth (annotations)</p></li>
-<li><p><strong>threshold</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – a particular threshold in which to calculate the performance
-measures</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>precision</strong> (<em>float</em>)</p></li>
-<li><p><strong>recall</strong> (<em>float</em>)</p></li>
-<li><p><strong>specificity</strong> (<em>float</em>)</p></li>
-<li><p><strong>accuracy</strong> (<em>float</em>)</p></li>
-<li><p><strong>jaccard</strong> (<em>float</em>)</p></li>
-<li><p><strong>f1_score</strong> (<em>float</em>)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.evaluator.run">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.evaluator.</span></span><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f1_thresh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eer_thresh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/evaluator.html#run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.evaluator.run" title="Permalink to this definition">#</a></dt>
-<dd><p>Runs inference and calculates measures.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataset</strong> (py:class:<cite>torch.utils.data.Dataset</cite>) – a dataset to iterate on</p></li>
-<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – the local name of this dataset (e.g. <code class="docutils literal notranslate"><span class="pre">train</span></code>, or <code class="docutils literal notranslate"><span class="pre">test</span></code>), to be
-used when saving measures files.</p></li>
-<li><p><strong>predictions_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – folder where predictions for the dataset images has been previously
-stored</p></li>
-<li><p><strong>output_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, Optional) – folder where to store results.</p></li>
-<li><p><strong>f1_thresh</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, Optional) – This number should come from
-the training set or a separate validation set. Using a test set value
-may bias your analysis. This number is also used to print the a priori
-F1-score on the evaluated set.</p></li>
-<li><p><strong>eer_thresh</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, Optional) – This number should come from
-the training set or a separate validation set. Using a test set value
-may bias your analysis. This number is used to print the a priori
-EER.</p></li>
-<li><p><strong>steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, Optional) – number of threshold steps to consider when evaluating thresholds.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>f1_threshold</strong> (<em>float</em>) – Threshold to achieve the highest possible F1-score for this dataset</p></li>
-<li><p><strong>eer_threshold</strong> (<em>float</em>) – Threshold achieving Equal Error Rate for this dataset</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
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-<span id="ptbench-engine-predictor"></span><h1>ptbench.engine.predictor<a class="headerlink" href="#module-ptbench.engine.predictor" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
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-<td><p>Runs inference on input data, outputs HDF5 files with predictions.</p></td>
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-<dd><p>Runs inference on input data, outputs HDF5 files with predictions.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – neural network model (e.g. pasa)</p></li>
-<li><p><strong>data_loader</strong> (py:class:<cite>torch.torch.utils.data.DataLoader</cite>) – </p></li>
-<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – the local name of this dataset (e.g. <code class="docutils literal notranslate"><span class="pre">train</span></code>, or <code class="docutils literal notranslate"><span class="pre">test</span></code>), to be
-used when saving measures files.</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – device to use <code class="docutils literal notranslate"><span class="pre">cpu</span></code> or <code class="docutils literal notranslate"><span class="pre">cuda:0</span></code></p></li>
-<li><p><strong>output_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – folder where to store output prediction and model
-summary</p></li>
-<li><p><strong>grad_cams</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><em>bool</em></a>) – if we export grad cams for every prediction (must be used along
-a batch size of 1 with the DensenetRS model)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>all_predictions</strong> (<em>list</em>) – All the predictions associated with filename and groundtruth</p></li>
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- <article role="main">
- <section id="module-ptbench.engine.trainer">
-<span id="ptbench-engine-trainer"></span><h1>ptbench.engine.trainer<a class="headerlink" href="#module-ptbench.engine.trainer" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.check_exist_logfile" title="ptbench.engine.trainer.check_exist_logfile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_exist_logfile</span></code></a>(logfile_name, arguments)</p></td>
-<td><p>Check existance of logfile (trainlog.csv), If the logfile exist the and the epochs number are still 0, The logfile will be replaced.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.trainer.check_gpu" title="ptbench.engine.trainer.check_gpu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_gpu</span></code></a>(device)</p></td>
-<td><p>Check the device type and the availability of GPU.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.checkpointer_process" title="ptbench.engine.trainer.checkpointer_process"><code class="xref py py-obj docutils literal notranslate"><span class="pre">checkpointer_process</span></code></a>(checkpointer, ...)</p></td>
-<td><p>Process the checkpointer, save the final model and keep track of the best model.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.trainer.create_logfile_fields" title="ptbench.engine.trainer.create_logfile_fields"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_logfile_fields</span></code></a>(valid_loader, ...)</p></td>
-<td><p>Creation of the logfile fields that will appear in the logfile.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.run" title="ptbench.engine.trainer.run"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run</span></code></a>(model, data_loader, valid_loader, ...)</p></td>
-<td><p>Fits a CNN model using supervised learning and save it to disk.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.trainer.save_model_summary" title="ptbench.engine.trainer.save_model_summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model_summary</span></code></a>(output_folder, model)</p></td>
-<td><p>Save a little summary of the model in a txt file.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.static_information_to_csv" title="ptbench.engine.trainer.static_information_to_csv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">static_information_to_csv</span></code></a>(...)</p></td>
-<td><p>Save the static information in a csv file.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.trainer.torch_evaluation" title="ptbench.engine.trainer.torch_evaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">torch_evaluation</span></code></a>(model)</p></td>
-<td><p>Context manager to turn ON/OFF model evaluation.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.train_epoch" title="ptbench.engine.trainer.train_epoch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_epoch</span></code></a>(loader, model, optimizer, ...)</p></td>
-<td><p>Trains the model for a single epoch (through all batches)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.engine.trainer.validate_epoch" title="ptbench.engine.trainer.validate_epoch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">validate_epoch</span></code></a>(loader, model, device, ...)</p></td>
-<td><p>Processes input samples and returns loss (scalar)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.engine.trainer.write_log_info" title="ptbench.engine.trainer.write_log_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_log_info</span></code></a>(epoch, current_time, ...)</p></td>
-<td><p>Write log info in trainlog.csv.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.torch_evaluation">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">torch_evaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#torch_evaluation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.torch_evaluation" title="Permalink to this definition">#</a></dt>
-<dd><p>Context manager to turn ON/OFF model evaluation.</p>
-<p>This context manager will turn evaluation mode ON on entry and turn it OFF
-when exiting the <code class="docutils literal notranslate"><span class="pre">with</span></code> statement block.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network</p>
-</dd>
-<dt class="field-even">Yields<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.check_gpu">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">check_gpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#check_gpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.check_gpu" title="Permalink to this definition">#</a></dt>
-<dd><p>Check the device type and the availability of GPU.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>device</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>) – device to use</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.save_model_summary">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">save_model_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#save_model_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.save_model_summary" title="Permalink to this definition">#</a></dt>
-<dd><p>Save a little summary of the model in a txt file.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>output_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – output path</p></li>
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network (e.g. driu, hed, unet)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>r</strong> (<em>str</em>) – The model summary in a text format.</p></li>
-<li><p><strong>n</strong> (<em>int</em>) – The number of parameters of the model.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.static_information_to_csv">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">static_information_to_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">static_logfile_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#static_information_to_csv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.static_information_to_csv" title="Permalink to this definition">#</a></dt>
-<dd><p>Save the static information in a csv file.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>static_logfile_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The static file name which is a join between the output folder and “constant.csvâ€</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.check_exist_logfile">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">check_exist_logfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logfile_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arguments</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#check_exist_logfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.check_exist_logfile" title="Permalink to this definition">#</a></dt>
-<dd><p>Check existance of logfile (trainlog.csv), If the logfile exist the and
-the epochs number are still 0, The logfile will be replaced.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>logfile_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The logfile_name which is a join between the output_folder and trainlog.csv</p></li>
-<li><p><strong>arguments</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – start and end epochs</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.create_logfile_fields">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">create_logfile_fields</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">valid_loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_valid_loaders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#create_logfile_fields"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.create_logfile_fields" title="Permalink to this definition">#</a></dt>
-<dd><p>Creation of the logfile fields that will appear in the logfile.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>valid_loader</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to validate the model and enable automatic checkpointing.
-If set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, then do not validate it.</p></li>
-<li><p><strong>extra_valid_loaders</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to validate the model, however <strong>does not affect</strong> automatic
-checkpointing. If set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, or empty, then does not log anything
-else. Otherwise, an extra column with the loss of every dataset in
-this list is kept on the final training log.</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>) – device to use</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>logfile_fields</strong> (<em>tuple</em>) – The fields that will appear in trainlog.csv</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.train_epoch">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">train_epoch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_chunk_count</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#train_epoch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.train_epoch" title="Permalink to this definition">#</a></dt>
-<dd><p>Trains the model for a single epoch (through all batches)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>loader</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to train the model</p></li>
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network (e.g. driu, hed, unet)</p></li>
-<li><p><strong>optimizer</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>) – </p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>) – device to use</p></li>
-<li><p><strong>criterion</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code>) – </p></li>
-<li><p><strong>batch_chunk_count</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – If this number is different than 1, then each batch will be divided in
-this number of chunks. Gradients will be accumulated to perform each
-mini-batch. This is particularly interesting when one has limited RAM
-on the GPU, but would like to keep training with larger batches. One
-exchanges for longer processing times in this case. To better understand
-gradient accumulation, read
-<a class="reference external" href="https://stackoverflow.com/questions/62067400/understanding-accumulated-gradients-in-pytorch">https://stackoverflow.com/questions/62067400/understanding-accumulated-gradients-in-pytorch</a>.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>loss</strong> (<em>float</em>) – A floating-point value corresponding the weighted average of this
-epoch’s loss</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.validate_epoch">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">validate_epoch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pbar_desc</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#validate_epoch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.validate_epoch" title="Permalink to this definition">#</a></dt>
-<dd><p>Processes input samples and returns loss (scalar)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>loader</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to validate the model</p></li>
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network (e.g. driu, hed, unet)</p></li>
-<li><p><strong>optimizer</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>) – </p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>) – device to use</p></li>
-<li><p><strong>criterion</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code>) – loss function</p></li>
-<li><p><strong>pbar_desc</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – A string for the progress bar descriptor</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>loss</strong> (<em>float</em>) – A floating-point value corresponding the weighted average of this
-epoch’s loss</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.checkpointer_process">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">checkpointer_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_period</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">valid_loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lowest_validation_loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arguments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_epoch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#checkpointer_process"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.checkpointer_process" title="Permalink to this definition">#</a></dt>
-<dd><p>Process the checkpointer, save the final model and keep track of the
-best model.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>checkpointer</strong> (<a class="reference internal" href="../utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer" title="ptbench.utils.checkpointer.Checkpointer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.utils.checkpointer.Checkpointer</span></code></a>) – checkpointer implementation</p></li>
-<li><p><strong>checkpoint_period</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – save a checkpoint every <code class="docutils literal notranslate"><span class="pre">n</span></code> epochs. If set to <code class="docutils literal notranslate"><span class="pre">0</span></code> (zero), then do
-not save intermediary checkpoints</p></li>
-<li><p><strong>valid_loss</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – Current epoch validation loss</p></li>
-<li><p><strong>lowest_validation_loss</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – Keeps track of the best (lowest) validation loss</p></li>
-<li><p><strong>arguments</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – start and end epochs</p></li>
-<li><p><strong>max_epoch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – end_potch</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>lowest_validation_loss</strong> (<em>float</em>) – The lowest validation loss currently observed</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.write_log_info">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">write_log_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">current_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_seconds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">valid_loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_valid_losses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logwriter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logfile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#write_log_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.write_log_info" title="Permalink to this definition">#</a></dt>
-<dd><p>Write log info in trainlog.csv.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>epoch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – Current epoch</p></li>
-<li><p><strong>current_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – Current training time</p></li>
-<li><p><strong>eta_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – estimated time-of-arrival taking into consideration previous epoch performance</p></li>
-<li><p><strong>loss</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – Current epoch’s training loss</p></li>
-<li><p><strong>valid_loss</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, None) – Current epoch’s validation loss</p></li>
-<li><p><strong>extra_valid_losses</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>) – Validation losses from other validation datasets being currently
-tracked</p></li>
-<li><p><strong>optimizer</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>) – </p></li>
-<li><p><strong>logwriter</strong> (<a class="reference external" href="https://docs.python.org/3/library/csv.html#csv.DictWriter" title="(in Python v3.11)"><em>csv.DictWriter</em></a>) – Dictionary writer that give the ability to write on the trainlog.csv</p></li>
-<li><p><strong>logfile</strong> (<a class="reference external" href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="(in Python v3.11)"><em>io.TextIOWrapper</em></a>) – </p></li>
-<li><p><strong>resource_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><em>tuple</em></a>) – Monitored resources at the machine (CPU and GPU)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.engine.trainer.run">
-<span class="sig-prename descclassname"><span class="pre">ptbench.engine.trainer.</span></span><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">valid_loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_valid_loaders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_period</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arguments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitoring_interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_chunk_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion_valid</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/engine/trainer.html#run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.engine.trainer.run" title="Permalink to this definition">#</a></dt>
-<dd><p>Fits a CNN model using supervised learning and save it to disk.</p>
-<p>This method supports periodic checkpointing and the output of a
-CSV-formatted log with the evolution of some figures during training.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – Network (e.g. driu, hed, unet)</p></li>
-<li><p><strong>data_loader</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to train the model</p></li>
-<li><p><strong>valid_loaders</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to validate the model and enable automatic checkpointing.
-If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then do not validate it.</p></li>
-<li><p><strong>extra_valid_loaders</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> of <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code></a>) – To be used to validate the model, however <strong>does not affect</strong> automatic
-checkpointing. If empty, then does not log anything else. Otherwise,
-an extra column with the loss of every dataset in this list is kept on
-the final training log.</p></li>
-<li><p><strong>optimizer</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>) – </p></li>
-<li><p><strong>criterion</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code>) – loss function</p></li>
-<li><p><strong>checkpointer</strong> (<a class="reference internal" href="../utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer" title="ptbench.utils.checkpointer.Checkpointer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ptbench.utils.checkpointer.Checkpointer</span></code></a>) – checkpointer implementation</p></li>
-<li><p><strong>checkpoint_period</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – save a checkpoint every <code class="docutils literal notranslate"><span class="pre">n</span></code> epochs. If set to <code class="docutils literal notranslate"><span class="pre">0</span></code> (zero), then do
-not save intermediary checkpoints</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>) – device to use</p></li>
-<li><p><strong>arguments</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – start and end epochs</p></li>
-<li><p><strong>output_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – output path</p></li>
-<li><p><strong>monitoring_interval</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – interval, in seconds (or fractions), through which we should monitor
-resources during training.</p></li>
-<li><p><strong>batch_chunk_count</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – If this number is different than 1, then each batch will be divided in
-this number of chunks. Gradients will be accumulated to perform each
-mini-batch. This is particularly interesting when one has limited RAM
-on the GPU, but would like to keep training with larger batches. One
-exchanges for longer processing times in this case.</p></li>
-<li><p><strong>criterion_valid</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.loss._Loss</span></code>) – specific loss function for the validation set</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-
- </article>
- </div>
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-<li><a class="reference internal" href="#ptbench.engine.trainer.torch_evaluation"><code class="docutils literal notranslate"><span class="pre">torch_evaluation()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.engine.trainer.check_gpu"><code class="docutils literal notranslate"><span class="pre">check_gpu()</span></code></a></li>
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-<span id="ptbench-models-alexnet"></span><h1>ptbench.models.alexnet<a class="headerlink" href="#module-ptbench.models.alexnet" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.alexnet.build_alexnet" title="ptbench.models.alexnet.build_alexnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_alexnet</span></code></a>([pretrained])</p></td>
-<td><p>Build Alexnet CNN.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.alexnet.Alexnet" title="ptbench.models.alexnet.Alexnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Alexnet</span></code></a>([pretrained])</p></td>
-<td><p>Alexnet module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.alexnet.Alexnet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.alexnet.</span></span><span class="sig-name descname"><span class="pre">Alexnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/alexnet.html#Alexnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.alexnet.Alexnet" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>Alexnet module.</p>
-<p>Note: only usable with a normalized dataset</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.alexnet.Alexnet.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/alexnet.html#Alexnet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.alexnet.Alexnet.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.alexnet.build_alexnet">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.alexnet.</span></span><span class="sig-name descname"><span class="pre">build_alexnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/alexnet.html#build_alexnet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.alexnet.build_alexnet" title="Permalink to this definition">#</a></dt>
-<dd><p>Build Alexnet CNN.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>)</p></li>
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- <section id="module-ptbench.models.densenet">
-<span id="ptbench-models-densenet"></span><h1>ptbench.models.densenet<a class="headerlink" href="#module-ptbench.models.densenet" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.densenet.build_densenet" title="ptbench.models.densenet.build_densenet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_densenet</span></code></a>([pretrained, nb_channels])</p></td>
-<td><p>Build Densenet CNN.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.densenet.Densenet" title="ptbench.models.densenet.Densenet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Densenet</span></code></a>([pretrained])</p></td>
-<td><p>Densenet module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.densenet.Densenet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.densenet.</span></span><span class="sig-name descname"><span class="pre">Densenet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/densenet.html#Densenet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet.Densenet" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>Densenet module.</p>
-<p>Note: only usable with a normalized dataset</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.densenet.Densenet.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/densenet.html#Densenet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet.Densenet.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.densenet.build_densenet">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.densenet.</span></span><span class="sig-name descname"><span class="pre">build_densenet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nb_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/densenet.html#build_densenet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet.build_densenet" title="Permalink to this definition">#</a></dt>
-<dd><p>Build Densenet CNN.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
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-<span id="ptbench-models-densenet-rs"></span><h1>ptbench.models.densenet_rs<a class="headerlink" href="#module-ptbench.models.densenet_rs" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.densenet_rs.build_densenetrs" title="ptbench.models.densenet_rs.build_densenetrs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_densenetrs</span></code></a>()</p></td>
-<td><p>Build DensenetRS CNN.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.densenet_rs.DensenetRS" title="ptbench.models.densenet_rs.DensenetRS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DensenetRS</span></code></a>()</p></td>
-<td><p>Densenet121 module for radiological extraction.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.densenet_rs.DensenetRS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.densenet_rs.</span></span><span class="sig-name descname"><span class="pre">DensenetRS</span></span><a class="reference internal" href="../../_modules/ptbench/models/densenet_rs.html#DensenetRS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet_rs.DensenetRS" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>Densenet121 module for radiological extraction.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.densenet_rs.DensenetRS.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/densenet_rs.html#DensenetRS.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet_rs.DensenetRS.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.densenet_rs.build_densenetrs">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.densenet_rs.</span></span><span class="sig-name descname"><span class="pre">build_densenetrs</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/densenet_rs.html#build_densenetrs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.densenet_rs.build_densenetrs" title="Permalink to this definition">#</a></dt>
-<dd><p>Build DensenetRS CNN.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>)</p></li>
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-<span id="ptbench-models-logistic-regression"></span><h1>ptbench.models.logistic_regression<a class="headerlink" href="#module-ptbench.models.logistic_regression" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.logistic_regression.build_logistic_regression" title="ptbench.models.logistic_regression.build_logistic_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_logistic_regression</span></code></a>(input_size)</p></td>
-<td><p>Build logistic regression module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.logistic_regression.LogisticRegression" title="ptbench.models.logistic_regression.LogisticRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>(input_size)</p></td>
-<td><p>Radiological signs to Tuberculosis module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.logistic_regression.LogisticRegression">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.logistic_regression.</span></span><span class="sig-name descname"><span class="pre">LogisticRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/logistic_regression.html#LogisticRegression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.logistic_regression.LogisticRegression" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>Radiological signs to Tuberculosis module.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.logistic_regression.LogisticRegression.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/logistic_regression.html#LogisticRegression.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.logistic_regression.LogisticRegression.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
-</ul>
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-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.logistic_regression.build_logistic_regression">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.logistic_regression.</span></span><span class="sig-name descname"><span class="pre">build_logistic_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/logistic_regression.html#build_logistic_regression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.logistic_regression.build_logistic_regression" title="Permalink to this definition">#</a></dt>
-<dd><p>Build logistic regression module.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>)</p></li>
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- <section id="module-ptbench.models.normalizer">
-<span id="ptbench-models-normalizer"></span><h1>ptbench.models.normalizer<a class="headerlink" href="#module-ptbench.models.normalizer" title="Permalink to this heading">#</a></h1>
-<p>A network model that prefixes a z-normalization step to any other module.</p>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.normalizer.TorchVisionNormalizer" title="ptbench.models.normalizer.TorchVisionNormalizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TorchVisionNormalizer</span></code></a>([nb_channels])</p></td>
-<td><p>A simple normalizer that applies the standard torchvision normalization.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.normalizer.TorchVisionNormalizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.normalizer.</span></span><span class="sig-name descname"><span class="pre">TorchVisionNormalizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nb_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/normalizer.html#TorchVisionNormalizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.normalizer.TorchVisionNormalizer" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>A simple normalizer that applies the standard torchvision normalization.</p>
-<p>This module does not learn.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>nb_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, Optional) – Number of images channels fed to the model</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.normalizer.TorchVisionNormalizer.set_mean_std">
-<span class="sig-name descname"><span class="pre">set_mean_std</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/normalizer.html#TorchVisionNormalizer.set_mean_std"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.normalizer.TorchVisionNormalizer.set_mean_std" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
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-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/normalizer.html#TorchVisionNormalizer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.normalizer.TorchVisionNormalizer.forward" title="Permalink to this definition">#</a></dt>
-<dd><p>Defines the computation performed at every call.</p>
-<p>Should be overridden by all subclasses.</p>
-<div class="admonition note">
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- <section id="module-ptbench.models.pasa">
-<span id="ptbench-models-pasa"></span><h1>ptbench.models.pasa<a class="headerlink" href="#module-ptbench.models.pasa" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.pasa.build_pasa" title="ptbench.models.pasa.build_pasa"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_pasa</span></code></a>()</p></td>
-<td><p>Build pasa CNN.</p></td>
-</tr>
-</tbody>
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-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
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-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.pasa.PASA" title="ptbench.models.pasa.PASA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PASA</span></code></a>()</p></td>
-<td><p>PASA module.</p></td>
-</tr>
-</tbody>
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-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.pasa.PASA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.pasa.</span></span><span class="sig-name descname"><span class="pre">PASA</span></span><a class="reference internal" href="../../_modules/ptbench/models/pasa.html#PASA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.pasa.PASA" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>PASA module.</p>
-<p>Based on paper by <a class="reference internal" href="../../references.html#pasa-2019" id="id1"><span>[PASA-2019]</span></a>.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.pasa.PASA.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/pasa.html#PASA.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.pasa.PASA.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
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-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.pasa.build_pasa">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.pasa.</span></span><span class="sig-name descname"><span class="pre">build_pasa</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/pasa.html#build_pasa"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.pasa.build_pasa" title="Permalink to this definition">#</a></dt>
-<dd><p>Build pasa CNN.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>)</p></li>
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-<span id="ptbench-models-signs-to-tb"></span><h1>ptbench.models.signs_to_tb<a class="headerlink" href="#module-ptbench.models.signs_to_tb" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.signs_to_tb.build_signs_to_tb" title="ptbench.models.signs_to_tb.build_signs_to_tb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_signs_to_tb</span></code></a>(input_size, hidden_size)</p></td>
-<td><p>Build SignsToTB shallow model.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.models.signs_to_tb.SignsToTB" title="ptbench.models.signs_to_tb.SignsToTB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SignsToTB</span></code></a>(input_size, hidden_size)</p></td>
-<td><p>Radiological signs to Tuberculosis module.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.models.signs_to_tb.SignsToTB">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.models.signs_to_tb.</span></span><span class="sig-name descname"><span class="pre">SignsToTB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/signs_to_tb.html#SignsToTB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.signs_to_tb.SignsToTB" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
-<p>Radiological signs to Tuberculosis module.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.models.signs_to_tb.SignsToTB.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/signs_to_tb.html#SignsToTB.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.signs_to_tb.SignsToTB.forward" title="Permalink to this definition">#</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – list of tensors.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>)</p></li>
-</ul>
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-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.models.signs_to_tb.build_signs_to_tb">
-<span class="sig-prename descclassname"><span class="pre">ptbench.models.signs_to_tb.</span></span><span class="sig-name descname"><span class="pre">build_signs_to_tb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/models/signs_to_tb.html#build_signs_to_tb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.models.signs_to_tb.build_signs_to_tb" title="Permalink to this definition">#</a></dt>
-<dd><p>Build SignsToTB shallow model.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>)</p></li>
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- <section id="module-ptbench.utils.checkpointer">
-<span id="ptbench-utils-checkpointer"></span><h1>ptbench.utils.checkpointer<a class="headerlink" href="#module-ptbench.utils.checkpointer" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.checkpointer.Checkpointer" title="ptbench.utils.checkpointer.Checkpointer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Checkpointer</span></code></a>(model[, optimizer, scheduler, path])</p></td>
-<td><p>A simple pytorch checkpointer.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.checkpointer.Checkpointer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.checkpointer.</span></span><span class="sig-name descname"><span class="pre">Checkpointer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scheduler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'.'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/checkpointer.html#Checkpointer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.checkpointer.Checkpointer" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>A simple pytorch checkpointer.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><em>torch.nn.Module</em></a>) – Network model, eventually loaded from a checkpointed file</p></li>
-<li><p><strong>optimizer</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>, Optional) – Optimizer</p></li>
-<li><p><strong>scheduler</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/optim.html#module-torch.optim" title="(in PyTorch v1.13)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.optim</span></code></a>, Optional) – Learning rate scheduler</p></li>
-<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, Optional) – Directory where to save checkpoints.</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.checkpointer.Checkpointer.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/checkpointer.html#Checkpointer.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.checkpointer.Checkpointer.save" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.checkpointer.Checkpointer.load">
-<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/checkpointer.html#Checkpointer.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.checkpointer.Checkpointer.load" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads model, optimizer and scheduler from file.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>f</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, Optional) – Name of a file (absolute or relative to <code class="docutils literal notranslate"><span class="pre">self.path</span></code>), that
-contains the checkpoint data to load into the model, and optionally
-into the optimizer and the scheduler. If not specified, loads data
-from current path.</p></li>
-<li><p><strong>partial</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, Optional) – If True, loading is not strict and only the model is loaded</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.checkpointer.Checkpointer.has_checkpoint">
-<span class="sig-name descname"><span class="pre">has_checkpoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/checkpointer.html#Checkpointer.has_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.checkpointer.Checkpointer.has_checkpoint" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.checkpointer.Checkpointer.last_checkpoint">
-<span class="sig-name descname"><span class="pre">last_checkpoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/checkpointer.html#Checkpointer.last_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.checkpointer.Checkpointer.last_checkpoint" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
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-<span id="ptbench-utils-download"></span><h1>ptbench.utils.download<a class="headerlink" href="#module-ptbench.utils.download" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.download.download_to_tempfile" title="ptbench.utils.download.download_to_tempfile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download_to_tempfile</span></code></a>(url[, progress])</p></td>
-<td><p>Downloads a file to a temporary named file and returns it.</p></td>
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-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.download.</span></span><span class="sig-name descname"><span class="pre">download_to_tempfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/download.html#download_to_tempfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.download.download_to_tempfile" title="Permalink to this definition">#</a></dt>
-<dd><p>Downloads a file to a temporary named file and returns it.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>url</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><em>str</em></a>) – The URL pointing to the file to download</p></li>
-<li><p><strong>progress</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, Optional) – If a progress bar should be displayed for downloading the URL.</p></li>
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-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>f</strong> (<a class="reference external" href="https://docs.python.org/3/library/tempfile.html#tempfile.NamedTemporaryFile" title="(in Python v3.11)"><code class="xref py py-func docutils literal notranslate"><span class="pre">tempfile.NamedTemporaryFile()</span></code></a>) – A named temporary file that contains the downloaded URL</p></li>
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- <section id="module-ptbench.utils.grad_cams">
-<span id="ptbench-utils-grad-cams"></span><h1>ptbench.utils.grad_cams<a class="headerlink" href="#module-ptbench.utils.grad_cams" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.grad_cams.BaseWrapper" title="ptbench.utils.grad_cams.BaseWrapper"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseWrapper</span></code></a>(model)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.grad_cams.GradCAM" title="ptbench.utils.grad_cams.GradCAM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GradCAM</span></code></a>(model[, candidate_layers])</p></td>
-<td><p>"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization" <a class="reference external" href="https://arxiv.org/pdf/1610.02391.pdf">https://arxiv.org/pdf/1610.02391.pdf</a> Look at Figure 2 on page 4</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.BaseWrapper">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.grad_cams.</span></span><span class="sig-name descname"><span class="pre">BaseWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#BaseWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.BaseWrapper" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.BaseWrapper.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#BaseWrapper.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.BaseWrapper.forward" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.BaseWrapper.backward">
-<span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#BaseWrapper.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.BaseWrapper.backward" title="Permalink to this definition">#</a></dt>
-<dd><p>Class-specific backpropagation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.BaseWrapper.generate">
-<span class="sig-name descname"><span class="pre">generate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#BaseWrapper.generate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.BaseWrapper.generate" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.BaseWrapper.remove_hook">
-<span class="sig-name descname"><span class="pre">remove_hook</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#BaseWrapper.remove_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.BaseWrapper.remove_hook" title="Permalink to this definition">#</a></dt>
-<dd><p>Remove all the forward/backward hook functions.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.GradCAM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.grad_cams.</span></span><span class="sig-name descname"><span class="pre">GradCAM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">candidate_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#GradCAM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.GradCAM" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#ptbench.utils.grad_cams.BaseWrapper" title="ptbench.utils.grad_cams.BaseWrapper"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseWrapper</span></code></a></p>
-<p>“Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationâ€
-<a class="reference external" href="https://arxiv.org/pdf/1610.02391.pdf">https://arxiv.org/pdf/1610.02391.pdf</a>
-Look at Figure 2 on page 4</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.grad_cams.GradCAM.generate">
-<span class="sig-name descname"><span class="pre">generate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_layer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/grad_cams.html#GradCAM.generate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.grad_cams.GradCAM.generate" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
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- <section id="module-ptbench.utils.measure">
-<span id="ptbench-utils-measure"></span><h1>ptbench.utils.measure<a class="headerlink" href="#module-ptbench.utils.measure" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.measure.base_measures" title="ptbench.utils.measure.base_measures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base_measures</span></code></a>(tp, fp, tn, fn)</p></td>
-<td><p>Calculates measures from true/false positive and negative counts.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.measure.bayesian_measures" title="ptbench.utils.measure.bayesian_measures"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesian_measures</span></code></a>(tp, fp, tn, fn, lambda_, ...)</p></td>
-<td><p>Calculates mean and mode from true/false positive and negative counts with credible regions.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.measure.beta_credible_region" title="ptbench.utils.measure.beta_credible_region"><code class="xref py py-obj docutils literal notranslate"><span class="pre">beta_credible_region</span></code></a>(successes, failures, ...)</p></td>
-<td><p>Returns the mode, upper and lower bounds of the equal-tailed credible region of a probability estimate following Bernoulli trials.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.measure.get_centered_maxf1" title="ptbench.utils.measure.get_centered_maxf1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_centered_maxf1</span></code></a>(f1_scores, thresholds)</p></td>
-<td><p>Return the centered max F1 score threshold when multiple threshold give the same max F1 score.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.measure.tricky_division" title="ptbench.utils.measure.tricky_division"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tricky_division</span></code></a>(n, d)</p></td>
-<td><p>Divides n by d.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.measure.SmoothedValue" title="ptbench.utils.measure.SmoothedValue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SmoothedValue</span></code></a>([window_size])</p></td>
-<td><p>Track a series of values and provide access to smoothed values over a window or the global series average.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.measure.SmoothedValue">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">SmoothedValue</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">window_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#SmoothedValue"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.SmoothedValue" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Track a series of values and provide access to smoothed values over a
-window or the global series average.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.measure.SmoothedValue.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#SmoothedValue.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.SmoothedValue.update" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="ptbench.utils.measure.SmoothedValue.median">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">median</span></span><a class="headerlink" href="#ptbench.utils.measure.SmoothedValue.median" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="ptbench.utils.measure.SmoothedValue.avg">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">avg</span></span><a class="headerlink" href="#ptbench.utils.measure.SmoothedValue.avg" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.measure.tricky_division">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">tricky_division</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#tricky_division"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.tricky_division" title="Permalink to this definition">#</a></dt>
-<dd><p>Divides n by d.</p>
-<p>Returns 0.0 in case of a division by zero</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.measure.base_measures">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">base_measures</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#base_measures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.base_measures" title="Permalink to this definition">#</a></dt>
-<dd><p>Calculates measures from true/false positive and negative counts.</p>
-<p>This function can return standard machine learning measures from true and
-false positive counts of positives and negatives. For a thorough look into
-these and alternate names for the returned values, please check Wikipedia’s
-entry on <a class="reference external" href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and Recall</a>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>tp</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – True positive count, AKA “hitâ€</p></li>
-<li><p><strong>fp</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – False positive count, AKA, “correct rejectionâ€</p></li>
-<li><p><strong>tn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – True negative count, AKA “false alarmâ€, or “Type I errorâ€</p></li>
-<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – False Negative count, AKA “missâ€, or “Type II errorâ€</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>precision</strong> (<em>float</em>) – P, AKA positive predictive value (PPV). It corresponds arithmetically
-to <code class="docutils literal notranslate"><span class="pre">tp/(tp+fp)</span></code>. In the case <code class="docutils literal notranslate"><span class="pre">tp+fp</span> <span class="pre">==</span> <span class="pre">0</span></code>, this function returns
-zero for precision.</p></li>
-<li><p><strong>recall</strong> (<em>float</em>) – R, AKA sensitivity, hit rate, or true positive rate (TPR). It
-corresponds arithmetically to <code class="docutils literal notranslate"><span class="pre">tp/(tp+fn)</span></code>. In the special case
-where <code class="docutils literal notranslate"><span class="pre">tp+fn</span> <span class="pre">==</span> <span class="pre">0</span></code>, this function returns zero for recall.</p></li>
-<li><p><strong>specificity</strong> (<em>float</em>) – S, AKA selectivity or true negative rate (TNR). It
-corresponds arithmetically to <code class="docutils literal notranslate"><span class="pre">tn/(tn+fp)</span></code>. In the special case
-where <code class="docutils literal notranslate"><span class="pre">tn+fp</span> <span class="pre">==</span> <span class="pre">0</span></code>, this function returns zero for specificity.</p></li>
-<li><p><strong>accuracy</strong> (<em>float</em>) – A, see <a class="reference external" href="https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers">Accuracy</a>. is
-the proportion of correct predictions (both true positives and true
-negatives) among the total number of pixels examined. It corresponds
-arithmetically to <code class="docutils literal notranslate"><span class="pre">(tp+tn)/(tp+tn+fp+fn)</span></code>. This measure includes
-both true-negatives and positives in the numerator, what makes it
-sensitive to data or regions without annotations.</p></li>
-<li><p><strong>jaccard</strong> (<em>float</em>) – J, see <a class="reference external" href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard Index or Similarity</a>. It corresponds
-arithmetically to <code class="docutils literal notranslate"><span class="pre">tp/(tp+fp+fn)</span></code>. In the special case where
-<code class="docutils literal notranslate"><span class="pre">tn+fp+fn</span> <span class="pre">==</span> <span class="pre">0</span></code>, this function returns zero for the Jaccard index.
-The Jaccard index depends on a TP-only numerator, similarly to the F1
-score. For regions where there are no annotations, the Jaccard index
-will always be zero, irrespective of the model output. Accuracy may be
-a better proxy if one needs to consider the true abscence of
-annotations in a region as part of the measure.</p></li>
-<li><p><strong>f1_score</strong> (<em>float</em>) – F1, see <a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F1-score</a>. It
-corresponds arithmetically to <code class="docutils literal notranslate"><span class="pre">2*P*R/(P+R)</span></code> or <code class="docutils literal notranslate"><span class="pre">2*tp/(2*tp+fp+fn)</span></code>.
-In the special case where <code class="docutils literal notranslate"><span class="pre">P+R</span> <span class="pre">==</span> <span class="pre">(2*tp+fp+fn)</span> <span class="pre">==</span> <span class="pre">0</span></code>, this function
-returns zero for the Jaccard index. The F1 or Dice score depends on a
-TP-only numerator, similarly to the Jaccard index. For regions where
-there are no annotations, the F1-score will always be zero,
-irrespective of the model output. Accuracy may be a better proxy if
-one needs to consider the true abscence of annotations in a region as
-part of the measure.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.measure.beta_credible_region">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">beta_credible_region</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">successes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coverage</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#beta_credible_region"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.beta_credible_region" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns the mode, upper and lower bounds of the equal-tailed credible
-region of a probability estimate following Bernoulli trials.</p>
-<p>This implemetnation is based on <a class="reference internal" href="../../references.html#goutte-2005" id="id1"><span>[GOUTTE-2005]</span></a>. It assumes <span class="math notranslate nohighlight">\(k\)</span>
-successes and <span class="math notranslate nohighlight">\(l\)</span> failures (<span class="math notranslate nohighlight">\(n = k+l\)</span> total trials) are issued
-from a series of Bernoulli trials (likelihood is binomial). The posterior
-is derivated using the Bayes Theorem with a beta prior. As there is no
-reason to favour high vs. low precision, we use a symmetric Beta prior
-(<span class="math notranslate nohighlight">\(\alpha=\beta\)</span>):</p>
-<div class="math-wrapper docutils container">
-<div class="math notranslate nohighlight">
-\[\begin{split}P(p|k,n) &= \frac{P(k,n|p)P(p)}{P(k,n)} \\
-P(p|k,n) &= \frac{\frac{n!}{k!(n-k)!}p^{k}(1-p)^{n-k}P(p)}{P(k)} \\
-P(p|k,n) &= \frac{1}{B(k+\alpha, n-k+eta)}p^{k+\alpha-1}(1-p)^{n-k+\beta-1} \\
-P(p|k,n) &= \frac{1}{B(k+\alpha, n-k+\alpha)}p^{k+\alpha-1}(1-p)^{n-k+\alpha-1}\end{split}\]</div>
-</div>
-<p>The mode for this posterior (also the maximum a posteriori) is:</p>
-<div class="math-wrapper docutils container">
-<div class="math notranslate nohighlight">
-\[\text{mode}(p) = \frac{k+\lambda-1}{n+2\lambda-2}\]</div>
-</div>
-<p>Concretely, the prior may be flat (all rates are equally likely,
-<span class="math notranslate nohighlight">\(\lambda=1\)</span>) or we may use Jeoffrey’s prior
-(<span class="math notranslate nohighlight">\(\lambda=0.5\)</span>), that is invariant through re-parameterisation.
-Jeffrey’s prior indicate that rates close to zero or one are more likely.</p>
-<p>The mode above works if <span class="math notranslate nohighlight">\(k+{\alpha},n-k+{\alpha} > 1\)</span>, which is
-usually the case for a resonably well tunned system, with more than a few
-samples for analysis. In the limit of the system performance, <span class="math notranslate nohighlight">\(k\)</span>
-may be 0, which will make the mode become zero.</p>
-<p>For our purposes, it may be more suitable to represent <span class="math notranslate nohighlight">\(n = k + l\)</span>,
-with <span class="math notranslate nohighlight">\(k\)</span>, the number of successes and <span class="math notranslate nohighlight">\(l\)</span>, the number of
-failures in the binomial experiment, and find this more suitable
-representation:</p>
-<div class="math-wrapper docutils container">
-<div class="math notranslate nohighlight">
-\[\begin{split}P(p|k,l) &= \frac{1}{B(k+\alpha, l+\alpha)}p^{k+\alpha-1}(1-p)^{l+\alpha-1} \\
-\text{mode}(p) &= \frac{k+\lambda-1}{k+l+2\lambda-2}\end{split}\]</div>
-</div>
-<p>This can be mapped to most rates calculated in the context of binary
-classification this way:</p>
-<ul class="simple">
-<li><p>Precision or Positive-Predictive Value (PPV): p = TP/(TP+FP), so k=TP, l=FP</p></li>
-<li><p>Recall, Sensitivity, or True Positive Rate: r = TP/(TP+FN), so k=TP, l=FN</p></li>
-<li><p>Specificity or True Negative Rage: s = TN/(TN+FP), so k=TN, l=FP</p></li>
-<li><p>F1-score: f1 = 2TP/(2TP+FP+FN), so k=2TP, l=FP+FN</p></li>
-<li><p>Accuracy: acc = TP+TN/(TP+TN+FP+FN), so k=TP+TN, l=FP+FN</p></li>
-<li><p>Jaccard: j = TP/(TP+FP+FN), so k=TP, l=FP+FN</p></li>
-</ul>
-<p>Contrary to frequentist approaches, in which one can only
-say that if the test were repeated an infinite number of times,
-and one constructed a confidence interval each time, then X%
-of the confidence intervals would contain the true rate, here
-we can say that given our observed data, there is a X% probability
-that the true value of <span class="math notranslate nohighlight">\(k/n\)</span> falls within the provided
-interval.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>For a disambiguation with Confidence Interval, read
-<a class="reference external" href="https://en.wikipedia.org/wiki/Credible_interval">https://en.wikipedia.org/wiki/Credible_interval</a>.</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>successes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – Number of successes observed on the experiment</p></li>
-<li><p><strong>failures</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – Number of failures observed on the experiment</p></li>
-<li><p><strong>lambda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, Optional) – The parameterisation of the Beta prior to consider. Use
-<span class="math notranslate nohighlight">\(\lambda=1\)</span> for a flat prior. Use <span class="math notranslate nohighlight">\(\lambda=0.5\)</span> for
-Jeffrey’s prior (the default).</p></li>
-<li><p><strong>coverage</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, Optional) – A floating-point number between 0 and 1.0 indicating the
-coverage you’re expecting. A value of 0.95 will ensure 95%
-of the area under the probability density of the posterior
-is covered by the returned equal-tailed interval.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>mean</strong> (<em>float</em>) – The mean of the posterior distribution</p></li>
-<li><p><strong>mode</strong> (<em>float</em>) – The mode of the posterior distribution</p></li>
-<li><p><strong>lower, upper</strong> (<em>float</em>) – The lower and upper bounds of the credible region</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.measure.bayesian_measures">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">bayesian_measures</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coverage</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#bayesian_measures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.bayesian_measures" title="Permalink to this definition">#</a></dt>
-<dd><p>Calculates mean and mode from true/false positive and negative counts
-with credible regions.</p>
-<p>This function can return bayesian estimates of standard machine learning
-measures from true and false positive counts of positives and negatives.
-For a thorough look into these and alternate names for the returned values,
-please check Wikipedia’s entry on <a class="reference external" href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and Recall</a>. See
-<a class="reference internal" href="#ptbench.utils.measure.beta_credible_region" title="ptbench.utils.measure.beta_credible_region"><code class="xref py py-func docutils literal notranslate"><span class="pre">beta_credible_region()</span></code></a> for details on the calculation of returned
-values.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>tp</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – True positive count, AKA “hitâ€</p></li>
-<li><p><strong>fp</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – False positive count, AKA “false alarmâ€, or “Type I errorâ€</p></li>
-<li><p><strong>tn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – True negative count, AKA “correct rejectionâ€</p></li>
-<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – False Negative count, AKA “missâ€, or “Type II errorâ€</p></li>
-<li><p><strong>lambda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – The parameterisation of the Beta prior to consider. Use
-<span class="math notranslate nohighlight">\(\lambda=1\)</span> for a flat prior. Use <span class="math notranslate nohighlight">\(\lambda=0.5\)</span> for
-Jeffrey’s prior.</p></li>
-<li><p><strong>coverage</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – A floating-point number between 0 and 1.0 indicating the
-coverage you’re expecting. A value of 0.95 will ensure 95%
-of the area under the probability density of the posterior
-is covered by the returned equal-tailed interval.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>precision</strong> (<em>(float, float, float, float)</em>) – P, AKA positive predictive value (PPV), mean, mode and credible
-intervals (95% CI). It corresponds arithmetically
-to <code class="docutils literal notranslate"><span class="pre">tp/(tp+fp)</span></code>.</p></li>
-<li><p><strong>recall</strong> (<em>(float, float, float, float)</em>) – R, AKA sensitivity, hit rate, or true positive rate (TPR), mean, mode
-and credible intervals (95% CI). It corresponds arithmetically to
-<code class="docutils literal notranslate"><span class="pre">tp/(tp+fn)</span></code>.</p></li>
-<li><p><strong>specificity</strong> (<em>(float, float, float, float)</em>) – S, AKA selectivity or true negative rate (TNR), mean, mode and credible
-intervals (95% CI). It corresponds arithmetically to <code class="docutils literal notranslate"><span class="pre">tn/(tn+fp)</span></code>.</p></li>
-<li><p><strong>accuracy</strong> (<em>(float, float, float, float)</em>) – A, mean, mode and credible intervals (95% CI). See <a class="reference external" href="https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers">Accuracy</a>. is
-the proportion of correct predictions (both true positives and true
-negatives) among the total number of pixels examined. It corresponds
-arithmetically to <code class="docutils literal notranslate"><span class="pre">(tp+tn)/(tp+tn+fp+fn)</span></code>. This measure includes
-both true-negatives and positives in the numerator, what makes it
-sensitive to data or regions without annotations.</p></li>
-<li><p><strong>jaccard</strong> (<em>(float, float, float, float)</em>) – J, mean, mode and credible intervals (95% CI). See <a class="reference external" href="https://en.wikipedia.org/wiki/Jaccard_index">Jaccard Index or
-Similarity</a>. It
-corresponds arithmetically to <code class="docutils literal notranslate"><span class="pre">tp/(tp+fp+fn)</span></code>. The Jaccard index
-depends on a TP-only numerator, similarly to the F1 score. For regions
-where there are no annotations, the Jaccard index will always be zero,
-irrespective of the model output. Accuracy may be a better proxy if
-one needs to consider the true abscence of annotations in a region as
-part of the measure.</p></li>
-<li><p><strong>f1_score</strong> (<em>(float, float, float, float)</em>) – F1, mean, mode and credible intervals (95% CI). See <a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F1-score</a>. It corresponds
-arithmetically to <code class="docutils literal notranslate"><span class="pre">2*P*R/(P+R)</span></code> or <code class="docutils literal notranslate"><span class="pre">2*tp/(2*tp+fp+fn)</span></code>. The F1 or
-Dice score depends on a TP-only numerator, similarly to the Jaccard
-index. For regions where there are no annotations, the F1-score will
-always be zero, irrespective of the model output. Accuracy may be a
-better proxy if one needs to consider the true abscence of annotations
-in a region as part of the measure.</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.measure.get_centered_maxf1">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.measure.</span></span><span class="sig-name descname"><span class="pre">get_centered_maxf1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f1_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/measure.html#get_centered_maxf1"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.measure.get_centered_maxf1" title="Permalink to this definition">#</a></dt>
-<dd><p>Return the centered max F1 score threshold when multiple threshold give
-the same max F1 score.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>f1_scores</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><em>numpy.ndarray</em></a>) – 1D array of f1 scores</p></li>
-<li><p><strong>thresholds</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><em>numpy.ndarray</em></a>) – 1D array of thresholds</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>max F1 score</strong> (<em>float</em>)</p></li>
-<li><p><strong>threshold</strong> (<em>float</em>)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
-
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-<li><a class="reference internal" href="#">ptbench.utils.measure</a><ul>
-<li><a class="reference internal" href="#ptbench.utils.measure.SmoothedValue"><code class="docutils literal notranslate"><span class="pre">SmoothedValue</span></code></a><ul>
-<li><a class="reference internal" href="#ptbench.utils.measure.SmoothedValue.update"><code class="docutils literal notranslate"><span class="pre">SmoothedValue.update()</span></code></a></li>
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- <section id="module-ptbench.utils.model_serialization">
-<span id="ptbench-utils-model-serialization"></span><h1>ptbench.utils.model_serialization<a class="headerlink" href="#module-ptbench.utils.model_serialization" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.model_serialization.align_and_update_state_dicts" title="ptbench.utils.model_serialization.align_and_update_state_dicts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">align_and_update_state_dicts</span></code></a>(...)</p></td>
-<td><p>Strategy: suppose that the models that we will create will have prefixes appended to each of its keys, for example due to an extra level of nesting that the original pre-trained weights from ImageNet won't contain.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.model_serialization.load_state_dict" title="ptbench.utils.model_serialization.load_state_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_state_dict</span></code></a>(model, loaded_state_dict)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.model_serialization.strip_prefix_if_present" title="ptbench.utils.model_serialization.strip_prefix_if_present"><code class="xref py py-obj docutils literal notranslate"><span class="pre">strip_prefix_if_present</span></code></a>(state_dict, prefix)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.model_serialization.align_and_update_state_dicts">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_serialization.</span></span><span class="sig-name descname"><span class="pre">align_and_update_state_dicts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loaded_state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/model_serialization.html#align_and_update_state_dicts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.model_serialization.align_and_update_state_dicts" title="Permalink to this definition">#</a></dt>
-<dd><p>Strategy: suppose that the models that we will create will have prefixes appended
-to each of its keys, for example due to an extra level of nesting that the original
-pre-trained weights from ImageNet won’t contain. For example, model.state_dict()
-might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
-res2.conv1.weight. We thus want to match both parameters together.
-For that, we look for each model weight, look among all loaded keys if there is one
-that is a suffix of the current weight name, and use it if that’s the case.
-If multiple matches exist, take the one with longest size
-of the corresponding name. For example, for the same model as before, the pretrained
-weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
-we want to match backbone[0].body.conv1.weight to conv1.weight, and
-backbone[0].body.res2.conv1.weight to res2.conv1.weight.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.model_serialization.strip_prefix_if_present">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_serialization.</span></span><span class="sig-name descname"><span class="pre">strip_prefix_if_present</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/model_serialization.html#strip_prefix_if_present"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.model_serialization.strip_prefix_if_present" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.model_serialization.load_state_dict">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_serialization.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loaded_state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/model_serialization.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.model_serialization.load_state_dict" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-</section>
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-<li><a class="reference internal" href="#">ptbench.utils.model_serialization</a><ul>
-<li><a class="reference internal" href="#ptbench.utils.model_serialization.align_and_update_state_dicts"><code class="docutils literal notranslate"><span class="pre">align_and_update_state_dicts()</span></code></a></li>
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- <section id="module-ptbench.utils.model_zoo">
-<span id="ptbench-utils-model-zoo"></span><h1>ptbench.utils.model_zoo<a class="headerlink" href="#module-ptbench.utils.model_zoo" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.model_zoo.modelurls" title="ptbench.utils.model_zoo.modelurls"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modelurls</span></code></a></p></td>
-<td><p>URLs of pre-trained models (backbones)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.model_zoo.cache_url" title="ptbench.utils.model_zoo.cache_url"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cache_url</span></code></a>(url[, model_dir, progress])</p></td>
-<td><p>Loads the Torch serialized object at the given URL.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.model_zoo.download_url_to_file" title="ptbench.utils.model_zoo.download_url_to_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">download_url_to_file</span></code></a>(url, dst, hash_prefix, ...)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.utils.model_zoo.modelurls">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">modelurls</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'resnet101':</span> <span class="pre">'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',</span> <span class="pre">'resnet152':</span> <span class="pre">'https://download.pytorch.org/models/resnet152-b121ed2d.pth',</span> <span class="pre">'resnet18':</span> <span class="pre">'https://download.pytorch.org/models/resnet18-5c106cde.pth',</span> <span class="pre">'resnet34':</span> <span class="pre">'https://download.pytorch.org/models/resnet34-333f7ec4.pth',</span> <span class="pre">'resnet50':</span> <span class="pre">'https://download.pytorch.org/models/resnet50-19c8e357.pth',</span> <span class="pre">'vgg11':</span> <span class="pre">'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',</span> <span class="pre">'vgg11_bn':</span> <span class="pre">'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',</span> <span class="pre">'vgg13':</span> <span class="pre">'https://download.pytorch.org/models/vgg13-c768596a.pth',</span> <span class="pre">'vgg13_bn':</span> <span class="pre">'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',</span> <span class="pre">'vgg16':</span> <span class="pre">'https://download.pytorch.org/models/vgg16-397923af.pth',</span> <span class="pre">'vgg16_bn':</span> <span class="pre">'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',</span> <span class="pre">'vgg19':</span> <span class="pre">'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',</span> <span class="pre">'vgg19_bn':</span> <span class="pre">'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth'}</span></em><a class="headerlink" href="#ptbench.utils.model_zoo.modelurls" title="Permalink to this definition">#</a></dt>
-<dd><p>URLs of pre-trained models (backbones)</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.model_zoo.download_url_to_file">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">download_url_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hash_prefix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/model_zoo.html#download_url_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.model_zoo.download_url_to_file" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.model_zoo.cache_url">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">cache_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/model_zoo.html#cache_url"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.model_zoo.cache_url" title="Permalink to this definition">#</a></dt>
-<dd><p>Loads the Torch serialized object at the given URL.</p>
-<p>If the object is already present in <cite>model_dir</cite>, it’s deserialized and
-returned. The filename part of the URL should follow the naming convention
-<code class="docutils literal notranslate"><span class="pre">filename-<sha256>.ext</span></code> where <code class="docutils literal notranslate"><span class="pre"><sha256></span></code> is the first eight or more
-digits of the SHA256 hash of the contents of the file. The hash is used to
-ensure unique names and to verify the contents of the file.
-The default value of <cite>model_dir</cite> is <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME/models</span></code> where
-<code class="docutils literal notranslate"><span class="pre">$TORCH_HOME</span></code> defaults to <code class="docutils literal notranslate"><span class="pre">~/.torch</span></code>. The default directory can be
-overridden with the <code class="docutils literal notranslate"><span class="pre">$TORCH_MODEL_ZOO</span></code> environment variable.
-:type url:
-:param url: URL of the object to download
-:type url: string
-:type model_dir:
-:param model_dir: directory in which to save the object
-:type model_dir: string, optional
-:type progress:
-:param progress: whether or not to display a progress bar to stderr
-:type progress: bool, optional</p>
-</dd></dl>
-
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- <section id="module-ptbench.utils.plot">
-<span id="ptbench-utils-plot"></span><h1>ptbench.utils.plot<a class="headerlink" href="#module-ptbench.utils.plot" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.plot.precision_recall_f1iso" title="ptbench.utils.plot.precision_recall_f1iso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precision_recall_f1iso</span></code></a>(data)</p></td>
-<td><p>Creates a precision-recall plot.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.plot.relevance_analysis_plot" title="ptbench.utils.plot.relevance_analysis_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">relevance_analysis_plot</span></code></a>(data[, title])</p></td>
-<td><p>Create an histogram plot to show the relative importance of features.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.plot.roc_curve" title="ptbench.utils.plot.roc_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">roc_curve</span></code></a>(data[, title])</p></td>
-<td><p>Creates a ROC plot.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.plot.precision_recall_f1iso">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.plot.</span></span><span class="sig-name descname"><span class="pre">precision_recall_f1iso</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/plot.html#precision_recall_f1iso"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.plot.precision_recall_f1iso" title="Permalink to this definition">#</a></dt>
-<dd><p>Creates a precision-recall plot.</p>
-<p>This function creates and returns a Matplotlib figure with a
-precision-recall plot. The plot will be annotated with F1-score
-iso-lines (in which the F1-score maintains the same value).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – <p>A dictionary in which keys are strings defining plot labels and values
-are dictionaries with two entries:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">df</span></code>: <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.5.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
-<p>A dataframe that is produced by our predictor engine containing
-the following columns: <code class="docutils literal notranslate"><span class="pre">filename</span></code>, <code class="docutils literal notranslate"><span class="pre">likelihood</span></code>,
-<code class="docutils literal notranslate"><span class="pre">ground_truth</span></code>.</p>
-</li>
-<li><p><code class="docutils literal notranslate"><span class="pre">threshold</span></code>: <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
-<p>A threshold for each set. Not used here.</p>
-</li>
-</ul>
-</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>figure</strong> (<em>matplotlib.figure.Figure</em>) – A matplotlib figure you can save or display (uses an <code class="docutils literal notranslate"><span class="pre">agg</span></code> backend)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.plot.roc_curve">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.plot.</span></span><span class="sig-name descname"><span class="pre">roc_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/plot.html#roc_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.plot.roc_curve" title="Permalink to this definition">#</a></dt>
-<dd><p>Creates a ROC plot.</p>
-<p>This function creates and returns a Matplotlib figure with a
-ROC plot.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – <p>A dictionary in which keys are strings defining plot labels and values
-are dictionaries with two entries:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">df</span></code>: <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.5.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
-<p>A dataframe that is produced by our predictor engine containing
-the following columns: <code class="docutils literal notranslate"><span class="pre">filename</span></code>, <code class="docutils literal notranslate"><span class="pre">likelihood</span></code>,
-<code class="docutils literal notranslate"><span class="pre">ground_truth</span></code>.</p>
-</li>
-<li><p><code class="docutils literal notranslate"><span class="pre">threshold</span></code>: <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
-<p>A threshold for each set. Not used here.</p>
-</li>
-</ul>
-</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>figure</strong> (<em>matplotlib.figure.Figure</em>) – A matplotlib figure you can save or display (uses an <code class="docutils literal notranslate"><span class="pre">agg</span></code> backend)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.plot.relevance_analysis_plot">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.plot.</span></span><span class="sig-name descname"><span class="pre">relevance_analysis_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/plot.html#relevance_analysis_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.plot.relevance_analysis_plot" title="Permalink to this definition">#</a></dt>
-<dd><p>Create an histogram plot to show the relative importance of features.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – The list of values (one for each feature)</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>figure</strong> (<em>matplotlib.figure.Figure</em>) – A matplotlib figure you can save or display (uses an <code class="docutils literal notranslate"><span class="pre">agg</span></code> backend)</p></li>
-</ul>
-</p>
-</dd>
-</dl>
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-<span id="ptbench-utils-rc"></span><h1>ptbench.utils.rc<a class="headerlink" href="#module-ptbench.utils.rc" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.rc.load_rc" title="ptbench.utils.rc.load_rc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_rc</span></code></a>()</p></td>
-<td><p>Returns global configuration variables.</p></td>
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- <section id="module-ptbench.utils.resources">
-<span id="ptbench-utils-resources"></span><h1>ptbench.utils.resources<a class="headerlink" href="#module-ptbench.utils.resources" title="Permalink to this heading">#</a></h1>
-<p>Tools for interacting with the running computer or GPU.</p>
-<p class="rubric">Module Attributes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.resources.GB" title="ptbench.utils.resources.GB"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GB</span></code></a></p></td>
-<td><p>The number of bytes in a gigabyte.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.resources.cpu_constants" title="ptbench.utils.resources.cpu_constants"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cpu_constants</span></code></a>()</p></td>
-<td><p>Returns static CPU information about the current system.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.resources.gpu_constants" title="ptbench.utils.resources.gpu_constants"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpu_constants</span></code></a>()</p></td>
-<td><p>Returns GPU (static) information using nvidia-smi.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.resources.gpu_log" title="ptbench.utils.resources.gpu_log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gpu_log</span></code></a>()</p></td>
-<td><p>Returns GPU information about current non-static status using nvidia- smi.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.resources.run_nvidia_smi" title="ptbench.utils.resources.run_nvidia_smi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_nvidia_smi</span></code></a>(query[, rename])</p></td>
-<td><p>Returns GPU information from query.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.resources.CPULogger" title="ptbench.utils.resources.CPULogger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CPULogger</span></code></a>([pid])</p></td>
-<td><p>Logs CPU information using <a class="reference external" href="https://psutil.readthedocs.io/en/stable/index.html#module-psutil" title="(in psutil v5.7.3)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">psutil</span></code></a></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#ptbench.utils.resources.ResourceMonitor" title="ptbench.utils.resources.ResourceMonitor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResourceMonitor</span></code></a>(interval, has_gpu, main_pid, ...)</p></td>
-<td><p>An external, non-blocking CPU/GPU resource monitor.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<dl class="py data">
-<dt class="sig sig-object py" id="ptbench.utils.resources.GB">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">GB</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">1073741824.0</span></em><a class="headerlink" href="#ptbench.utils.resources.GB" title="Permalink to this definition">#</a></dt>
-<dd><p>The number of bytes in a gigabyte.</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.resources.run_nvidia_smi">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">run_nvidia_smi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#run_nvidia_smi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.run_nvidia_smi" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns GPU information from query.</p>
-<p>For a comprehensive list of options and help, execute <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span>
-<span class="pre">--help-query-gpu</span></code> on a host with a GPU</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>query</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><em>list</em></a>) – A list of query strings as defined by <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span> <span class="pre">--help-query-gpu</span></code></p></li>
-<li><p><strong>rename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>, Optional) – A list of keys to yield in the return value for each entry above. It
-gives you the opportunity to rewrite some key names for convenience.
-This list, if provided, must be of the same length as <code class="docutils literal notranslate"><span class="pre">query</span></code>.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><ul class="simple">
-<li></li>
-<li><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>, None) – An ordered dictionary (organized as 2-tuples) containing the queried
-parameters (<code class="docutils literal notranslate"><span class="pre">rename</span></code> versions). If <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> is not available,
-returns <code class="docutils literal notranslate"><span class="pre">None</span></code>. Percentage information is left alone,
-memory information is transformed to gigabytes (floating-point).</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.resources.gpu_constants">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">gpu_constants</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#gpu_constants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.gpu_constants" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns GPU (static) information using nvidia-smi.</p>
-<p>See <a class="reference internal" href="#ptbench.utils.resources.run_nvidia_smi" title="ptbench.utils.resources.run_nvidia_smi"><code class="xref py py-func docutils literal notranslate"><span class="pre">run_nvidia_smi()</span></code></a> for operational details.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>, None) – If <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> is not available, returns <code class="docutils literal notranslate"><span class="pre">None</span></code>, otherwise, we
-return an ordered dictionary (organized as 2-tuples) containing the
-following <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> query information:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">gpu_name</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_name</span></code> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">driver_version</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_driver_version</span></code> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">memory.total</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_memory_total</span></code> (transformed to gigabytes,
-<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>)</p></li>
-</ul>
-</li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.resources.gpu_log">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">gpu_log</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#gpu_log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.gpu_log" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns GPU information about current non-static status using nvidia-
-smi.</p>
-<p>See <a class="reference internal" href="#ptbench.utils.resources.run_nvidia_smi" title="ptbench.utils.resources.run_nvidia_smi"><code class="xref py py-func docutils literal notranslate"><span class="pre">run_nvidia_smi()</span></code></a> for operational details.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>, None) – If <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> is not available, returns <code class="docutils literal notranslate"><span class="pre">None</span></code>, otherwise, we
-return an ordered dictionary (organized as 2-tuples) containing the
-following <code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code> query information:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">memory.used</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_memory_used</span></code> (transformed to gigabytes,
-<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">memory.free</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_memory_free</span></code> (transformed to gigabytes,
-<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">100*memory.used/memory.total</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_memory_percent</span></code>,
-(<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, in percent)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">utilization.gpu</span></code>, as <code class="docutils literal notranslate"><span class="pre">gpu_percent</span></code>,
-(<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, in percent)</p></li>
-</ul>
-</li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.resources.cpu_constants">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">cpu_constants</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#cpu_constants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.cpu_constants" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns static CPU information about the current system.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>data</strong> (<em>tuple</em>) – An ordered dictionary (organized as 2-tuples) containing these entries:</p>
-<ol class="arabic simple" start="0">
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_memory_total</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>): total memory available,
-in gigabytes</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_count</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>): number of logical CPUs available</p></li>
-</ol>
-</li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.resources.CPULogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">CPULogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#CPULogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.CPULogger" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>Logs CPU information using <a class="reference external" href="https://psutil.readthedocs.io/en/stable/index.html#module-psutil" title="(in psutil v5.7.3)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">psutil</span></code></a></p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>pid</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, Optional) – Process identifier of the main process (parent process) to observe</p>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.resources.CPULogger.log">
-<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#CPULogger.log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.CPULogger.log" title="Permalink to this definition">#</a></dt>
-<dd><p>Returns current process cluster information.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>data</strong> (<em>tuple</em>) – An ordered dictionary (organized as 2-tuples) containing these entries:</p>
-<ol class="arabic simple" start="0">
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_memory_used</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>): total memory used from
-the system, in gigabytes</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_rss</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>): RAM currently used by
-process and children, in gigabytes</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_vms</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>): total memory (RAM + swap) currently
-used by process and children, in gigabytes</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_percent</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>): percentage of the total CPU
-used by this process and children (recursively) since last call
-(first time called should be ignored). This number depends on the
-number of CPUs in the system and can be greater than 100%</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_processes</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>): total number of processes
-including self and children (recursively)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">cpu_open_files</span></code> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>): total number of open files by
-self and children</p></li>
-</ol>
-</li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ptbench.utils.resources.ResourceMonitor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ptbench.utils.resources.</span></span><span class="sig-name descname"><span class="pre">ResourceMonitor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_gpu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logging_level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#ResourceMonitor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.ResourceMonitor" title="Permalink to this definition">#</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<p>An external, non-blocking CPU/GPU resource monitor.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>interval</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><em>float</em></a>) – Number of seconds to wait between each measurement (maybe a floating
-point number as accepted by <a class="reference external" href="https://docs.python.org/3/library/time.html#time.sleep" title="(in Python v3.11)"><code class="xref py py-func docutils literal notranslate"><span class="pre">time.sleep()</span></code></a>)</p></li>
-<li><p><strong>has_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><em>bool</em></a>) – A flag indicating if we have a GPU installed on the platform or not</p></li>
-<li><p><strong>main_pid</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – The main process identifier to monitor</p></li>
-<li><p><strong>logging_level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><em>int</em></a>) – The logging level to use for logging from launched processes</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ptbench.utils.resources.ResourceMonitor.monitored_keys">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">monitored_keys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">has_gpu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/resources.html#ResourceMonitor.monitored_keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.resources.ResourceMonitor.monitored_keys" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
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-
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-<li><a class="reference internal" href="#">ptbench.utils.resources</a><ul>
-<li><a class="reference internal" href="#ptbench.utils.resources.GB"><code class="docutils literal notranslate"><span class="pre">GB</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.utils.resources.run_nvidia_smi"><code class="docutils literal notranslate"><span class="pre">run_nvidia_smi()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.utils.resources.gpu_constants"><code class="docutils literal notranslate"><span class="pre">gpu_constants()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.utils.resources.gpu_log"><code class="docutils literal notranslate"><span class="pre">gpu_log()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.utils.resources.cpu_constants"><code class="docutils literal notranslate"><span class="pre">cpu_constants()</span></code></a></li>
-<li><a class="reference internal" href="#ptbench.utils.resources.CPULogger"><code class="docutils literal notranslate"><span class="pre">CPULogger</span></code></a><ul>
-<li><a class="reference internal" href="#ptbench.utils.resources.CPULogger.log"><code class="docutils literal notranslate"><span class="pre">CPULogger.log()</span></code></a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#ptbench.utils.resources.ResourceMonitor"><code class="docutils literal notranslate"><span class="pre">ResourceMonitor</span></code></a><ul>
-<li><a class="reference internal" href="#ptbench.utils.resources.ResourceMonitor.monitored_keys"><code class="docutils literal notranslate"><span class="pre">ResourceMonitor.monitored_keys()</span></code></a></li>
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- <section id="module-ptbench.utils.summary">
-<span id="ptbench-utils-summary"></span><h1>ptbench.utils.summary<a class="headerlink" href="#module-ptbench.utils.summary" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.summary.summary" title="ptbench.utils.summary.summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">summary</span></code></a>(model)</p></td>
-<td><p>Counts the number of parameters in each model layer.</p></td>
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-<dl class="py function">
-<dt class="sig sig-object py" id="ptbench.utils.summary.summary">
-<span class="sig-prename descclassname"><span class="pre">ptbench.utils.summary.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/ptbench/utils/summary.html#summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ptbench.utils.summary.summary" title="Permalink to this definition">#</a></dt>
-<dd><p>Counts the number of parameters in each model layer.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>) – model to summarize</p>
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-<li></li>
-<li><p><strong>repr</strong> (<em>str</em>) – a multiline string representation of the network</p></li>
-<li><p><strong>nparam</strong> (<em>int</em>) – number of parameters</p></li>
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-<span id="ptbench-utils-table"></span><h1>ptbench.utils.table<a class="headerlink" href="#module-ptbench.utils.table" title="Permalink to this heading">#</a></h1>
-<p class="rubric">Functions</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#ptbench.utils.table.performance_table" title="ptbench.utils.table.performance_table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">performance_table</span></code></a>(data, fmt)</p></td>
-<td><p>Tables result comparison in a given format.</p></td>
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-<dd><p>Tables result comparison in a given format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>data</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><em>dict</em></a>) – <p>A dictionary in which keys are strings defining plot labels and values
-are dictionaries with two entries:</p>
-<ul>
-<li><p><code class="docutils literal notranslate"><span class="pre">df</span></code>: <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.5.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
-<p>A dataframe that is produced by our predictor engine containing
-the following columns: <code class="docutils literal notranslate"><span class="pre">filename</span></code>, <code class="docutils literal notranslate"><span class="pre">likelihood</span></code>,
-<code class="docutils literal notranslate"><span class="pre">ground_truth</span></code>.</p>
-</li>
-<li><p><code class="docutils literal notranslate"><span class="pre">threshold</span></code>: <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
-<p>A threshold to compute measures.</p>
-</li>
-</ul>
-</p>
-</dd>
-</dl>
-<dl class="simple">
-<dt>fmt<span class="classifier">str</span></dt><dd><p>One of the formats supported by tabulate.</p>
-</dd>
-</dl>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li></li>
-<li><p><strong>table</strong> (<em>str</em>) – A table in a specific format</p></li>
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- <article role="main">
- <section id="command-line-interface">
-<span id="ptbench-cli"></span><h1>Command-line Interface<a class="headerlink" href="#command-line-interface" title="Permalink to this heading">#</a></h1>
-<p>This section contains an overview of command-line applications shipped with
-this package.</p>
-<section id="ptbench">
-<h2>ptbench<a class="headerlink" href="#ptbench" title="Permalink to this heading">#</a></h2>
-<p>Active Tuberculosis Detection On Chest X-Ray Images.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>COMMAND<span class="w"> </span><span class="o">[</span>ARGS<span class="o">]</span>...
-</pre></div>
-</div>
-<section id="ptbench-aggregpred">
-<h3>aggregpred<a class="headerlink" href="#ptbench-aggregpred" title="Permalink to this heading">#</a></h3>
-<p>Aggregate multiple predictions csv files into one.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>aggregpred<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>LABEL_PATH<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-aggregpred-f">
-<span id="cmdoption-ptbench-aggregpred-output-folder"></span><span class="sig-name descname"><span class="pre">-f</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><output_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-aggregpred-f" title="Permalink to this definition">#</a></dt>
-<dd><p>Path where to store the aggregated csv file (created if necessary)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-aggregpred-v">
-<span id="cmdoption-ptbench-aggregpred-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-aggregpred-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-aggregpred-arg-LABEL_PATH">
-<span id="cmdoption-ptbench-aggregpred-arg-label-path"></span><span class="sig-name descname"><span class="pre">LABEL_PATH</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-aggregpred-arg-LABEL_PATH" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Aggregate multiple predictions csv files into one:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>aggregpred<span class="w"> </span>-vv<span class="w"> </span>path/to/train/predictions.csv<span class="w"> </span>path/to/test/predictions.csv
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-compare">
-<h3>compare<a class="headerlink" href="#ptbench-compare" title="Permalink to this heading">#</a></h3>
-<p>Compares multiple systems together.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>compare<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>LABEL_PATH<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-f">
-<span id="cmdoption-ptbench-compare-output-figure"></span><span class="sig-name descname"><span class="pre">-f</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-figure</span></span><span class="sig-prename descclassname"> <span class="pre"><output_figure></span></span><a class="headerlink" href="#cmdoption-ptbench-compare-f" title="Permalink to this definition">#</a></dt>
-<dd><p>Path where write the output figure (any extension supported by matplotlib is possible). If not provided, does not produce a figure.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-T">
-<span id="cmdoption-ptbench-compare-t"></span><span id="cmdoption-ptbench-compare-table-format"></span><span class="sig-name descname"><span class="pre">-T</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--table-format</span></span><span class="sig-prename descclassname"> <span class="pre"><table_format></span></span><a class="headerlink" href="#cmdoption-ptbench-compare-T" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> The format to use for the comparison table</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">rst</span></code></p>
-</dd>
-<dt class="field-even">Options<span class="colon">:</span></dt>
-<dd class="field-even"><p>asciidoc | double_grid | double_outline | fancy_grid | fancy_outline | github | grid | heavy_grid | heavy_outline | html | jira | latex | latex_booktabs | latex_longtable | latex_raw | mediawiki | mixed_grid | mixed_outline | moinmoin | orgtbl | outline | pipe | plain | presto | pretty | psql | rounded_grid | rounded_outline | rst | simple | simple_grid | simple_outline | textile | tsv | unsafehtml | youtrack</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-u">
-<span id="cmdoption-ptbench-compare-output-table"></span><span class="sig-name descname"><span class="pre">-u</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-table</span></span><span class="sig-prename descclassname"> <span class="pre"><output_table></span></span><a class="headerlink" href="#cmdoption-ptbench-compare-u" title="Permalink to this definition">#</a></dt>
-<dd><p>Path where write the output table. If not provided, does not write write a table to file, only to stdout.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-0">
-<span id="cmdoption-ptbench-compare-threshold"></span><span class="sig-name descname"><span class="pre">-t</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--threshold</span></span><span class="sig-prename descclassname"> <span class="pre"><threshold></span></span><a class="headerlink" href="#cmdoption-ptbench-compare-0" title="Permalink to this definition">#</a></dt>
-<dd><p>This number is used to separate positive and negative cases by thresholding their score.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-v">
-<span id="cmdoption-ptbench-compare-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-compare-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-compare-arg-LABEL_PATH">
-<span id="cmdoption-ptbench-compare-arg-label-path"></span><span class="sig-name descname"><span class="pre">LABEL_PATH</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-compare-arg-LABEL_PATH" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Compares system A and B, with their own predictions files:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>compare<span class="w"> </span>-vv<span class="w"> </span>A<span class="w"> </span>path/to/A/predictions.csv<span class="w"> </span>B<span class="w"> </span>path/to/B/predictions.csv
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-config">
-<h3>config<a class="headerlink" href="#ptbench-config" title="Permalink to this heading">#</a></h3>
-<p>Commands for listing, describing and copying configuration resources.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>COMMAND<span class="w"> </span><span class="o">[</span>ARGS<span class="o">]</span>...
-</pre></div>
-</div>
-<section id="ptbench-config-copy">
-<h4>copy<a class="headerlink" href="#ptbench-config-copy" title="Permalink to this heading">#</a></h4>
-<p>Copy a specific configuration resource so it can be modified locally.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>copy<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>SOURCE<span class="w"> </span>DESTINATION
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-copy-v">
-<span id="cmdoption-ptbench-config-copy-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-copy-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-copy-arg-SOURCE">
-<span id="cmdoption-ptbench-config-copy-arg-source"></span><span class="sig-name descname"><span class="pre">SOURCE</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-copy-arg-SOURCE" title="Permalink to this definition">#</a></dt>
-<dd><p>Required argument</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-copy-arg-DESTINATION">
-<span id="cmdoption-ptbench-config-copy-arg-destination"></span><span class="sig-name descname"><span class="pre">DESTINATION</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-copy-arg-DESTINATION" title="Permalink to this definition">#</a></dt>
-<dd><p>Required argument</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Makes a copy of one of the stock configuration files locally, so it can be</div>
-<div class="line-block">
-<div class="line">adapted:</div>
-</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>ptbench<span class="w"> </span>config<span class="w"> </span>copy<span class="w"> </span>montgomery<span class="w"> </span>-vvv<span class="w"> </span>newdataset.py
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-config-describe">
-<h4>describe<a class="headerlink" href="#ptbench-config-describe" title="Permalink to this heading">#</a></h4>
-<p>Describes a specific configuration file.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>describe<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>NAME...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-describe-v">
-<span id="cmdoption-ptbench-config-describe-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-describe-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-describe-arg-NAME">
-<span id="cmdoption-ptbench-config-describe-arg-name"></span><span class="sig-name descname"><span class="pre">NAME</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-describe-arg-NAME" title="Permalink to this definition">#</a></dt>
-<dd><p>Required argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Describes the Montgomery dataset configuration:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>describe<span class="w"> </span>montgomery
-</pre></div>
-</div>
-</div></blockquote>
-<div class="line-block">
-<div class="line">2. Describes the Montgomery dataset configuration and lists its</div>
-<div class="line-block">
-<div class="line">contents:</div>
-</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>describe<span class="w"> </span>montgomery<span class="w"> </span>-v
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-config-list">
-<h4>list<a class="headerlink" href="#ptbench-config-list" title="Permalink to this heading">#</a></h4>
-<p>Lists configuration files installed.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>list<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span>
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-config-list-v">
-<span id="cmdoption-ptbench-config-list-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-config-list-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Lists all configuration resources (type: ptbench.config) installed:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>list
-</pre></div>
-</div>
-</div></blockquote>
-<div class="line-block">
-<div class="line">2. Lists all configuration resources and their descriptions (notice this may</div>
-<div class="line-block">
-<div class="line">be slow as it needs to load all modules once):</div>
-</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>config<span class="w"> </span>list<span class="w"> </span>-v
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-</section>
-<section id="ptbench-dataset">
-<h3>dataset<a class="headerlink" href="#ptbench-dataset" title="Permalink to this heading">#</a></h3>
-<p>Commands for listing and verifying datasets.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>COMMAND<span class="w"> </span><span class="o">[</span>ARGS<span class="o">]</span>...
-</pre></div>
-</div>
-<section id="ptbench-dataset-check">
-<h4>check<a class="headerlink" href="#ptbench-dataset-check" title="Permalink to this heading">#</a></h4>
-<p>Checks file access on one or more datasets.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>check<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>DATASET<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-dataset-check-l">
-<span id="cmdoption-ptbench-dataset-check-limit"></span><span class="sig-name descname"><span class="pre">-l</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--limit</span></span><span class="sig-prename descclassname"> <span class="pre"><limit></span></span><a class="headerlink" href="#cmdoption-ptbench-dataset-check-l" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Limit check to the first N samples in each dataset, making the check sensibly faster. Set it to zero to check everything.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-dataset-check-v">
-<span id="cmdoption-ptbench-dataset-check-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-dataset-check-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-dataset-check-arg-DATASET">
-<span id="cmdoption-ptbench-dataset-check-arg-dataset"></span><span class="sig-name descname"><span class="pre">DATASET</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-dataset-check-arg-DATASET" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<ol class="arabic">
-<li><p>Check if all files of the Montgomery dataset can be loaded:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>check<span class="w"> </span>-vv<span class="w"> </span>montgomery
-</pre></div>
-</div>
-</li>
-<li><p>Check if all files of multiple installed datasets can be loaded:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>check<span class="w"> </span>-vv<span class="w"> </span>montgomery<span class="w"> </span>shenzhen
-</pre></div>
-</div>
-</li>
-<li><p>Check if all files of all installed datasets can be loaded:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>check
-</pre></div>
-</div>
-</li>
-</ol>
-</section>
-<section id="ptbench-dataset-list">
-<h4>list<a class="headerlink" href="#ptbench-dataset-list" title="Permalink to this heading">#</a></h4>
-<p>Lists all supported and configured datasets.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>list<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span>
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-dataset-list-v">
-<span id="cmdoption-ptbench-dataset-list-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-dataset-list-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. To install a dataset, set up its data directory (“datadirâ€). For</div>
-<div class="line-block">
-<div class="line">example, to setup access to Montgomery files you downloaded locally at</div>
-<div class="line">the directory “/path/to/montgomery/filesâ€, edit the RC file (typically</div>
-<div class="line"><code class="docutils literal notranslate"><span class="pre">$HOME/.config/ptbench.toml</span></code>), and add a line like the following:</div>
-</div>
-</div>
-<blockquote>
-<div><div class="highlight-toml notranslate"><div class="highlight"><pre><span></span><span class="k">[datadir]</span>
-<span class="n">montgomery</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"/path/to/montgomery/files"</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This setting <strong>is</strong> case-sensitive.</p>
-</div>
-</div></blockquote>
-<div class="line-block">
-<div class="line">2. List all raw datasets supported (and configured):</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>ptbench<span class="w"> </span>dataset<span class="w"> </span>list
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-</section>
-<section id="ptbench-evaluate">
-<h3>evaluate<a class="headerlink" href="#ptbench-evaluate" title="Permalink to this heading">#</a></h3>
-<p>Evaluates a CNN on a tuberculosis prediction task.</p>
-<blockquote>
-<div><p>Note: batch size of 1 is required on the predictions.</p>
-</div></blockquote>
-<p>It is possible to pass one or several Python
-files (or names of <code class="docutils literal notranslate"><span class="pre">ptbench.config</span></code> entry points or module names) as
-CONFIG arguments to the command line which contain the parameters listed below as Python variables. The options through the command-line (see below)
-will override the values of configuration files. You can run this command with
-<code class="docutils literal notranslate"><span class="pre"><COMMAND></span> <span class="pre">-H</span> <span class="pre">example_config.py</span></code> to create a template config file.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>evaluate<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>CONFIG<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-o">
-<span id="cmdoption-ptbench-evaluate-output-folder"></span><span class="sig-name descname"><span class="pre">-o</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><output_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-o" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Path where to store the analysis result (created if does not exist)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-p">
-<span id="cmdoption-ptbench-evaluate-predictions-folder"></span><span class="sig-name descname"><span class="pre">-p</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--predictions-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><predictions_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-p" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Path where predictions are currently stored</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-d">
-<span id="cmdoption-ptbench-evaluate-dataset"></span><span class="sig-name descname"><span class="pre">-d</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dataset</span></span><span class="sig-prename descclassname"> <span class="pre"><dataset></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-d" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A torch.utils.data.dataset.Dataset instance implementing a dataset to be used for evaluation purposes, possibly including all pre-processing pipelines required or, optionally, a dictionary mapping string keys to torch.utils.data.dataset.Dataset instances. All keys that do not start with an underscore (_) will be processed.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-t">
-<span id="cmdoption-ptbench-evaluate-threshold"></span><span class="sig-name descname"><span class="pre">-t</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--threshold</span></span><span class="sig-prename descclassname"> <span class="pre"><threshold></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-t" title="Permalink to this definition">#</a></dt>
-<dd><p>This number is used to define positives and negatives from probability maps, and report F1-scores (a priori). It should either come from the training set or a separate validation set to avoid biasing the analysis. Optionally, if you provide a multi-set dataset as input, this may also be the name of an existing set from which the threshold will be estimated (highest F1-score) and then applied to the subsequent sets. This number is also used to print the test set F1-score a priori performance</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-S">
-<span id="cmdoption-ptbench-evaluate-s"></span><span id="cmdoption-ptbench-evaluate-steps"></span><span class="sig-name descname"><span class="pre">-S</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--steps</span></span><span class="sig-prename descclassname"> <span class="pre"><steps></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-S" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> This number is used to define the number of threshold steps to consider when evaluating the highest possible F1-score on test data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">1000</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-v">
-<span id="cmdoption-ptbench-evaluate-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-H">
-<span id="cmdoption-ptbench-evaluate-h"></span><span id="cmdoption-ptbench-evaluate-dump-config"></span><span class="sig-name descname"><span class="pre">-H</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dump-config</span></span><span class="sig-prename descclassname"> <span class="pre"><dump_config></span></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-H" title="Permalink to this definition">#</a></dt>
-<dd><p>Name of the config file to be generated</p>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-evaluate-arg-CONFIG">
-<span id="cmdoption-ptbench-evaluate-arg-config"></span><span class="sig-name descname"><span class="pre">CONFIG</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-evaluate-arg-CONFIG" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Runs evaluation on an existing dataset configuration:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>evaluate<span class="w"> </span>-vv<span class="w"> </span>montgomery<span class="w"> </span>--predictions-folder<span class="o">=</span>path/to/predictions<span class="w"> </span>--output-folder<span class="o">=</span>path/to/results
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-predict">
-<h3>predict<a class="headerlink" href="#ptbench-predict" title="Permalink to this heading">#</a></h3>
-<p>Predicts Tuberculosis presence (probabilities) on input images.</p>
-<p>It is possible to pass one or several Python
-files (or names of <code class="docutils literal notranslate"><span class="pre">ptbench.config</span></code> entry points or module names) as
-CONFIG arguments to the command line which contain the parameters listed below as Python variables. The options through the command-line (see below)
-will override the values of configuration files. You can run this command with
-<code class="docutils literal notranslate"><span class="pre"><COMMAND></span> <span class="pre">-H</span> <span class="pre">example_config.py</span></code> to create a template config file.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>predict<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>CONFIG<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-o">
-<span id="cmdoption-ptbench-predict-output-folder"></span><span class="sig-name descname"><span class="pre">-o</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><output_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-o" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Path where to store the predictions (created if does not exist)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-m">
-<span id="cmdoption-ptbench-predict-model"></span><span class="sig-name descname"><span class="pre">-m</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--model</span></span><span class="sig-prename descclassname"> <span class="pre"><model></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-m" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A torch.nn.Module instance implementing the network to be evaluated</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-d">
-<span id="cmdoption-ptbench-predict-dataset"></span><span class="sig-name descname"><span class="pre">-d</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dataset</span></span><span class="sig-prename descclassname"> <span class="pre"><dataset></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-d" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A torch.utils.data.dataset.Dataset instance implementing a dataset to be used for running prediction, possibly including all pre-processing pipelines required or, optionally, a dictionary mapping string keys to torch.utils.data.dataset.Dataset instances. All keys that do not start with an underscore (_) will be processed.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-b">
-<span id="cmdoption-ptbench-predict-batch-size"></span><span class="sig-name descname"><span class="pre">-b</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--batch-size</span></span><span class="sig-prename descclassname"> <span class="pre"><batch_size></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-b" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Number of samples in every batch (this parameter affects memory requirements for the network)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">1</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-device">
-<span class="sig-name descname"><span class="pre">--device</span></span><span class="sig-prename descclassname"> <span class="pre"><device></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-device" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A string indicating the device to use (e.g. “cpu†or “cuda:0â€)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">cpu</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-w">
-<span id="cmdoption-ptbench-predict-weight"></span><span class="sig-name descname"><span class="pre">-w</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--weight</span></span><span class="sig-prename descclassname"> <span class="pre"><weight></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-w" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Path or URL to pretrained model file (.pth extension)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-r">
-<span id="cmdoption-ptbench-predict-relevance-analysis"></span><span class="sig-name descname"><span class="pre">-r</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--relevance-analysis</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predict-r" title="Permalink to this definition">#</a></dt>
-<dd><p>If set, generate relevance analysis pdfs to indicate the relativeimportance of each feature</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-g">
-<span id="cmdoption-ptbench-predict-grad-cams"></span><span class="sig-name descname"><span class="pre">-g</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--grad-cams</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predict-g" title="Permalink to this definition">#</a></dt>
-<dd><p>If set, generate grad cams for each prediction (must use batch of 1)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-v">
-<span id="cmdoption-ptbench-predict-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predict-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-H">
-<span id="cmdoption-ptbench-predict-h"></span><span id="cmdoption-ptbench-predict-dump-config"></span><span class="sig-name descname"><span class="pre">-H</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dump-config</span></span><span class="sig-prename descclassname"> <span class="pre"><dump_config></span></span><a class="headerlink" href="#cmdoption-ptbench-predict-H" title="Permalink to this definition">#</a></dt>
-<dd><p>Name of the config file to be generated</p>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predict-arg-CONFIG">
-<span id="cmdoption-ptbench-predict-arg-config"></span><span class="sig-name descname"><span class="pre">CONFIG</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predict-arg-CONFIG" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Runs prediction on an existing dataset configuration:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>predict<span class="w"> </span>-vv<span class="w"> </span>pasa<span class="w"> </span>montgomery<span class="w"> </span>--weight<span class="o">=</span>path/to/model_final.pth<span class="w"> </span>--output-folder<span class="o">=</span>path/to/predictions
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-predtojson">
-<h3>predtojson<a class="headerlink" href="#ptbench-predtojson" title="Permalink to this heading">#</a></h3>
-<p>Convert predictions to dataset.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>predtojson<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>LABEL_PATH<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predtojson-f">
-<span id="cmdoption-ptbench-predtojson-output-folder"></span><span class="sig-name descname"><span class="pre">-f</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><output_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-predtojson-f" title="Permalink to this definition">#</a></dt>
-<dd><p>Path where to store the json file (created if does not exist)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predtojson-v">
-<span id="cmdoption-ptbench-predtojson-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predtojson-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-predtojson-arg-LABEL_PATH">
-<span id="cmdoption-ptbench-predtojson-arg-label-path"></span><span class="sig-name descname"><span class="pre">LABEL_PATH</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-predtojson-arg-LABEL_PATH" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Convert predictions of radiological signs to a JSON dataset file:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>predtojson<span class="w"> </span>-vv<span class="w"> </span>train<span class="w"> </span>path/to/train/predictions.csv<span class="w"> </span><span class="nb">test</span><span class="w"> </span>path/to/test/predictions.csv
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-train">
-<h3>train<a class="headerlink" href="#ptbench-train" title="Permalink to this heading">#</a></h3>
-<p>Trains an CNN to perform tuberculosis detection.</p>
-<blockquote>
-<div><p>Training is performed for a configurable number of epochs, and
-generates at least a final_model.pth. It may also generate a number
-of intermediate checkpoints. Checkpoints are model files (.pth
-files) that are stored during the training and useful to resume the
-procedure in case it stops abruptly.</p>
-</div></blockquote>
-<p>It is possible to pass one or several Python
-files (or names of <code class="docutils literal notranslate"><span class="pre">ptbench.config</span></code> entry points or module names) as
-CONFIG arguments to the command line which contain the parameters listed below as Python variables. The options through the command-line (see below)
-will override the values of configuration files. You can run this command with
-<code class="docutils literal notranslate"><span class="pre"><COMMAND></span> <span class="pre">-H</span> <span class="pre">example_config.py</span></code> to create a template config file.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span><span class="o">[</span>CONFIG<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-o">
-<span id="cmdoption-ptbench-train-output-folder"></span><span class="sig-name descname"><span class="pre">-o</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-folder</span></span><span class="sig-prename descclassname"> <span class="pre"><output_folder></span></span><a class="headerlink" href="#cmdoption-ptbench-train-o" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Path where to store the generated model (created if does not exist)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-m">
-<span id="cmdoption-ptbench-train-model"></span><span class="sig-name descname"><span class="pre">-m</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--model</span></span><span class="sig-prename descclassname"> <span class="pre"><model></span></span><a class="headerlink" href="#cmdoption-ptbench-train-m" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A torch.nn.Module instance implementing the network to be trained</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-d">
-<span id="cmdoption-ptbench-train-dataset"></span><span class="sig-name descname"><span class="pre">-d</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dataset</span></span><span class="sig-prename descclassname"> <span class="pre"><dataset></span></span><a class="headerlink" href="#cmdoption-ptbench-train-d" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A dictionary mapping string keys to torch.utils.data.dataset.Dataset instances implementing datasets to be used for training and validating the model, possibly including all pre-processing pipelines required or, optionally, a dictionary mapping string keys to torch.utils.data.dataset.Dataset instances. At least one key named <code class="docutils literal notranslate"><span class="pre">train</span></code> must be available. This dataset will be used for training the network model. The dataset description must include all required pre-processing, including eventual data augmentation. If a dataset named <code class="docutils literal notranslate"><span class="pre">__train__</span></code> is available, it is used prioritarily for training instead of <code class="docutils literal notranslate"><span class="pre">train</span></code>. If a dataset named <code class="docutils literal notranslate"><span class="pre">__valid__</span></code> is available, it is used for model validation (and automatic check-pointing) at each epoch. If a dataset list named <code class="docutils literal notranslate"><span class="pre">__extra_valid__</span></code> is available, then it will be tracked during the validation process and its loss output at the training log as well, in the format of an array occupying a single column. All other keys are considered test datasets and are ignored during training</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-optimizer">
-<span class="sig-name descname"><span class="pre">--optimizer</span></span><span class="sig-prename descclassname"> <span class="pre"><optimizer></span></span><a class="headerlink" href="#cmdoption-ptbench-train-optimizer" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A torch.optim.Optimizer that will be used to train the network</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-criterion">
-<span class="sig-name descname"><span class="pre">--criterion</span></span><span class="sig-prename descclassname"> <span class="pre"><criterion></span></span><a class="headerlink" href="#cmdoption-ptbench-train-criterion" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A loss function to compute the CNN error for every sample respecting the PyTorch API for loss functions (see torch.nn.modules.loss)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-criterion-valid">
-<span class="sig-name descname"><span class="pre">--criterion-valid</span></span><span class="sig-prename descclassname"> <span class="pre"><criterion_valid></span></span><a class="headerlink" href="#cmdoption-ptbench-train-criterion-valid" title="Permalink to this definition">#</a></dt>
-<dd><p>A specific loss function for the validation set to compute the CNNerror for every sample respecting the PyTorch API for loss functions(see torch.nn.modules.loss)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-b">
-<span id="cmdoption-ptbench-train-batch-size"></span><span class="sig-name descname"><span class="pre">-b</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--batch-size</span></span><span class="sig-prename descclassname"> <span class="pre"><batch_size></span></span><a class="headerlink" href="#cmdoption-ptbench-train-b" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Number of samples in every batch (this parameter affects memory requirements for the network). If the number of samples in the batch is larger than the total number of samples available for training, this value is truncated. If this number is smaller, then batches of the specified size are created and fed to the network until there are no more new samples to feed (epoch is finished). If the total number of training samples is not a multiple of the batch-size, the last batch will be smaller than the first, unless –drop-incomplete-batch is set, in which case this batch is not used.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">1</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-c">
-<span id="cmdoption-ptbench-train-batch-chunk-count"></span><span class="sig-name descname"><span class="pre">-c</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--batch-chunk-count</span></span><span class="sig-prename descclassname"> <span class="pre"><batch_chunk_count></span></span><a class="headerlink" href="#cmdoption-ptbench-train-c" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Number of chunks in every batch (this parameter affects memory requirements for the network). The number of samples loaded for every iteration will be batch-size/batch-chunk-count. batch-size needs to be divisible by batch-chunk-count, otherwise an error will be raised. This parameter is used to reduce number of samples loaded in each iteration, in order to reduce the memory usage in exchange for processing time (more iterations). This is specially interesting whe one is running with GPUs with limited RAM. The default of 1 forces the whole batch to be processed at once. Otherwise the batch is broken into batch-chunk-count pieces, and gradients are accumulated to complete each batch.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">1</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-D">
-<span id="cmdoption-ptbench-train-drop-incomplete-batch"></span><span id="cmdoption-ptbench-train-no-drop-incomplete-batch"></span><span class="sig-name descname"><span class="pre">-D</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--drop-incomplete-batch</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--no-drop-incomplete-batch</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-D" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> If set, then may drop the last batch in an epoch, in case it is incomplete. If you set this option, you should also consider increasing the total number of epochs of training, as the total number of training steps may be reduced</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">False</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-e">
-<span id="cmdoption-ptbench-train-epochs"></span><span class="sig-name descname"><span class="pre">-e</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--epochs</span></span><span class="sig-prename descclassname"> <span class="pre"><epochs></span></span><a class="headerlink" href="#cmdoption-ptbench-train-e" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Number of epochs (complete training set passes) to train for. If continuing from a saved checkpoint, ensure to provide a greater number of epochs than that saved on the checkpoint to be loaded.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">1000</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-p">
-<span id="cmdoption-ptbench-train-checkpoint-period"></span><span class="sig-name descname"><span class="pre">-p</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--checkpoint-period</span></span><span class="sig-prename descclassname"> <span class="pre"><checkpoint_period></span></span><a class="headerlink" href="#cmdoption-ptbench-train-p" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Number of epochs after which a checkpoint is saved. A value of zero will disable check-pointing. If checkpointing is enabled and training stops, it is automatically resumed from the last saved checkpoint if training is restarted with the same configuration.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-device">
-<span class="sig-name descname"><span class="pre">--device</span></span><span class="sig-prename descclassname"> <span class="pre"><device></span></span><a class="headerlink" href="#cmdoption-ptbench-train-device" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> A string indicating the device to use (e.g. “cpu†or “cuda:0â€)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">cpu</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-s">
-<span id="cmdoption-ptbench-train-seed"></span><span class="sig-name descname"><span class="pre">-s</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--seed</span></span><span class="sig-prename descclassname"> <span class="pre"><seed></span></span><a class="headerlink" href="#cmdoption-ptbench-train-s" title="Permalink to this definition">#</a></dt>
-<dd><p>Seed to use for the random number generator</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">42</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-P">
-<span id="cmdoption-ptbench-train-parallel"></span><span class="sig-name descname"><span class="pre">-P</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--parallel</span></span><span class="sig-prename descclassname"> <span class="pre"><parallel></span></span><a class="headerlink" href="#cmdoption-ptbench-train-P" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Use multiprocessing for data loading: if set to -1 (default),
-disables multiprocessing data loading. Set to 0 to enable as many data
-loading instances as processing cores as available in the system. Set to
->= 1 to enable that many multiprocessing instances for data loading.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">-1</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-w">
-<span id="cmdoption-ptbench-train-weight"></span><span class="sig-name descname"><span class="pre">-w</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--weight</span></span><span class="sig-prename descclassname"> <span class="pre"><weight></span></span><a class="headerlink" href="#cmdoption-ptbench-train-w" title="Permalink to this definition">#</a></dt>
-<dd><p>Path or URL to pretrained model file (.pth extension)</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-n">
-<span id="cmdoption-ptbench-train-normalization"></span><span class="sig-name descname"><span class="pre">-n</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--normalization</span></span><span class="sig-prename descclassname"> <span class="pre"><normalization></span></span><a class="headerlink" href="#cmdoption-ptbench-train-n" title="Permalink to this definition">#</a></dt>
-<dd><p>Z-Normalization of input images: ‘imagenet’ for ImageNet parameters, ‘current’ for parameters of the current trainset, ‘none’ for no normalization.</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-I">
-<span id="cmdoption-ptbench-train-i"></span><span id="cmdoption-ptbench-train-monitoring-interval"></span><span class="sig-name descname"><span class="pre">-I</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--monitoring-interval</span></span><span class="sig-prename descclassname"> <span class="pre"><monitoring_interval></span></span><a class="headerlink" href="#cmdoption-ptbench-train-I" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Time between checks for the use of resources during each training
-epoch. An interval of 5 seconds, for example, will lead to CPU and GPU
-resources being probed every 5 seconds during each training epoch.
-Values registered in the training logs correspond to averages (or maxima)
-observed through possibly many probes in each epoch. Notice that setting a
-very small value may cause the probing process to become extremely busy,
-potentially biasing the overall perception of resource usage.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">5.0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-v">
-<span id="cmdoption-ptbench-train-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-H">
-<span id="cmdoption-ptbench-train-h"></span><span id="cmdoption-ptbench-train-dump-config"></span><span class="sig-name descname"><span class="pre">-H</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dump-config</span></span><span class="sig-prename descclassname"> <span class="pre"><dump_config></span></span><a class="headerlink" href="#cmdoption-ptbench-train-H" title="Permalink to this definition">#</a></dt>
-<dd><p>Name of the config file to be generated</p>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-arg-CONFIG">
-<span id="cmdoption-ptbench-train-arg-config"></span><span class="sig-name descname"><span class="pre">CONFIG</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-arg-CONFIG" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Trains PASA model with Montgomery dataset, on a GPU (<code class="docutils literal notranslate"><span class="pre">cuda:0</span></code>):</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span>-vv<span class="w"> </span>pasa<span class="w"> </span>montgomery<span class="w"> </span>--batch-size<span class="o">=</span><span class="m">4</span><span class="w"> </span>--device<span class="o">=</span><span class="s2">"cuda:0"</span>
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-<section id="ptbench-train-analysis">
-<h3>train-analysis<a class="headerlink" href="#ptbench-train-analysis" title="Permalink to this heading">#</a></h3>
-<dl class="simple">
-<dt>Analyzes the training logs for loss evolution and resource</dt><dd><p>utilisation.</p>
-</dd>
-</dl>
-<p>It is possible to pass one or several Python
-files (or names of <code class="docutils literal notranslate"><span class="pre">ptbench.config</span></code> entry points or module names) as
-CONFIG arguments to the command line which contain the parameters listed below as Python variables. The options through the command-line (see below)
-will override the values of configuration files. You can run this command with
-<code class="docutils literal notranslate"><span class="pre"><COMMAND></span> <span class="pre">-H</span> <span class="pre">example_config.py</span></code> to create a template config file.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train-analysis<span class="w"> </span><span class="o">[</span>OPTIONS<span class="o">]</span><span class="w"> </span>LOG<span class="w"> </span>CONSTANTS<span class="w"> </span><span class="o">[</span>CONFIG<span class="o">]</span>...
-</pre></div>
-</div>
-<p class="rubric">Options</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-o">
-<span id="cmdoption-ptbench-train-analysis-output-pdf"></span><span class="sig-name descname"><span class="pre">-o</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--output-pdf</span></span><span class="sig-prename descclassname"> <span class="pre"><output_pdf></span></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-o" title="Permalink to this definition">#</a></dt>
-<dd><p><strong>Required</strong> Name of the output file to dump</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">trainlog.pdf</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-v">
-<span id="cmdoption-ptbench-train-analysis-verbose"></span><span class="sig-name descname"><span class="pre">-v</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--verbose</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-v" title="Permalink to this definition">#</a></dt>
-<dd><p>Increase the verbosity level from 0 (only error and critical) messages will be displayed, to 1 (like 0, but adds warnings), 2 (like 1, but adds info messags), and 3 (like 2, but also adds debugging messages) by adding the –verbose option as often as desired (e.g. ‘-vvv’ for debug).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Default<span class="colon">:</span></dt>
-<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-H">
-<span id="cmdoption-ptbench-train-analysis-h"></span><span id="cmdoption-ptbench-train-analysis-dump-config"></span><span class="sig-name descname"><span class="pre">-H</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--dump-config</span></span><span class="sig-prename descclassname"> <span class="pre"><dump_config></span></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-H" title="Permalink to this definition">#</a></dt>
-<dd><p>Name of the config file to be generated</p>
-</dd></dl>
-
-<p class="rubric">Arguments</p>
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-arg-LOG">
-<span id="cmdoption-ptbench-train-analysis-arg-log"></span><span class="sig-name descname"><span class="pre">LOG</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-arg-LOG" title="Permalink to this definition">#</a></dt>
-<dd><p>Required argument</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-arg-CONSTANTS">
-<span id="cmdoption-ptbench-train-analysis-arg-constants"></span><span class="sig-name descname"><span class="pre">CONSTANTS</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-arg-CONSTANTS" title="Permalink to this definition">#</a></dt>
-<dd><p>Required argument</p>
-</dd></dl>
-
-<dl class="std option">
-<dt class="sig sig-object std" id="cmdoption-ptbench-train-analysis-arg-CONFIG">
-<span id="cmdoption-ptbench-train-analysis-arg-config"></span><span class="sig-name descname"><span class="pre">CONFIG</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-ptbench-train-analysis-arg-CONFIG" title="Permalink to this definition">#</a></dt>
-<dd><p>Optional argument(s)</p>
-</dd></dl>
-
-<p>Examples:</p>
-<div class="line-block">
-<div class="line">1. Analyzes a training log and produces various plots:</div>
-</div>
-<blockquote>
-<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train-analysis<span class="w"> </span>-vv<span class="w"> </span>log.csv<span class="w"> </span>constants.csv
-</pre></div>
-</div>
-</div></blockquote>
-</section>
-</section>
-</section>
-
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- <section id="preset-configurations">
-<span id="ptbench-config"></span><h1>Preset Configurations<a class="headerlink" href="#preset-configurations" title="Permalink to this heading">#</a></h1>
-<p>This module contains preset configurations for baseline CNN architectures and
-datasets.</p>
-<section id="models">
-<h2>Models<a class="headerlink" href="#models" title="Permalink to this heading">#</a></h2>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.alexnet.html#module-ptbench.configs.models.alexnet" title="ptbench.configs.models.alexnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.alexnet</span></code></a></p></td>
-<td><p>AlexNet.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.alexnet_pretrained.html#module-ptbench.configs.models.alexnet_pretrained" title="ptbench.configs.models.alexnet_pretrained"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.alexnet_pretrained</span></code></a></p></td>
-<td><p>AlexNet.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.densenet</span></code></a></p></td>
-<td><p>DenseNet.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.densenet_pretrained.html#module-ptbench.configs.models.densenet_pretrained" title="ptbench.configs.models.densenet_pretrained"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.densenet_pretrained</span></code></a></p></td>
-<td><p>DenseNet.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression" title="ptbench.configs.models.logistic_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.logistic_regression</span></code></a></p></td>
-<td><p>Feedforward network for Tuberculosis Detection.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa" title="ptbench.configs.models.pasa"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.pasa</span></code></a></p></td>
-<td><p>CNN for Tuberculosis Detection.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models.signs_to_tb.html#module-ptbench.configs.models.signs_to_tb" title="ptbench.configs.models.signs_to_tb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models.signs_to_tb</span></code></a></p></td>
-<td><p>Feedforward network for Tuberculosis Detection.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/models/ptbench.configs.models_datasets.densenet_rs.html#module-ptbench.configs.models_datasets.densenet_rs" title="ptbench.configs.models_datasets.densenet_rs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.models_datasets.densenet_rs</span></code></a></p></td>
-<td><p>CNN for radiological findings detection.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="datasets">
-<span id="ptbench-configs-datasets"></span><h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this heading">#</a></h2>
-<p>Datasets include iterative accessors to raw data
-(<a class="reference internal" href="install.html#ptbench-setup-datasets"><span class="std std-ref">Supported Datasets</span></a>) including data pre-processing and augmentation,
-if applicable. Use these datasets for training and evaluating your models.</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian.default.html#module-ptbench.configs.datasets.indian.default" title="ptbench.configs.datasets.indian.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian.default</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (default protocol)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian.rgb.html#module-ptbench.configs.datasets.indian.rgb" title="ptbench.configs.datasets.indian.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian.rgb</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (default protocol, converted in RGB)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian_RS.default.html#module-ptbench.configs.datasets.indian_RS.default" title="ptbench.configs.datasets.indian_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian_RS.default</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (default protocol) (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch.default.html#module-ptbench.configs.datasets.mc_ch.default" title="ptbench.configs.datasets.mc_ch.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.html#module-ptbench.configs.datasets.mc_ch.rgb" title="ptbench.configs.datasets.mc_ch.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch.rgb</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen (RGB) datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.html#module-ptbench.configs.datasets.mc_ch_RS.default" title="ptbench.configs.datasets.mc_ch_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_RS.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.default.html#module-ptbench.configs.datasets.mc_ch_in.default" title="ptbench.configs.datasets.mc_ch_in.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html#module-ptbench.configs.datasets.mc_ch_in.rgb" title="ptbench.configs.datasets.mc_ch_in.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in.rgb</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian (RGB) datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_RS.default" title="ptbench.configs.datasets.mc_ch_in_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in_RS.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.html#module-ptbench.configs.datasets.mc_ch_in_pc.default" title="ptbench.configs.datasets.mc_ch_in_pc.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in_pc.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.rgb.html#module-ptbench.configs.datasets.mc_ch_in_pc.rgb" title="ptbench.configs.datasets.mc_ch_in_pc.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in_pc.rgb</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest (RGB) datasets.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_pc_RS.default" title="ptbench.configs.datasets.mc_ch_in_pc_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in_pc_RS.default</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen, Indian and PadChest (TB) datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery.default.html#module-ptbench.configs.datasets.montgomery.default" title="ptbench.configs.datasets.montgomery.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery.default</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (default protocol)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery.rgb.html#module-ptbench.configs.datasets.montgomery.rgb" title="ptbench.configs.datasets.montgomery.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery.rgb</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (default protocol, converted in RGB)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.html#module-ptbench.configs.datasets.montgomery_RS.default" title="ptbench.configs.datasets.montgomery_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery_RS.default</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (default protocol) (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html#module-ptbench.configs.datasets.nih_cxr14_re.cardiomegaly" title="ptbench.configs.datasets.nih_cxr14_re.cardiomegaly"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.nih_cxr14_re.cardiomegaly</span></code></a></p></td>
-<td><p>NIH CXR14 dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.default.html#module-ptbench.configs.datasets.nih_cxr14_re.default" title="ptbench.configs.datasets.nih_cxr14_re.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.nih_cxr14_re.default</span></code></a></p></td>
-<td><p>NIH CXR14 (relabeled) dataset for computer-aided diagnosis (default protocol)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.html#module-ptbench.configs.datasets.nih_cxr14_re_pc.idiap" title="ptbench.configs.datasets.nih_cxr14_re_pc.idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.nih_cxr14_re_pc.idiap</span></code></a></p></td>
-<td><p>Aggregated dataset composed of NIH CXR14 relabeld and PadChest (normalized) datasets.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.html#module-ptbench.configs.datasets.padchest.cardiomegaly_idiap" title="ptbench.configs.datasets.padchest.cardiomegaly_idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest.cardiomegaly_idiap</span></code></a></p></td>
-<td><p>Padchest cardiomegaly (idiap protocol) dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest.idiap.html#module-ptbench.configs.datasets.padchest.idiap" title="ptbench.configs.datasets.padchest.idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest.idiap</span></code></a></p></td>
-<td><p>Padchest (idiap protocol) dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest.no_tb_idiap.html#module-ptbench.configs.datasets.padchest.no_tb_idiap" title="ptbench.configs.datasets.padchest.no_tb_idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest.no_tb_idiap</span></code></a></p></td>
-<td><p>Padchest tuberculosis (no TB idiap protocol) dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html#module-ptbench.configs.datasets.padchest.tb_idiap" title="ptbench.configs.datasets.padchest.tb_idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest.tb_idiap</span></code></a></p></td>
-<td><p>Padchest tuberculosis (idiap protocol) dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap_rgb.html#module-ptbench.configs.datasets.padchest.tb_idiap_rgb" title="ptbench.configs.datasets.padchest.tb_idiap_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest.tb_idiap_rgb</span></code></a></p></td>
-<td><p>Padchest tuberculosis (idiap protocol, rgb) dataset for computer-aided diagnosis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html#module-ptbench.configs.datasets.padchest_RS.tb_idiap" title="ptbench.configs.datasets.padchest_RS.tb_idiap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.padchest_RS.tb_idiap</span></code></a></p></td>
-<td><p>Extended Padchest TB dataset for TB detection (default protocol) (extended with DensenetRS predictions)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen.default.html#module-ptbench.configs.datasets.shenzhen.default" title="ptbench.configs.datasets.shenzhen.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen.default</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (default protocol)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.html#module-ptbench.configs.datasets.shenzhen.rgb" title="ptbench.configs.datasets.shenzhen.rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen.rgb</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (default protocol, converted in RGB)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.html#module-ptbench.configs.datasets.shenzhen_RS.default" title="ptbench.configs.datasets.shenzhen_RS.default"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen_RS.default</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (default protocol) (extended with DensenetRS predictions)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="cross-validation-datasets">
-<span id="ptbench-configs-datasets-folds"></span><h2>Cross-Validation Datasets<a class="headerlink" href="#cross-validation-datasets" title="Permalink to this heading">#</a></h2>
-<p>We support cross-validation with precise preset folds. In this section, you
-will find the configuration for the first fold (fold-0) for all supported
-datasets. Nine other folds are available for every configuration (from 1 to
-9), making up 10 folds per supported dataset.</p>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.html#module-ptbench.configs.datasets.hivtb.fold_0" title="ptbench.configs.datasets.hivtb.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.hivtb.fold_0</span></code></a></p></td>
-<td><p>HIV-TB dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.html#module-ptbench.configs.datasets.hivtb.fold_0_rgb" title="ptbench.configs.datasets.hivtb.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.hivtb.fold_0_rgb</span></code></a></p></td>
-<td><p>HIV-TB dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html#module-ptbench.configs.datasets.hivtb_RS.fold_0" title="ptbench.configs.datasets.hivtb_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.hivtb_RS.fold_0</span></code></a></p></td>
-<td><p>HIV-TB dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0.html#module-ptbench.configs.datasets.indian.fold_0" title="ptbench.configs.datasets.indian.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian.fold_0</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.html#module-ptbench.configs.datasets.indian.fold_0_rgb" title="ptbench.configs.datasets.indian.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian.fold_0_rgb</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (cross validation fold 0, RGB)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.html#module-ptbench.configs.datasets.indian_RS.fold_0" title="ptbench.configs.datasets.indian_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.indian_RS.fold_0</span></code></a></p></td>
-<td><p>Indian dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.html#module-ptbench.configs.datasets.mc_ch.fold_0" title="ptbench.configs.datasets.mc_ch.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch.fold_0</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch.fold_0_rgb" title="ptbench.configs.datasets.mc_ch.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch.fold_0_rgb</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross validation fold 0, RGB)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_RS.fold_0" title="ptbench.configs.datasets.mc_ch_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_RS.fold_0</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery and Shenzhen datasets (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html#module-ptbench.configs.datasets.mc_ch_in.fold_0" title="ptbench.configs.datasets.mc_ch_in.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in.fold_0</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch_in.fold_0_rgb" title="ptbench.configs.datasets.mc_ch_in.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in.fold_0_rgb</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets (cross validation fold 0, RGB)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_in_RS.fold_0" title="ptbench.configs.datasets.mc_ch_in_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.mc_ch_in_RS.fold_0</span></code></a></p></td>
-<td><p>Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.html#module-ptbench.configs.datasets.montgomery.fold_0" title="ptbench.configs.datasets.montgomery.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery.fold_0</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0_rgb.html#module-ptbench.configs.datasets.montgomery.fold_0_rgb" title="ptbench.configs.datasets.montgomery.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery.fold_0_rgb</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (cross validation fold 0, RGB)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.html#module-ptbench.configs.datasets.montgomery_RS.fold_0" title="ptbench.configs.datasets.montgomery_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.montgomery_RS.fold_0</span></code></a></p></td>
-<td><p>Montgomery dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.html#module-ptbench.configs.datasets.shenzhen.fold_0" title="ptbench.configs.datasets.shenzhen.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen.fold_0</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html#module-ptbench.configs.datasets.shenzhen.fold_0_rgb" title="ptbench.configs.datasets.shenzhen.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen.fold_0_rgb</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (cross validation fold 0, RGB)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.html#module-ptbench.configs.datasets.shenzhen_RS.fold_0" title="ptbench.configs.datasets.shenzhen_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.shenzhen_RS.fold_0</span></code></a></p></td>
-<td><p>Shenzhen dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.html#module-ptbench.configs.datasets.tbpoc.fold_0" title="ptbench.configs.datasets.tbpoc.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.tbpoc.fold_0</span></code></a></p></td>
-<td><p>TB-POC dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.html#module-ptbench.configs.datasets.tbpoc.fold_0_rgb" title="ptbench.configs.datasets.tbpoc.fold_0_rgb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.tbpoc.fold_0_rgb</span></code></a></p></td>
-<td><p>TB-POC dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.html#module-ptbench.configs.datasets.tbpoc_RS.fold_0" title="ptbench.configs.datasets.tbpoc_RS.fold_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ptbench.configs.datasets.tbpoc_RS.fold_0</span></code></a></p></td>
-<td><p>TB-POC dataset for TB detection (cross validation fold 0)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
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-<section class="genindex-section">
- <h1 id="index">Index</h1>
- <div class="genindex-jumpbox"><a href="#Symbols"><strong>Symbols</strong></a> | <a href="#A"><strong>A</strong></a> | <a href="#B"><strong>B</strong></a> | <a href="#C"><strong>C</strong></a> | <a href="#D"><strong>D</strong></a> | <a href="#E"><strong>E</strong></a> | <a href="#F"><strong>F</strong></a> | <a href="#G"><strong>G</strong></a> | <a href="#H"><strong>H</strong></a> | <a href="#J"><strong>J</strong></a> | <a href="#L"><strong>L</strong></a> | <a href="#M"><strong>M</strong></a> | <a href="#N"><strong>N</strong></a> | <a href="#P"><strong>P</strong></a> | <a href="#R"><strong>R</strong></a> | <a href="#S"><strong>S</strong></a> | <a href="#T"><strong>T</strong></a> | <a href="#U"><strong>U</strong></a> | <a href="#V"><strong>V</strong></a> | <a href="#W"><strong>W</strong></a></div>
-</section>
-<section id="Symbols" class="genindex-section">
- <h2>Symbols</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- --batch-chunk-count
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-c">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --batch-size
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-b">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-b">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --checkpoint-period
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-p">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --criterion
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-criterion">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --criterion-valid
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-criterion-valid">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --dataset
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-d">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-d">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-d">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --device
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-device">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-device">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --drop-incomplete-batch
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-D">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --dump-config
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-H">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-H">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-H">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-H">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li>
- --epochs
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-e">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --grad-cams
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-g">ptbench-predict command line option</a>
-</li>
- </ul></li>
- <li>
- --limit
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-l">ptbench-dataset-check command line option</a>
-</li>
- </ul></li>
- <li>
- --model
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-m">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-m">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --monitoring-interval
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-I">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --no-drop-incomplete-batch
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-D">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --normalization
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-n">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --optimizer
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-optimizer">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --output-figure
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-f">ptbench-compare command line option</a>
-</li>
- </ul></li>
- <li>
- --output-folder
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-f">ptbench-aggregpred command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-o">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-o">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-f">ptbench-predtojson command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-o">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --output-pdf
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-o">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li>
- --output-table
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-u">ptbench-compare command line option</a>
-</li>
- </ul></li>
- <li>
- --parallel
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-P">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --predictions-folder
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-p">ptbench-evaluate command line option</a>
-</li>
- </ul></li>
- <li>
- --relevance-analysis
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-r">ptbench-predict command line option</a>
-</li>
- </ul></li>
- <li>
- --seed
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-s">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- --steps
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-S">ptbench-evaluate command line option</a>
-</li>
- </ul></li>
- <li>
- --table-format
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-T">ptbench-compare command line option</a>
-</li>
- </ul></li>
- <li>
- --threshold
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-0">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-t">ptbench-evaluate command line option</a>
-</li>
- </ul></li>
- <li>
- --verbose
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-v">ptbench-aggregpred command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-v">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-v">ptbench-config-copy command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-v">ptbench-config-describe command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-list-v">ptbench-config-list command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-v">ptbench-dataset-check command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-list-v">ptbench-dataset-list command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-v">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-v">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-v">ptbench-predtojson command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-v">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-v">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- --weight
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-w">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-w">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -b
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-b">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-b">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -c
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-c">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -D
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-D">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -d
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-d">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-d">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-d">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -e
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-e">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -f
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-f">ptbench-aggregpred command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-f">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-f">ptbench-predtojson command line option</a>
-</li>
- </ul></li>
- <li>
- -g
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-g">ptbench-predict command line option</a>
-</li>
- </ul></li>
- <li>
- -H
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-H">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-H">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-H">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-H">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li>
- -I
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-I">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -l
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-l">ptbench-dataset-check command line option</a>
-</li>
- </ul></li>
- <li>
- -m
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-m">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-m">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -n
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-n">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -o
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-o">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-o">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-o">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-o">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li>
- -P
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-P">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -p
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-p">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-p">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -r
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-r">ptbench-predict command line option</a>
-</li>
- </ul></li>
- <li>
- -S
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-S">ptbench-evaluate command line option</a>
-</li>
- </ul></li>
- <li>
- -s
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-s">ptbench-train command line option</a>
-</li>
- </ul></li>
- <li>
- -T
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-T">ptbench-compare command line option</a>
-</li>
- </ul></li>
- <li>
- -t
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-0">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-t">ptbench-evaluate command line option</a>
-</li>
- </ul></li>
- <li>
- -u
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-u">ptbench-compare command line option</a>
-</li>
- </ul></li>
- <li>
- -v
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-v">ptbench-aggregpred command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-v">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-v">ptbench-config-copy command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-v">ptbench-config-describe command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-list-v">ptbench-config-list command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-v">ptbench-dataset-check command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-list-v">ptbench-dataset-list command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-v">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-v">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-v">ptbench-predtojson command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-v">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-v">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li>
- -w
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-w">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-w">ptbench-train command line option</a>
-</li>
- </ul></li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="A" class="genindex-section">
- <h2>A</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.Alexnet">Alexnet (class in ptbench.models.alexnet)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.align_and_update_state_dicts">align_and_update_state_dicts() (in module ptbench.utils.model_serialization)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue.avg">avg (ptbench.utils.measure.SmoothedValue property)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="B" class="genindex-section">
- <h2>B</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.backward">backward() (ptbench.utils.grad_cams.BaseWrapper method)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.base_measures">base_measures() (in module ptbench.utils.measure)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper">BaseWrapper (class in ptbench.utils.grad_cams)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.bayesian_measures">bayesian_measures() (in module ptbench.utils.measure)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.beta_credible_region">beta_credible_region() (in module ptbench.utils.measure)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.build_alexnet">build_alexnet() (in module ptbench.models.alexnet)</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet.html#ptbench.models.densenet.build_densenet">build_densenet() (in module ptbench.models.densenet)</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.build_densenetrs">build_densenetrs() (in module ptbench.models.densenet_rs)</a>
-</li>
- <li><a href="api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.build_logistic_regression">build_logistic_regression() (in module ptbench.models.logistic_regression)</a>
-</li>
- <li><a href="api/models/ptbench.models.pasa.html#ptbench.models.pasa.build_pasa">build_pasa() (in module ptbench.models.pasa)</a>
-</li>
- <li><a href="api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.build_signs_to_tb">build_signs_to_tb() (in module ptbench.models.signs_to_tb)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="C" class="genindex-section">
- <h2>C</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.model_zoo.html#ptbench.utils.model_zoo.cache_url">cache_url() (in module ptbench.utils.model_zoo)</a>
-</li>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.check">check() (ptbench.data.dataset.CSVDataset method)</a>
-
- <ul>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset.check">(ptbench.data.dataset.JSONDataset method)</a>
-</li>
- </ul></li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.check_exist_logfile">check_exist_logfile() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.check_gpu">check_gpu() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer">Checkpointer (class in ptbench.utils.checkpointer)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.checkpointer_process">checkpointer_process() (in module ptbench.engine.trainer)</a>
-</li>
- <li>
- CONFIG
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-arg-CONFIG">ptbench-evaluate command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-arg-CONFIG">ptbench-predict command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-arg-CONFIG">ptbench-train command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-CONFIG">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- CONSTANTS
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-CONSTANTS">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li><a href="api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset.copy">copy() (ptbench.data.utils.SampleListDataset method)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.cpu_constants">cpu_constants() (in module ptbench.utils.resources)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.CPULogger">CPULogger (class in ptbench.utils.resources)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.create_logfile_fields">create_logfile_fields() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset">CSVDataset (class in ptbench.data.dataset)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="D" class="genindex-section">
- <h2>D</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.sample.html#ptbench.data.sample.DelayedSample.data">data (ptbench.data.sample.DelayedSample property)</a>
-</li>
- <li>
- DATASET
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-arg-DATASET">ptbench-dataset-check command line option</a>
-</li>
- </ul></li>
- <li><a href="api/data/raw/ptbench.data.hivtb.html#ptbench.data.hivtb.dataset">dataset (in module ptbench.data.hivtb)</a>
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.hivtb_RS.html#ptbench.data.hivtb_RS.dataset">(in module ptbench.data.hivtb_RS)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.indian.html#ptbench.data.indian.dataset">(in module ptbench.data.indian)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.indian_RS.html#ptbench.data.indian_RS.dataset">(in module ptbench.data.indian_RS)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.montgomery.html#ptbench.data.montgomery.dataset">(in module ptbench.data.montgomery)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.montgomery_RS.html#ptbench.data.montgomery_RS.dataset">(in module ptbench.data.montgomery_RS)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.nih_cxr14_re.html#ptbench.data.nih_cxr14_re.dataset">(in module ptbench.data.nih_cxr14_re)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.padchest.html#ptbench.data.padchest.dataset">(in module ptbench.data.padchest)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.padchest_RS.html#ptbench.data.padchest_RS.dataset">(in module ptbench.data.padchest_RS)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.shenzhen.html#ptbench.data.shenzhen.dataset">(in module ptbench.data.shenzhen)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.shenzhen_RS.html#ptbench.data.shenzhen_RS.dataset">(in module ptbench.data.shenzhen_RS)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.tbpoc.html#ptbench.data.tbpoc.dataset">(in module ptbench.data.tbpoc)</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.tbpoc_RS.html#ptbench.data.tbpoc_RS.dataset">(in module ptbench.data.tbpoc_RS)</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.sample.html#ptbench.data.sample.DelayedSample">DelayedSample (class in ptbench.data.sample)</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet.html#ptbench.models.densenet.Densenet">Densenet (class in ptbench.models.densenet)</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.DensenetRS">DensenetRS (class in ptbench.models.densenet_rs)</a>
-</li>
- <li>
- DESTINATION
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-arg-DESTINATION">ptbench-config-copy command line option</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.download.html#ptbench.utils.download.download_to_tempfile">download_to_tempfile() (in module ptbench.utils.download)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_zoo.html#ptbench.utils.model_zoo.download_url_to_file">download_url_to_file() (in module ptbench.utils.model_zoo)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="E" class="genindex-section">
- <h2>E</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.eer_threshold">eer_threshold() (in module ptbench.engine.evaluator)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.transforms.html#ptbench.data.transforms.ElasticDeformation">ElasticDeformation (class in ptbench.data.transforms)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="F" class="genindex-section">
- <h2>F</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/models/ptbench.models.alexnet.html#ptbench.models.alexnet.Alexnet.forward">forward() (ptbench.models.alexnet.Alexnet method)</a>
-
- <ul>
- <li><a href="api/models/ptbench.models.densenet.html#ptbench.models.densenet.Densenet.forward">(ptbench.models.densenet.Densenet method)</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet_rs.html#ptbench.models.densenet_rs.DensenetRS.forward">(ptbench.models.densenet_rs.DensenetRS method)</a>
-</li>
- <li><a href="api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.LogisticRegression.forward">(ptbench.models.logistic_regression.LogisticRegression method)</a>
-</li>
- <li><a href="api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer.forward">(ptbench.models.normalizer.TorchVisionNormalizer method)</a>
-</li>
- <li><a href="api/models/ptbench.models.pasa.html#ptbench.models.pasa.PASA.forward">(ptbench.models.pasa.PASA method)</a>
-</li>
- <li><a href="api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.SignsToTB.forward">(ptbench.models.signs_to_tb.SignsToTB method)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.forward">(ptbench.utils.grad_cams.BaseWrapper method)</a>
-</li>
- </ul></li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="G" class="genindex-section">
- <h2>G</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.GB">GB (in module ptbench.utils.resources)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.generate">generate() (ptbench.utils.grad_cams.BaseWrapper method)</a>
-
- <ul>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.GradCAM.generate">(ptbench.utils.grad_cams.GradCAM method)</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.get_centered_maxf1">get_centered_maxf1() (in module ptbench.utils.measure)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.get_positive_weights">get_positive_weights() (in module ptbench.configs.datasets)</a>
-</li>
- <li><a href="api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.get_samples_weights">get_samples_weights() (in module ptbench.configs.datasets)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.gpu_constants">gpu_constants() (in module ptbench.utils.resources)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.gpu_log">gpu_log() (in module ptbench.utils.resources)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.GradCAM">GradCAM (class in ptbench.utils.grad_cams)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="H" class="genindex-section">
- <h2>H</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.has_checkpoint">has_checkpoint() (ptbench.utils.checkpointer.Checkpointer method)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="J" class="genindex-section">
- <h2>J</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset">JSONDataset (class in ptbench.data.dataset)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="L" class="genindex-section">
- <h2>L</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- LABEL_PATH
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-arg-LABEL_PATH">ptbench-aggregpred command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-arg-LABEL_PATH">ptbench-compare command line option</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-arg-LABEL_PATH">ptbench-predtojson command line option</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.last_checkpoint">last_checkpoint() (ptbench.utils.checkpointer.Checkpointer method)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.load">load() (ptbench.utils.checkpointer.Checkpointer method)</a>
-</li>
- <li><a href="api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil">load_pil() (in module ptbench.data.loader)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil_baw">load_pil_baw() (in module ptbench.data.loader)</a>
-</li>
- <li><a href="api/data/ptbench.data.loader.html#ptbench.data.loader.load_pil_rgb">load_pil_rgb() (in module ptbench.data.loader)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.rc.html#ptbench.utils.rc.load_rc">load_rc() (in module ptbench.utils.rc)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.load_state_dict">load_state_dict() (in module ptbench.utils.model_serialization)</a>
-</li>
- <li>
- LOG
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-LOG">ptbench-train-analysis command line option</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.CPULogger.log">log() (ptbench.utils.resources.CPULogger method)</a>
-</li>
- <li><a href="api/models/ptbench.models.logistic_regression.html#ptbench.models.logistic_regression.LogisticRegression">LogisticRegression (class in ptbench.models.logistic_regression)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="M" class="genindex-section">
- <h2>M</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.make_dataset">make_dataset() (in module ptbench.configs.datasets)</a>
-</li>
- <li><a href="api/data/ptbench.data.loader.html#ptbench.data.loader.make_delayed">make_delayed() (in module ptbench.data.loader)</a>
-</li>
- <li><a href="api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.make_subset">make_subset() (in module ptbench.configs.datasets)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue.median">median (ptbench.utils.measure.SmoothedValue property)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_zoo.html#ptbench.utils.model_zoo.modelurls">modelurls (in module ptbench.utils.model_zoo)</a>
-</li>
- <li>
- module
-
- <ul>
- <li><a href="api/data/ptbench.configs.datasets.html#module-ptbench.configs.datasets">ptbench.configs.datasets</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.html#module-ptbench.configs.datasets.hivtb.fold_0">ptbench.configs.datasets.hivtb.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.html#module-ptbench.configs.datasets.hivtb.fold_0_rgb">ptbench.configs.datasets.hivtb.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html#module-ptbench.configs.datasets.hivtb_RS.fold_0">ptbench.configs.datasets.hivtb_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.default.html#module-ptbench.configs.datasets.indian.default">ptbench.configs.datasets.indian.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0.html#module-ptbench.configs.datasets.indian.fold_0">ptbench.configs.datasets.indian.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.html#module-ptbench.configs.datasets.indian.fold_0_rgb">ptbench.configs.datasets.indian.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.rgb.html#module-ptbench.configs.datasets.indian.rgb">ptbench.configs.datasets.indian.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.default.html#module-ptbench.configs.datasets.indian_RS.default">ptbench.configs.datasets.indian_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.html#module-ptbench.configs.datasets.indian_RS.fold_0">ptbench.configs.datasets.indian_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.default.html#module-ptbench.configs.datasets.mc_ch.default">ptbench.configs.datasets.mc_ch.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.html#module-ptbench.configs.datasets.mc_ch.fold_0">ptbench.configs.datasets.mc_ch.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch.fold_0_rgb">ptbench.configs.datasets.mc_ch.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.html#module-ptbench.configs.datasets.mc_ch.rgb">ptbench.configs.datasets.mc_ch.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.default.html#module-ptbench.configs.datasets.mc_ch_in.default">ptbench.configs.datasets.mc_ch_in.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html#module-ptbench.configs.datasets.mc_ch_in.fold_0">ptbench.configs.datasets.mc_ch_in.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch_in.fold_0_rgb">ptbench.configs.datasets.mc_ch_in.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html#module-ptbench.configs.datasets.mc_ch_in.rgb">ptbench.configs.datasets.mc_ch_in.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.html#module-ptbench.configs.datasets.mc_ch_in_pc.default">ptbench.configs.datasets.mc_ch_in_pc.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.rgb.html#module-ptbench.configs.datasets.mc_ch_in_pc.rgb">ptbench.configs.datasets.mc_ch_in_pc.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_pc_RS.default">ptbench.configs.datasets.mc_ch_in_pc_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_RS.default">ptbench.configs.datasets.mc_ch_in_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_in_RS.fold_0">ptbench.configs.datasets.mc_ch_in_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.html#module-ptbench.configs.datasets.mc_ch_RS.default">ptbench.configs.datasets.mc_ch_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_RS.fold_0">ptbench.configs.datasets.mc_ch_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.default.html#module-ptbench.configs.datasets.montgomery.default">ptbench.configs.datasets.montgomery.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.html#module-ptbench.configs.datasets.montgomery.fold_0">ptbench.configs.datasets.montgomery.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0_rgb.html#module-ptbench.configs.datasets.montgomery.fold_0_rgb">ptbench.configs.datasets.montgomery.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.rgb.html#module-ptbench.configs.datasets.montgomery.rgb">ptbench.configs.datasets.montgomery.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.html#module-ptbench.configs.datasets.montgomery_RS.default">ptbench.configs.datasets.montgomery_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.html#module-ptbench.configs.datasets.montgomery_RS.fold_0">ptbench.configs.datasets.montgomery_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html#module-ptbench.configs.datasets.nih_cxr14_re.cardiomegaly">ptbench.configs.datasets.nih_cxr14_re.cardiomegaly</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.default.html#module-ptbench.configs.datasets.nih_cxr14_re.default">ptbench.configs.datasets.nih_cxr14_re.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.html#module-ptbench.configs.datasets.nih_cxr14_re_pc.idiap">ptbench.configs.datasets.nih_cxr14_re_pc.idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.html#module-ptbench.configs.datasets.padchest.cardiomegaly_idiap">ptbench.configs.datasets.padchest.cardiomegaly_idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.idiap.html#module-ptbench.configs.datasets.padchest.idiap">ptbench.configs.datasets.padchest.idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.no_tb_idiap.html#module-ptbench.configs.datasets.padchest.no_tb_idiap">ptbench.configs.datasets.padchest.no_tb_idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html#module-ptbench.configs.datasets.padchest.tb_idiap">ptbench.configs.datasets.padchest.tb_idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap_rgb.html#module-ptbench.configs.datasets.padchest.tb_idiap_rgb">ptbench.configs.datasets.padchest.tb_idiap_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html#module-ptbench.configs.datasets.padchest_RS.tb_idiap">ptbench.configs.datasets.padchest_RS.tb_idiap</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.default.html#module-ptbench.configs.datasets.shenzhen.default">ptbench.configs.datasets.shenzhen.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.html#module-ptbench.configs.datasets.shenzhen.fold_0">ptbench.configs.datasets.shenzhen.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html#module-ptbench.configs.datasets.shenzhen.fold_0_rgb">ptbench.configs.datasets.shenzhen.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.html#module-ptbench.configs.datasets.shenzhen.rgb">ptbench.configs.datasets.shenzhen.rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.html#module-ptbench.configs.datasets.shenzhen_RS.default">ptbench.configs.datasets.shenzhen_RS.default</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.html#module-ptbench.configs.datasets.shenzhen_RS.fold_0">ptbench.configs.datasets.shenzhen_RS.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.html#module-ptbench.configs.datasets.tbpoc.fold_0">ptbench.configs.datasets.tbpoc.fold_0</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.html#module-ptbench.configs.datasets.tbpoc.fold_0_rgb">ptbench.configs.datasets.tbpoc.fold_0_rgb</a>
-</li>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.html#module-ptbench.configs.datasets.tbpoc_RS.fold_0">ptbench.configs.datasets.tbpoc_RS.fold_0</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.alexnet.html#module-ptbench.configs.models.alexnet">ptbench.configs.models.alexnet</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.alexnet_pretrained.html#module-ptbench.configs.models.alexnet_pretrained">ptbench.configs.models.alexnet_pretrained</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet">ptbench.configs.models.densenet</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.densenet_pretrained.html#module-ptbench.configs.models.densenet_pretrained">ptbench.configs.models.densenet_pretrained</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression">ptbench.configs.models.logistic_regression</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa">ptbench.configs.models.pasa</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models.signs_to_tb.html#module-ptbench.configs.models.signs_to_tb">ptbench.configs.models.signs_to_tb</a>
-</li>
- <li><a href="api/configs/models/ptbench.configs.models_datasets.densenet_rs.html#module-ptbench.configs.models_datasets.densenet_rs">ptbench.configs.models_datasets.densenet_rs</a>
-</li>
- <li><a href="api/data/ptbench.data.dataset.html#module-ptbench.data.dataset">ptbench.data.dataset</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb">ptbench.data.hivtb</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.hivtb_RS.html#module-ptbench.data.hivtb_RS">ptbench.data.hivtb_RS</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.indian.html#module-ptbench.data.indian">ptbench.data.indian</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.indian_RS.html#module-ptbench.data.indian_RS">ptbench.data.indian_RS</a>
-</li>
- <li><a href="api/data/ptbench.data.loader.html#module-ptbench.data.loader">ptbench.data.loader</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery">ptbench.data.montgomery</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.montgomery_RS.html#module-ptbench.data.montgomery_RS">ptbench.data.montgomery_RS</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re">ptbench.data.nih_cxr14_re</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest">ptbench.data.padchest</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.padchest_RS.html#module-ptbench.data.padchest_RS">ptbench.data.padchest_RS</a>
-</li>
- <li><a href="api/data/ptbench.data.sample.html#module-ptbench.data.sample">ptbench.data.sample</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen">ptbench.data.shenzhen</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.shenzhen_RS.html#module-ptbench.data.shenzhen_RS">ptbench.data.shenzhen_RS</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc">ptbench.data.tbpoc</a>
-</li>
- <li><a href="api/data/raw/ptbench.data.tbpoc_RS.html#module-ptbench.data.tbpoc_RS">ptbench.data.tbpoc_RS</a>
-</li>
- <li><a href="api/data/ptbench.data.transforms.html#module-ptbench.data.transforms">ptbench.data.transforms</a>
-</li>
- <li><a href="api/data/ptbench.data.utils.html#module-ptbench.data.utils">ptbench.data.utils</a>
-</li>
- <li><a href="api/engine/ptbench.engine.evaluator.html#module-ptbench.engine.evaluator">ptbench.engine.evaluator</a>
-</li>
- <li><a href="api/engine/ptbench.engine.predictor.html#module-ptbench.engine.predictor">ptbench.engine.predictor</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#module-ptbench.engine.trainer">ptbench.engine.trainer</a>
-</li>
- <li><a href="api/models/ptbench.models.alexnet.html#module-ptbench.models.alexnet">ptbench.models.alexnet</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet.html#module-ptbench.models.densenet">ptbench.models.densenet</a>
-</li>
- <li><a href="api/models/ptbench.models.densenet_rs.html#module-ptbench.models.densenet_rs">ptbench.models.densenet_rs</a>
-</li>
- <li><a href="api/models/ptbench.models.logistic_regression.html#module-ptbench.models.logistic_regression">ptbench.models.logistic_regression</a>
-</li>
- <li><a href="api/models/ptbench.models.normalizer.html#module-ptbench.models.normalizer">ptbench.models.normalizer</a>
-</li>
- <li><a href="api/models/ptbench.models.pasa.html#module-ptbench.models.pasa">ptbench.models.pasa</a>
-</li>
- <li><a href="api/models/ptbench.models.signs_to_tb.html#module-ptbench.models.signs_to_tb">ptbench.models.signs_to_tb</a>
-</li>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#module-ptbench.utils.checkpointer">ptbench.utils.checkpointer</a>
-</li>
- <li><a href="api/utils/ptbench.utils.download.html#module-ptbench.utils.download">ptbench.utils.download</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#module-ptbench.utils.grad_cams">ptbench.utils.grad_cams</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#module-ptbench.utils.measure">ptbench.utils.measure</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_serialization.html#module-ptbench.utils.model_serialization">ptbench.utils.model_serialization</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_zoo.html#module-ptbench.utils.model_zoo">ptbench.utils.model_zoo</a>
-</li>
- <li><a href="api/utils/ptbench.utils.plot.html#module-ptbench.utils.plot">ptbench.utils.plot</a>
-</li>
- <li><a href="api/utils/ptbench.utils.rc.html#module-ptbench.utils.rc">ptbench.utils.rc</a>
-</li>
- <li><a href="api/utils/ptbench.utils.resources.html#module-ptbench.utils.resources">ptbench.utils.resources</a>
-</li>
- <li><a href="api/utils/ptbench.utils.summary.html#module-ptbench.utils.summary">ptbench.utils.summary</a>
-</li>
- <li><a href="api/utils/ptbench.utils.table.html#module-ptbench.utils.table">ptbench.utils.table</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.ResourceMonitor.monitored_keys">monitored_keys() (ptbench.utils.resources.ResourceMonitor static method)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="N" class="genindex-section">
- <h2>N</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- NAME
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-arg-NAME">ptbench-config-describe command line option</a>
-</li>
- </ul></li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="P" class="genindex-section">
- <h2>P</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/models/ptbench.models.pasa.html#ptbench.models.pasa.PASA">PASA (class in ptbench.models.pasa)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.table.html#ptbench.utils.table.performance_table">performance_table() (in module ptbench.utils.table)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.posneg">posneg() (in module ptbench.engine.evaluator)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.plot.html#ptbench.utils.plot.precision_recall_f1iso">precision_recall_f1iso() (in module ptbench.utils.plot)</a>
-</li>
- <li>
- ptbench-aggregpred command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-f">--output-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-f">-f</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-aggregpred-arg-LABEL_PATH">LABEL_PATH</a>
-</li>
- </ul></li>
- <li>
- ptbench-compare command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-compare-f">--output-figure</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-u">--output-table</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-T">--table-format</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-0">--threshold</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-f">-f</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-T">-T</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-0">-t</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-u">-u</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-compare-arg-LABEL_PATH">LABEL_PATH</a>
-</li>
- </ul></li>
- <li>
- ptbench-config-copy command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-arg-DESTINATION">DESTINATION</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-arg-SOURCE">SOURCE</a>
-</li>
- </ul></li>
- <li>
- ptbench-config-describe command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-describe-arg-NAME">NAME</a>
-</li>
- </ul></li>
- <li>
- ptbench-config-list command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-list-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-config-list-v">-v</a>
-</li>
- </ul></li>
- <li>
- ptbench-dataset-check command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-l">--limit</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-l">-l</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-check-arg-DATASET">DATASET</a>
-</li>
- </ul></li>
- <li>
- ptbench-dataset-list command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-dataset-list-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-dataset-list-v">-v</a>
-</li>
- </ul></li>
- <li>
- ptbench-evaluate command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-d">--dataset</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-H">--dump-config</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-o">--output-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-p">--predictions-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-S">--steps</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-t">--threshold</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-d">-d</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-H">-H</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-o">-o</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-p">-p</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-S">-S</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-t">-t</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-evaluate-arg-CONFIG">CONFIG</a>
-</li>
- </ul></li>
- <li>
- ptbench-predict command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predict-b">--batch-size</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-d">--dataset</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-device">--device</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-H">--dump-config</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-g">--grad-cams</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-m">--model</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-o">--output-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-r">--relevance-analysis</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-w">--weight</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-b">-b</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-d">-d</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-g">-g</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-H">-H</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-m">-m</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-o">-o</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-r">-r</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-w">-w</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predict-arg-CONFIG">CONFIG</a>
-</li>
- </ul></li>
- <li>
- ptbench-predtojson command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-f">--output-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-f">-f</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-predtojson-arg-LABEL_PATH">LABEL_PATH</a>
-</li>
- </ul></li>
- <li>
- ptbench-train command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-c">--batch-chunk-count</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-b">--batch-size</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-p">--checkpoint-period</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-criterion">--criterion</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-criterion-valid">--criterion-valid</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-d">--dataset</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-device">--device</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-D">--drop-incomplete-batch</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-H">--dump-config</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-e">--epochs</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-m">--model</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-I">--monitoring-interval</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-D">--no-drop-incomplete-batch</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-n">--normalization</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-optimizer">--optimizer</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-o">--output-folder</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-P">--parallel</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-s">--seed</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-w">--weight</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-b">-b</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-c">-c</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-d">-d</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-D">-D</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-e">-e</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-H">-H</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-I">-I</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-m">-m</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-n">-n</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-o">-o</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-p">-p</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-P">-P</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-s">-s</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-w">-w</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-arg-CONFIG">CONFIG</a>
-</li>
- </ul></li>
- <li>
- ptbench-train-analysis command line option
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-H">--dump-config</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-o">--output-pdf</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-v">--verbose</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-H">-H</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-o">-o</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-v">-v</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-CONFIG">CONFIG</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-CONSTANTS">CONSTANTS</a>
-</li>
- <li><a href="cli.html#cmdoption-ptbench-train-analysis-arg-LOG">LOG</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets
-
- <ul>
- <li><a href="api/data/ptbench.configs.datasets.html#module-ptbench.configs.datasets">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.hivtb.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.html#module-ptbench.configs.datasets.hivtb.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.hivtb.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.html#module-ptbench.configs.datasets.hivtb.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.hivtb_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html#module-ptbench.configs.datasets.hivtb_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.default.html#module-ptbench.configs.datasets.indian.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0.html#module-ptbench.configs.datasets.indian.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.html#module-ptbench.configs.datasets.indian.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian.rgb.html#module-ptbench.configs.datasets.indian.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.default.html#module-ptbench.configs.datasets.indian_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.indian_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.html#module-ptbench.configs.datasets.indian_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.default.html#module-ptbench.configs.datasets.mc_ch.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.html#module-ptbench.configs.datasets.mc_ch.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.html#module-ptbench.configs.datasets.mc_ch.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.default.html#module-ptbench.configs.datasets.mc_ch_in.default">module</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- ptbench.configs.datasets.mc_ch_in.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html#module-ptbench.configs.datasets.mc_ch_in.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch_in.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html#module-ptbench.configs.datasets.mc_ch_in.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in_pc.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.html#module-ptbench.configs.datasets.mc_ch_in_pc.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in_pc.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.rgb.html#module-ptbench.configs.datasets.mc_ch_in_pc.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in_pc_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_pc_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_in_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_in_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.html#module-ptbench.configs.datasets.mc_ch_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.mc_ch_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.default.html#module-ptbench.configs.datasets.montgomery.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.html#module-ptbench.configs.datasets.montgomery.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0_rgb.html#module-ptbench.configs.datasets.montgomery.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery.rgb.html#module-ptbench.configs.datasets.montgomery.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.html#module-ptbench.configs.datasets.montgomery_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.montgomery_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.html#module-ptbench.configs.datasets.montgomery_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.nih_cxr14_re.cardiomegaly
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html#module-ptbench.configs.datasets.nih_cxr14_re.cardiomegaly">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.nih_cxr14_re.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.default.html#module-ptbench.configs.datasets.nih_cxr14_re.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.nih_cxr14_re_pc.idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.html#module-ptbench.configs.datasets.nih_cxr14_re_pc.idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest.cardiomegaly_idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.html#module-ptbench.configs.datasets.padchest.cardiomegaly_idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest.idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.idiap.html#module-ptbench.configs.datasets.padchest.idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest.no_tb_idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.no_tb_idiap.html#module-ptbench.configs.datasets.padchest.no_tb_idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest.tb_idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html#module-ptbench.configs.datasets.padchest.tb_idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest.tb_idiap_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap_rgb.html#module-ptbench.configs.datasets.padchest.tb_idiap_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.padchest_RS.tb_idiap
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html#module-ptbench.configs.datasets.padchest_RS.tb_idiap">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.default.html#module-ptbench.configs.datasets.shenzhen.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.html#module-ptbench.configs.datasets.shenzhen.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html#module-ptbench.configs.datasets.shenzhen.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen.rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.html#module-ptbench.configs.datasets.shenzhen.rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen_RS.default
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.html#module-ptbench.configs.datasets.shenzhen_RS.default">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.shenzhen_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.html#module-ptbench.configs.datasets.shenzhen_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.tbpoc.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.html#module-ptbench.configs.datasets.tbpoc.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.tbpoc.fold_0_rgb
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.html#module-ptbench.configs.datasets.tbpoc.fold_0_rgb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.datasets.tbpoc_RS.fold_0
-
- <ul>
- <li><a href="api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.html#module-ptbench.configs.datasets.tbpoc_RS.fold_0">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.alexnet
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.alexnet.html#module-ptbench.configs.models.alexnet">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.alexnet_pretrained
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.alexnet_pretrained.html#module-ptbench.configs.models.alexnet_pretrained">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.densenet
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.densenet_pretrained
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.densenet_pretrained.html#module-ptbench.configs.models.densenet_pretrained">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.logistic_regression
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.pasa
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models.signs_to_tb
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models.signs_to_tb.html#module-ptbench.configs.models.signs_to_tb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.configs.models_datasets.densenet_rs
-
- <ul>
- <li><a href="api/configs/models/ptbench.configs.models_datasets.densenet_rs.html#module-ptbench.configs.models_datasets.densenet_rs">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.dataset
-
- <ul>
- <li><a href="api/data/ptbench.data.dataset.html#module-ptbench.data.dataset">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.hivtb
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.hivtb.html#module-ptbench.data.hivtb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.hivtb_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.hivtb_RS.html#module-ptbench.data.hivtb_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.indian
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.indian.html#module-ptbench.data.indian">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.indian_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.indian_RS.html#module-ptbench.data.indian_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.loader
-
- <ul>
- <li><a href="api/data/ptbench.data.loader.html#module-ptbench.data.loader">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.montgomery
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.montgomery.html#module-ptbench.data.montgomery">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.montgomery_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.montgomery_RS.html#module-ptbench.data.montgomery_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.nih_cxr14_re
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.nih_cxr14_re.html#module-ptbench.data.nih_cxr14_re">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.padchest
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.padchest.html#module-ptbench.data.padchest">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.padchest_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.padchest_RS.html#module-ptbench.data.padchest_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.sample
-
- <ul>
- <li><a href="api/data/ptbench.data.sample.html#module-ptbench.data.sample">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.shenzhen
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.shenzhen.html#module-ptbench.data.shenzhen">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.shenzhen_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.shenzhen_RS.html#module-ptbench.data.shenzhen_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.tbpoc
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.tbpoc.html#module-ptbench.data.tbpoc">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.tbpoc_RS
-
- <ul>
- <li><a href="api/data/raw/ptbench.data.tbpoc_RS.html#module-ptbench.data.tbpoc_RS">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.transforms
-
- <ul>
- <li><a href="api/data/ptbench.data.transforms.html#module-ptbench.data.transforms">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.data.utils
-
- <ul>
- <li><a href="api/data/ptbench.data.utils.html#module-ptbench.data.utils">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.engine.evaluator
-
- <ul>
- <li><a href="api/engine/ptbench.engine.evaluator.html#module-ptbench.engine.evaluator">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.engine.predictor
-
- <ul>
- <li><a href="api/engine/ptbench.engine.predictor.html#module-ptbench.engine.predictor">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.engine.trainer
-
- <ul>
- <li><a href="api/engine/ptbench.engine.trainer.html#module-ptbench.engine.trainer">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.alexnet
-
- <ul>
- <li><a href="api/models/ptbench.models.alexnet.html#module-ptbench.models.alexnet">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.densenet
-
- <ul>
- <li><a href="api/models/ptbench.models.densenet.html#module-ptbench.models.densenet">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.densenet_rs
-
- <ul>
- <li><a href="api/models/ptbench.models.densenet_rs.html#module-ptbench.models.densenet_rs">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.logistic_regression
-
- <ul>
- <li><a href="api/models/ptbench.models.logistic_regression.html#module-ptbench.models.logistic_regression">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.normalizer
-
- <ul>
- <li><a href="api/models/ptbench.models.normalizer.html#module-ptbench.models.normalizer">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.pasa
-
- <ul>
- <li><a href="api/models/ptbench.models.pasa.html#module-ptbench.models.pasa">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.models.signs_to_tb
-
- <ul>
- <li><a href="api/models/ptbench.models.signs_to_tb.html#module-ptbench.models.signs_to_tb">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.checkpointer
-
- <ul>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#module-ptbench.utils.checkpointer">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.download
-
- <ul>
- <li><a href="api/utils/ptbench.utils.download.html#module-ptbench.utils.download">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.grad_cams
-
- <ul>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#module-ptbench.utils.grad_cams">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.measure
-
- <ul>
- <li><a href="api/utils/ptbench.utils.measure.html#module-ptbench.utils.measure">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.model_serialization
-
- <ul>
- <li><a href="api/utils/ptbench.utils.model_serialization.html#module-ptbench.utils.model_serialization">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.model_zoo
-
- <ul>
- <li><a href="api/utils/ptbench.utils.model_zoo.html#module-ptbench.utils.model_zoo">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.plot
-
- <ul>
- <li><a href="api/utils/ptbench.utils.plot.html#module-ptbench.utils.plot">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.rc
-
- <ul>
- <li><a href="api/utils/ptbench.utils.rc.html#module-ptbench.utils.rc">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.resources
-
- <ul>
- <li><a href="api/utils/ptbench.utils.resources.html#module-ptbench.utils.resources">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.summary
-
- <ul>
- <li><a href="api/utils/ptbench.utils.summary.html#module-ptbench.utils.summary">module</a>
-</li>
- </ul></li>
- <li>
- ptbench.utils.table
-
- <ul>
- <li><a href="api/utils/ptbench.utils.table.html#module-ptbench.utils.table">module</a>
-</li>
- </ul></li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="R" class="genindex-section">
- <h2>R</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset.random_permute">random_permute() (ptbench.data.utils.SampleListDataset method)</a>
-</li>
- <li><a href="api/data/ptbench.configs.datasets.html#ptbench.configs.datasets.RANDOM_ROTATION">RANDOM_ROTATION (in module ptbench.configs.datasets)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.plot.html#ptbench.utils.plot.relevance_analysis_plot">relevance_analysis_plot() (in module ptbench.utils.plot)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.grad_cams.html#ptbench.utils.grad_cams.BaseWrapper.remove_hook">remove_hook() (ptbench.utils.grad_cams.BaseWrapper method)</a>
-</li>
- <li><a href="api/data/ptbench.data.transforms.html#ptbench.data.transforms.RemoveBlackBorders">RemoveBlackBorders (class in ptbench.data.transforms)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.ResourceMonitor">ResourceMonitor (class in ptbench.utils.resources)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.plot.html#ptbench.utils.plot.roc_curve">roc_curve() (in module ptbench.utils.plot)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.run">run() (in module ptbench.engine.evaluator)</a>
-
- <ul>
- <li><a href="api/engine/ptbench.engine.predictor.html#ptbench.engine.predictor.run">(in module ptbench.engine.predictor)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.run">(in module ptbench.engine.trainer)</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.resources.html#ptbench.utils.resources.run_nvidia_smi">run_nvidia_smi() (in module ptbench.utils.resources)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="S" class="genindex-section">
- <h2>S</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.sample.html#ptbench.data.sample.Sample">Sample (class in ptbench.data.sample)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.evaluator.html#ptbench.engine.evaluator.sample_measures_for_threshold">sample_measures_for_threshold() (in module ptbench.engine.evaluator)</a>
-</li>
- <li><a href="api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset">SampleListDataset (class in ptbench.data.utils)</a>
-</li>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.samples">samples() (ptbench.data.dataset.CSVDataset method)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.checkpointer.html#ptbench.utils.checkpointer.Checkpointer.save">save() (ptbench.utils.checkpointer.Checkpointer method)</a>
-</li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.save_model_summary">save_model_summary() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer.set_mean_std">set_mean_std() (ptbench.models.normalizer.TorchVisionNormalizer method)</a>
-</li>
- <li><a href="api/models/ptbench.models.signs_to_tb.html#ptbench.models.signs_to_tb.SignsToTB">SignsToTB (class in ptbench.models.signs_to_tb)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/data/ptbench.data.transforms.html#ptbench.data.transforms.SingleAutoLevel16to8">SingleAutoLevel16to8 (class in ptbench.data.transforms)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue">SmoothedValue (class in ptbench.utils.measure)</a>
-</li>
- <li>
- SOURCE
-
- <ul>
- <li><a href="cli.html#cmdoption-ptbench-config-copy-arg-SOURCE">ptbench-config-copy command line option</a>
-</li>
- </ul></li>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.static_information_to_csv">static_information_to_csv() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.model_serialization.html#ptbench.utils.model_serialization.strip_prefix_if_present">strip_prefix_if_present() (in module ptbench.utils.model_serialization)</a>
-</li>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.CSVDataset.subsets">subsets() (ptbench.data.dataset.CSVDataset method)</a>
-
- <ul>
- <li><a href="api/data/ptbench.data.dataset.html#ptbench.data.dataset.JSONDataset.subsets">(ptbench.data.dataset.JSONDataset method)</a>
-</li>
- </ul></li>
- <li><a href="api/utils/ptbench.utils.summary.html#ptbench.utils.summary.summary">summary() (in module ptbench.utils.summary)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="T" class="genindex-section">
- <h2>T</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.torch_evaluation">torch_evaluation() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/models/ptbench.models.normalizer.html#ptbench.models.normalizer.TorchVisionNormalizer">TorchVisionNormalizer (class in ptbench.models.normalizer)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.train_epoch">train_epoch() (in module ptbench.engine.trainer)</a>
-</li>
- <li><a href="api/data/ptbench.data.utils.html#ptbench.data.utils.SampleListDataset.transforms">transforms (ptbench.data.utils.SampleListDataset property)</a>
-</li>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.tricky_division">tricky_division() (in module ptbench.utils.measure)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="U" class="genindex-section">
- <h2>U</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/utils/ptbench.utils.measure.html#ptbench.utils.measure.SmoothedValue.update">update() (ptbench.utils.measure.SmoothedValue method)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="V" class="genindex-section">
- <h2>V</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.validate_epoch">validate_epoch() (in module ptbench.engine.trainer)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-<section id="W" class="genindex-section">
- <h2>W</h2>
- <table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="api/engine/ptbench.engine.trainer.html#ptbench.engine.trainer.write_log_info">write_log_info() (in module ptbench.engine.trainer)</a>
-</li>
- </ul></td>
- </tr></table>
-</section>
-
-
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- </div>
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diff --git a/sphinx/index.html b/sphinx/index.html
deleted file mode 100644
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- <section id="active-pulmonary-tuberculosis-detection-on-chest-x-rays">
-<span id="ptbench"></span><h1>Active Pulmonary Tuberculosis Detection On Chest X-Rays<a class="headerlink" href="#active-pulmonary-tuberculosis-detection-on-chest-x-rays" title="Permalink to this heading">#</a></h1>
-<p>Benchmarks of convolutional neural network (CNN) architectures applied to
-Pulmonary Tuberculosis (TB) detection on chest X-rays (CXR).</p>
-<p>Please use the BibTeX reference below to cite this work:</p>
-<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@INPROCEEDINGS</span><span class="p">{</span><span class="nl">raposo_union_2022</span><span class="p">,</span>
-<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Raposo, Geoffrey and Trajman, Anete and Anjos, Andr{\'{e}}}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="m">11</span><span class="p">,</span>
-<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Pulmonary Tuberculosis Screening from Radiological Signs on Chest X-Ray Images Using Deep Models}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">booktitle</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Union World Conference on Lung Health}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2022}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">date</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2022-11-01}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">organization</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{The Union}</span><span class="p">,</span>
-<span class="p">}</span>
-
-<span class="nc">@TECHREPORT</span><span class="p">{</span><span class="nl">Raposo_Idiap-Com-01-2021</span><span class="p">,</span>
-<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Raposo, Geoffrey}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">keywords</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{deep learning, generalization, Interpretability, transfer learning, Tuberculosis Detection}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">projects</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Idiap}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{7}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Active tuberculosis detection from frontal chest X-ray images}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">type</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Idiap-Com}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">number</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Idiap-Com-01-2021}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2021}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">institution</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Idiap}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://gitlab.idiap.ch/biosignal/software/ptbench}</span><span class="p">,</span>
-<span class="w"> </span><span class="na">pdf</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{https://publidiap.idiap.ch/downloads/reports/2021/Raposo_Idiap-Com-01-2021.pdf}</span>
-<span class="p">}</span>
-</pre></div>
-</div>
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- <article role="main">
- <section id="installation">
-<span id="ptbench-install"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h1>
-<p>We support two installation modes, through <a class="reference external" href="https://pip.pypa.io/en/stable/">pip</a>, or <a class="reference external" href="https://mamba.readthedocs.io/en/latest/index.html">mamba</a> (conda).</p>
-<div class="tab-set docutils container">
-<input checked="True" class="tab-input" id="tab-set--0-input--1" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--1">pip</label><div class="tab-content docutils container">
-<p>Stable, from PyPI:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>ptbench
-</pre></div>
-</div>
-<p>Latest beta, from GitLab package registry:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>--pre<span class="w"> </span>--index-url<span class="w"> </span>https://gitlab.idiap.ch/api/v4/groups/software/-/packages/pypi/simple<span class="w"> </span>--extra-index-url<span class="w"> </span>https://pypi.org/simple<span class="w"> </span>ptbench
-</pre></div>
-</div>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>To avoid long command-lines you may configure pip to define the indexes and
-package search priorities as you like.</p>
-</div>
-</div>
-<input class="tab-input" id="tab-set--0-input--2" name="tab-set--0" type="radio"><label class="tab-label" for="tab-set--0-input--2">mamba/conda</label><div class="tab-content docutils container">
-<p>Stable:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>mamba<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>https://www.idiap.ch/software/biosignal/conda<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>ptbench
-</pre></div>
-</div>
-<p>Latest beta:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>mamba<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>https://www.idiap.ch/software/biosignal/conda/label/beta<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>ptbench
-</pre></div>
-</div>
-</div>
-</div>
-<section id="setup">
-<span id="ptbench-setup"></span><h2>Setup<a class="headerlink" href="#setup" title="Permalink to this heading">#</a></h2>
-<p>A configuration file may be useful to setup global options that should be often
-reused. The location of the configuration file depends on the value of the
-environment variable <code class="docutils literal notranslate"><span class="pre">$XDG_CONFIG_HOME</span></code>, but defaults to
-<code class="docutils literal notranslate"><span class="pre">~/.config/ptbench.toml</span></code>. You may edit this file using your preferred
-editor.</p>
-<p>Here is an example configuration file that may be useful as a starting point:</p>
-<div class="highlight-toml notranslate"><div class="highlight"><pre><span></span><span class="k">[datadir]</span>
-<span class="n">indian</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"/Users/myself/dbs/tbxpredict"</span>
-<span class="n">montgomery</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"/Users/myself/dbs/montgomery-xrayset"</span>
-<span class="n">shenzhen</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"/Users/myself/dbs/shenzhen"</span>
-<span class="n">nih_cxr14_re</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"/Users/myself/dbs/nih-cxr14-re"</span>
-
-<span class="k">[nih_cxr14_re]</span>
-<span class="n">idiap_folder_structure</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">false</span><span class="w"> </span><span class="c1"># set to `true` if at Idiap</span>
-</pre></div>
-</div>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>To get a list of valid data directories that can be configured, execute:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>list
-</pre></div>
-</div>
-<p>You must procure and download datasets by yourself. The raw data is not
-included in this package as we are not authorised to redistribute it.</p>
-<p>To check whether the downloaded version is consistent with the structure
-that is expected by this package, run:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>dataset<span class="w"> </span>check<span class="w"> </span>montgomery
-</pre></div>
-</div>
-</div>
-<section id="supported-datasets">
-<span id="ptbench-setup-datasets"></span><h3>Supported Datasets<a class="headerlink" href="#supported-datasets" title="Permalink to this heading">#</a></h3>
-<p>Here is a list of currently supported datasets in this package, alongside
-notable properties. Each dataset name is linked to the location where
-raw data can be downloaded. The list of images in each split is available
-in the source code.</p>
-<section id="tuberculosis-datasets">
-<span id="ptbench-setup-datasets-tb"></span><h4>Tuberculosis datasets<a class="headerlink" href="#tuberculosis-datasets" title="Permalink to this heading">#</a></h4>
-<p>The following datasets contain only the tuberculosis final diagnosis (0 or 1).
-In addition to the splits presented in the following table, 10 folds
-(for cross-validation) randomly generated are available for these datasets.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Dataset</p></td>
-<td><p>Reference</p></td>
-<td><p>H x W</p></td>
-<td><p>Samples</p></td>
-<td><p>Training</p></td>
-<td><p>Validation</p></td>
-<td><p>Test</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://lhncbc.nlm.nih.gov/publication/pub9931">Montgomery</a></p></td>
-<td><p><a class="reference internal" href="references.html#montgomery-shenzhen-2014" id="id1"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></td>
-<td><p>4020 x 4892</p></td>
-<td><p>138</p></td>
-<td><p>88</p></td>
-<td><p>22</p></td>
-<td><p>28</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference external" href="https://lhncbc.nlm.nih.gov/publication/pub9931">Shenzhen</a></p></td>
-<td><p><a class="reference internal" href="references.html#montgomery-shenzhen-2014" id="id2"><span>[MONTGOMERY-SHENZHEN-2014]</span></a></p></td>
-<td><p>Varying</p></td>
-<td><p>662</p></td>
-<td><p>422</p></td>
-<td><p>107</p></td>
-<td><p>133</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://sourceforge.net/projects/tbxpredict/">Indian</a></p></td>
-<td><p><a class="reference internal" href="references.html#indian-2013" id="id3"><span>[INDIAN-2013]</span></a></p></td>
-<td><p>Varying</p></td>
-<td><p>155</p></td>
-<td><p>83</p></td>
-<td><p>20</p></td>
-<td><p>52</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="tuberculosis-radiological-findings-dataset">
-<span id="ptbench-setup-datasets-tb-signs"></span><h4>Tuberculosis + radiological findings dataset<a class="headerlink" href="#tuberculosis-radiological-findings-dataset" title="Permalink to this heading">#</a></h4>
-<p>The following dataset contains both the tuberculosis final diagnosis (0 or 1)
-and radiological findings.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Dataset</p></td>
-<td><p>Reference</p></td>
-<td><p>H x W</p></td>
-<td><p>Samples</p></td>
-<td><p>Train</p></td>
-<td><p>Test</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://bimcv.cipf.es/bimcv-projects/padchest/">PadChest</a></p></td>
-<td><p><a class="reference internal" href="references.html#padchest-2019" id="id4"><span>[PADCHEST-2019]</span></a></p></td>
-<td><p>Varying</p></td>
-<td><p>160’861</p></td>
-<td><p>160’861</p></td>
-<td><p>0</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="radiological-findings-datasets">
-<span id="ptbench-setup-datasets-signs"></span><h4>Radiological findings datasets<a class="headerlink" href="#radiological-findings-datasets" title="Permalink to this heading">#</a></h4>
-<p>The following dataset contains only the radiological findings without any
-information about tuberculosis.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>NIH CXR14 labels for training and validation sets are the relabeled
-versions done by the author of the CheXNeXt study <a class="reference internal" href="references.html#chexnext-2018" id="id5"><span>[CHEXNEXT-2018]</span></a>.</p>
-</div>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Dataset</p></td>
-<td><p>Reference</p></td>
-<td><p>H x W</p></td>
-<td><p>Samples</p></td>
-<td><p>Training</p></td>
-<td><p>Validation</p></td>
-<td><p>Test</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference external" href="https://nihcc.app.box.com/v/ChestXray-NIHCC">NIH_CXR14_re</a></p></td>
-<td><p><a class="reference internal" href="references.html#nih-cxr14-2017" id="id6"><span>[NIH-CXR14-2017]</span></a></p></td>
-<td><p>1024 x 1024</p></td>
-<td><p>109’041</p></td>
-<td><p>98’637</p></td>
-<td><p>6’350</p></td>
-<td><p>4’054</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="hiv-tuberculosis-datasets">
-<span id="ptbench-setup-datasets-hiv-tb"></span><h4>HIV-Tuberculosis datasets<a class="headerlink" href="#hiv-tuberculosis-datasets" title="Permalink to this heading">#</a></h4>
-<p>The following datasets contain only the tuberculosis final diagnosis (0 or 1)
-and come from HIV infected patients. 10 folds (for cross-validation) randomly
-generated are available for these datasets.</p>
-<p>Please contact the authors of these datasets to have access to the data.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Dataset</p></td>
-<td><p>Reference</p></td>
-<td><p>H x W</p></td>
-<td><p>Samples</p></td>
-</tr>
-<tr class="row-even"><td><p>TB POC</p></td>
-<td><p><a class="reference internal" href="references.html#tb-poc-2018" id="id7"><span>[TB-POC-2018]</span></a></p></td>
-<td><p>2048 x 2500</p></td>
-<td><p>407</p></td>
-</tr>
-<tr class="row-odd"><td><p>HIV TB</p></td>
-<td><p><a class="reference internal" href="references.html#hiv-tb-2019" id="id8"><span>[HIV-TB-2019]</span></a></p></td>
-<td><p>2048 x 2500</p></td>
-<td><p>243</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-</section>
-</section>
-
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-<li><a class="reference internal" href="#tuberculosis-radiological-findings-dataset">Tuberculosis + radiological findings dataset</a></li>
-<li><a class="reference internal" href="#radiological-findings-datasets">Radiological findings datasets</a></li>
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-<section class="domainindex-section">
- <h1>Python Module Index</h1>
- <div class="domainindex-jumpbox"><a href="#cap-p"><strong>p</strong></a></div>
-</section>
-<table class="domainindex-table">
- <tr class="pcap">
- <td></td><td> </td><td></td>
- </tr>
- <tr class="cap" id="cap-p">
- <td></td><td><strong>p</strong></td><td></td>
- </tr>
- <tr>
- <td><img src="_static/minus.png" class="toggler"
- id="toggle-1" style="display: none" alt="-" /></td>
- <td>
- <code class="xref">ptbench</code></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/data/ptbench.configs.datasets.html#module-ptbench.configs.datasets"><code class="xref">ptbench.configs.datasets</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0.html#module-ptbench.configs.datasets.hivtb.fold_0"><code class="xref">ptbench.configs.datasets.hivtb.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.hivtb.fold_0_rgb.html#module-ptbench.configs.datasets.hivtb.fold_0_rgb"><code class="xref">ptbench.configs.datasets.hivtb.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.hivtb_RS.fold_0.html#module-ptbench.configs.datasets.hivtb_RS.fold_0"><code class="xref">ptbench.configs.datasets.hivtb_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian.default.html#module-ptbench.configs.datasets.indian.default"><code class="xref">ptbench.configs.datasets.indian.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0.html#module-ptbench.configs.datasets.indian.fold_0"><code class="xref">ptbench.configs.datasets.indian.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian.fold_0_rgb.html#module-ptbench.configs.datasets.indian.fold_0_rgb"><code class="xref">ptbench.configs.datasets.indian.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian.rgb.html#module-ptbench.configs.datasets.indian.rgb"><code class="xref">ptbench.configs.datasets.indian.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.default.html#module-ptbench.configs.datasets.indian_RS.default"><code class="xref">ptbench.configs.datasets.indian_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.indian_RS.fold_0.html#module-ptbench.configs.datasets.indian_RS.fold_0"><code class="xref">ptbench.configs.datasets.indian_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.default.html#module-ptbench.configs.datasets.mc_ch.default"><code class="xref">ptbench.configs.datasets.mc_ch.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0.html#module-ptbench.configs.datasets.mc_ch.fold_0"><code class="xref">ptbench.configs.datasets.mc_ch.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch.fold_0_rgb"><code class="xref">ptbench.configs.datasets.mc_ch.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch.rgb.html#module-ptbench.configs.datasets.mc_ch.rgb"><code class="xref">ptbench.configs.datasets.mc_ch.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.default.html#module-ptbench.configs.datasets.mc_ch_in.default"><code class="xref">ptbench.configs.datasets.mc_ch_in.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0.html#module-ptbench.configs.datasets.mc_ch_in.fold_0"><code class="xref">ptbench.configs.datasets.mc_ch_in.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.fold_0_rgb.html#module-ptbench.configs.datasets.mc_ch_in.fold_0_rgb"><code class="xref">ptbench.configs.datasets.mc_ch_in.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in.rgb.html#module-ptbench.configs.datasets.mc_ch_in.rgb"><code class="xref">ptbench.configs.datasets.mc_ch_in.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.default.html#module-ptbench.configs.datasets.mc_ch_in_pc.default"><code class="xref">ptbench.configs.datasets.mc_ch_in_pc.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc.rgb.html#module-ptbench.configs.datasets.mc_ch_in_pc.rgb"><code class="xref">ptbench.configs.datasets.mc_ch_in_pc.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_pc_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_pc_RS.default"><code class="xref">ptbench.configs.datasets.mc_ch_in_pc_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.default.html#module-ptbench.configs.datasets.mc_ch_in_RS.default"><code class="xref">ptbench.configs.datasets.mc_ch_in_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_in_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_in_RS.fold_0"><code class="xref">ptbench.configs.datasets.mc_ch_in_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.default.html#module-ptbench.configs.datasets.mc_ch_RS.default"><code class="xref">ptbench.configs.datasets.mc_ch_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.mc_ch_RS.fold_0.html#module-ptbench.configs.datasets.mc_ch_RS.fold_0"><code class="xref">ptbench.configs.datasets.mc_ch_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery.default.html#module-ptbench.configs.datasets.montgomery.default"><code class="xref">ptbench.configs.datasets.montgomery.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0.html#module-ptbench.configs.datasets.montgomery.fold_0"><code class="xref">ptbench.configs.datasets.montgomery.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery.fold_0_rgb.html#module-ptbench.configs.datasets.montgomery.fold_0_rgb"><code class="xref">ptbench.configs.datasets.montgomery.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery.rgb.html#module-ptbench.configs.datasets.montgomery.rgb"><code class="xref">ptbench.configs.datasets.montgomery.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.default.html#module-ptbench.configs.datasets.montgomery_RS.default"><code class="xref">ptbench.configs.datasets.montgomery_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.montgomery_RS.fold_0.html#module-ptbench.configs.datasets.montgomery_RS.fold_0"><code class="xref">ptbench.configs.datasets.montgomery_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.cardiomegaly.html#module-ptbench.configs.datasets.nih_cxr14_re.cardiomegaly"><code class="xref">ptbench.configs.datasets.nih_cxr14_re.cardiomegaly</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re.default.html#module-ptbench.configs.datasets.nih_cxr14_re.default"><code class="xref">ptbench.configs.datasets.nih_cxr14_re.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.nih_cxr14_re_pc.idiap.html#module-ptbench.configs.datasets.nih_cxr14_re_pc.idiap"><code class="xref">ptbench.configs.datasets.nih_cxr14_re_pc.idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest.cardiomegaly_idiap.html#module-ptbench.configs.datasets.padchest.cardiomegaly_idiap"><code class="xref">ptbench.configs.datasets.padchest.cardiomegaly_idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest.idiap.html#module-ptbench.configs.datasets.padchest.idiap"><code class="xref">ptbench.configs.datasets.padchest.idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest.no_tb_idiap.html#module-ptbench.configs.datasets.padchest.no_tb_idiap"><code class="xref">ptbench.configs.datasets.padchest.no_tb_idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap.html#module-ptbench.configs.datasets.padchest.tb_idiap"><code class="xref">ptbench.configs.datasets.padchest.tb_idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest.tb_idiap_rgb.html#module-ptbench.configs.datasets.padchest.tb_idiap_rgb"><code class="xref">ptbench.configs.datasets.padchest.tb_idiap_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.padchest_RS.tb_idiap.html#module-ptbench.configs.datasets.padchest_RS.tb_idiap"><code class="xref">ptbench.configs.datasets.padchest_RS.tb_idiap</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.default.html#module-ptbench.configs.datasets.shenzhen.default"><code class="xref">ptbench.configs.datasets.shenzhen.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0.html#module-ptbench.configs.datasets.shenzhen.fold_0"><code class="xref">ptbench.configs.datasets.shenzhen.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.fold_0_rgb.html#module-ptbench.configs.datasets.shenzhen.fold_0_rgb"><code class="xref">ptbench.configs.datasets.shenzhen.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen.rgb.html#module-ptbench.configs.datasets.shenzhen.rgb"><code class="xref">ptbench.configs.datasets.shenzhen.rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.default.html#module-ptbench.configs.datasets.shenzhen_RS.default"><code class="xref">ptbench.configs.datasets.shenzhen_RS.default</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.shenzhen_RS.fold_0.html#module-ptbench.configs.datasets.shenzhen_RS.fold_0"><code class="xref">ptbench.configs.datasets.shenzhen_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0.html#module-ptbench.configs.datasets.tbpoc.fold_0"><code class="xref">ptbench.configs.datasets.tbpoc.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.tbpoc.fold_0_rgb.html#module-ptbench.configs.datasets.tbpoc.fold_0_rgb"><code class="xref">ptbench.configs.datasets.tbpoc.fold_0_rgb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/datasets/ptbench.configs.datasets.tbpoc_RS.fold_0.html#module-ptbench.configs.datasets.tbpoc_RS.fold_0"><code class="xref">ptbench.configs.datasets.tbpoc_RS.fold_0</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.alexnet.html#module-ptbench.configs.models.alexnet"><code class="xref">ptbench.configs.models.alexnet</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.alexnet_pretrained.html#module-ptbench.configs.models.alexnet_pretrained"><code class="xref">ptbench.configs.models.alexnet_pretrained</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet"><code class="xref">ptbench.configs.models.densenet</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.densenet_pretrained.html#module-ptbench.configs.models.densenet_pretrained"><code class="xref">ptbench.configs.models.densenet_pretrained</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression"><code class="xref">ptbench.configs.models.logistic_regression</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa"><code class="xref">ptbench.configs.models.pasa</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models.signs_to_tb.html#module-ptbench.configs.models.signs_to_tb"><code class="xref">ptbench.configs.models.signs_to_tb</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/configs/models/ptbench.configs.models_datasets.densenet_rs.html#module-ptbench.configs.models_datasets.densenet_rs"><code class="xref">ptbench.configs.models_datasets.densenet_rs</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="api/data/ptbench.data.dataset.html#module-ptbench.data.dataset"><code class="xref">ptbench.data.dataset</code></a></td><td>
- <em></em></td>
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-<h1>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h1>
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- <section id="results">
-<span id="ptbench-results"></span><h1>Results<a class="headerlink" href="#results" title="Permalink to this heading">#</a></h1>
-<p>This section summarizes results that can be obtained with this package.</p>
-<section id="models-optimization">
-<h2>Models optimization<a class="headerlink" href="#models-optimization" title="Permalink to this heading">#</a></h2>
-<p>In the link below, you will find information about the optimization of each
-model we used.</p>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="optimization/pasa.html">Pasa model optimization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="optimization/pasa.html#thresholds-selection">Thresholds selection</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="optimization/densenet.html">Densenet model optimization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="optimization/densenet.html#training-on-tb-datasets-from-scratch">Training on TB datasets from scratch</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimization/densenet.html#pre-training-on-nih-cxr14">Pre-training on NIH CXR14</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimization/densenet.html#fine-tuning-on-tb-datasets">Fine-tuning on TB datasets</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="optimization/logreg.html">LogReg model optimization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="optimization/logreg.html#minimum-validation-loss-grid-search">Minimum validation loss grid search</a></li>
-<li class="toctree-l2"><a class="reference internal" href="optimization/logreg.html#thresholds-selection">Thresholds selection</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="optimization/signstotb.html">SignsToTB model optimization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="optimization/signstotb.html#minimum-validation-loss-grid-search">Minimum validation loss grid search</a></li>
-</ul>
-</li>
-</ul>
-</div>
-</section>
-<section id="models-training-runtime-and-memory-footprint">
-<h2>Models training runtime and memory footprint<a class="headerlink" href="#models-training-runtime-and-memory-footprint" title="Permalink to this heading">#</a></h2>
-<p>In the link below, you will find information about the training runtime and the
-memory footprint of each model we used.</p>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="runtime.html">Models training runtime and memory footprint</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="runtime.html#pasa">Pasa</a></li>
-<li class="toctree-l2"><a class="reference internal" href="runtime.html#densenet-pretraining">Densenet pretraining</a></li>
-<li class="toctree-l2"><a class="reference internal" href="runtime.html#densenet-fine-tuning">Densenet fine-tuning</a></li>
-<li class="toctree-l2"><a class="reference internal" href="runtime.html#logistic-regression">Logistic Regression</a></li>
-</ul>
-</li>
-</ul>
-</div>
-</section>
-<section id="auroc-scores">
-<h2>AUROC Scores<a class="headerlink" href="#auroc-scores" title="Permalink to this heading">#</a></h2>
-<ul class="simple">
-<li><p>Benchmark results for models: Pasa, DenseNet, SignsToTB</p></li>
-<li><p>Each dataset is split in a training, a validation and a testing subset</p></li>
-<li><p>Datasets names are abbreviated as follows: Montgomery (MC), Shenzhen (CH),
-Indian (IN)</p></li>
-<li><p>Models are only trained on the training subset</p></li>
-<li><p>During the training session, we keep checkpoints for the best performing
-networks based on the validation set. The best performing network during
-training is used for evaluation.</p></li>
-<li><p>Model resource configuration links are linked to the originating
-configuration files used to obtain these results.</p></li>
-</ul>
-<section id="k-folding">
-<h3>K-folding<a class="headerlink" href="#k-folding" title="Permalink to this heading">#</a></h3>
-<p>Stratified k-folding has been used (10 folds) to generate these results.</p>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>To generate the following results, you first need to predict TB on each
-fold, then use the <a class="reference internal" href="../cli.html#ptbench-cli"><span class="std std-ref">aggregpred command</span></a> to aggregate the
-predictions together, and finally evaluate this new file using the
-<a class="reference internal" href="../cli.html#ptbench-cli"><span class="std std-ref">compare command</span></a>.</p>
-</div>
-<section id="pasa-and-densenet-121-random-initialization">
-<h4>Pasa and DenseNet-121 (random initialization)<a class="headerlink" href="#pasa-and-densenet-121-random-initialization" title="Permalink to this heading">#</a></h4>
-<p>Thresholds used:</p>
-<ul class="simple">
-<li><p>Pasa trained on MC, test on MC, mean threshold: 0.5057</p></li>
-<li><p>Pasa trained on MC-CH, test on MC-CH, mean threshold: 0.4966</p></li>
-<li><p>Pasa trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4135</p></li>
-<li><p>Densenet trained on MC, test on MC, mean threshold: 0.5183</p></li>
-<li><p>Densenet trained on MC-CH, test on MC-CH, mean threshold: 0.2555</p></li>
-<li><p>Densenet trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4037</p></li>
-</ul>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>AUC</p></td>
-<td><p>MC test</p></td>
-<td><p>CH test</p></td>
-<td><p>IN test</p></td>
-</tr>
-<tr class="row-even"><td><p>Pasa (train: MC)</p></td>
-<td><p>0.890</p></td>
-<td><p>0.576</p></td>
-<td><p>0.642</p></td>
-</tr>
-<tr class="row-odd"><td><p>Pasa (train: MC+CH)</p></td>
-<td><p>0.870</p></td>
-<td><p>0.893</p></td>
-<td><p>0.669</p></td>
-</tr>
-<tr class="row-even"><td><p>Pasa (train: MC+CH+IN)</p></td>
-<td><p>0.881</p></td>
-<td><p>0.898</p></td>
-<td><p>0.848</p></td>
-</tr>
-<tr class="row-odd"><td><p>DenseNet-121 (train: MC)</p></td>
-<td><p>0.822</p></td>
-<td><p>0.607</p></td>
-<td><p>0.625</p></td>
-</tr>
-<tr class="row-even"><td><p>DenseNet-121 (train: MC+CH)</p></td>
-<td><p>0.883</p></td>
-<td><p>0.905</p></td>
-<td><p>0.672</p></td>
-</tr>
-<tr class="row-odd"><td><p>DenseNet-121 (train: MC+CH+IN)</p></td>
-<td><p>0.860</p></td>
-<td><p>0.917</p></td>
-<td><p>0.850</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><figure class="align-center" id="id1">
-<a class="reference internal image-reference" href="../_images/compare_pasa_mc_kfold_500.jpg"><img alt="Testing sets ROC curves for Pasa model trained on normalized-kfold MC" src="../_images/compare_pasa_mc_kfold_500.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 1 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa" title="ptbench.configs.models.pasa"><code class="xref py py-mod docutils literal notranslate"><span class="pre">Pasa</span></code></a>: Pasa trained on normalized-kfold MC</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id2">
-<a class="reference internal image-reference" href="../_images/compare_pasa_mc_ch_kfold_500.jpg"><img alt="Testing sets ROC curves for Pasa model trained on normalized-kfold MC-CH" src="../_images/compare_pasa_mc_ch_kfold_500.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 2 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa" title="ptbench.configs.models.pasa"><code class="xref py py-mod docutils literal notranslate"><span class="pre">Pasa</span></code></a>: Pasa trained on normalized-kfold MC-CH</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id3">
-<a class="reference internal image-reference" href="../_images/compare_pasa_mc_ch_in_kfold_500.jpg"><img alt="Testing sets ROC curves for Pasa model trained on normalized-kfold MC-CH-IN" src="../_images/compare_pasa_mc_ch_in_kfold_500.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 3 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.pasa.html#module-ptbench.configs.models.pasa" title="ptbench.configs.models.pasa"><code class="xref py py-mod docutils literal notranslate"><span class="pre">Pasa</span></code></a>: Pasa trained on normalized-kfold MC-CH-IN</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-<tr class="row-even"><td><figure class="align-center" id="id4">
-<a class="reference internal image-reference" href="../_images/compare_densenet_mc_kfold_2000.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC" src="../_images/compare_densenet_mc_kfold_2000.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 4 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id5">
-<a class="reference internal image-reference" href="../_images/compare_densenet_mc_ch_kfold_2000.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH" src="../_images/compare_densenet_mc_ch_kfold_2000.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 5 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC-CH</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id6">
-<a class="reference internal image-reference" href="../_images/compare_densenet_mc_ch_in_kfold_2000.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN" src="../_images/compare_densenet_mc_ch_in_kfold_2000.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 6 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC-CH-IN</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="densenet-121-pretrained-on-imagenet">
-<h4>DenseNet-121 (pretrained on ImageNet)<a class="headerlink" href="#densenet-121-pretrained-on-imagenet" title="Permalink to this heading">#</a></h4>
-<p>Thresholds used:</p>
-<ul class="simple">
-<li><p>DenseNet (pretrained on ImageNet) trained on MC, test on MC, mean threshold: 0.3581</p></li>
-<li><p>DenseNet (pretrained on ImageNet) trained on MC-CH, test on MC-CH, mean threshold: 0.3319</p></li>
-<li><p>DenseNet (pretrained on ImageNet) trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4048</p></li>
-</ul>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>AUC</p></td>
-<td><p>MC test</p></td>
-<td><p>CH test</p></td>
-<td><p>IN test</p></td>
-</tr>
-<tr class="row-even"><td><p>DenseNet-121 (train: MC)</p></td>
-<td><p>0.910</p></td>
-<td><p>0.814</p></td>
-<td><p>0.817</p></td>
-</tr>
-<tr class="row-odd"><td><p>DenseNet-121 (train: MC+CH)</p></td>
-<td><p>0.948</p></td>
-<td><p>0.946</p></td>
-<td><p>0.816</p></td>
-</tr>
-<tr class="row-even"><td><p>DenseNet-121 (train: MC+CH+IN)</p></td>
-<td><p>0.925</p></td>
-<td><p>0.944</p></td>
-<td><p>0.911</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><figure class="align-center" id="id7">
-<a class="reference internal image-reference" href="../_images/compare_densenetpreIN_mc_kfold_600.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC" src="../_images/compare_densenetpreIN_mc_kfold_600.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 7 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a> DenseNet trained on normalized-kfold MC</span><a class="headerlink" href="#id7" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id8">
-<a class="reference internal image-reference" href="../_images/compare_densenetpreIN_mc_ch_kfold_600.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH" src="../_images/compare_densenetpreIN_mc_ch_kfold_600.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 8 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a> DenseNet trained on normalized-kfold MC-CH</span><a class="headerlink" href="#id8" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id9">
-<a class="reference internal image-reference" href="../_images/compare_densenetpreIN_mc_ch_ch_kfold_600.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN" src="../_images/compare_densenetpreIN_mc_ch_ch_kfold_600.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 9 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a> DenseNet trained on normalized-kfold MC-CH-IN</span><a class="headerlink" href="#id9" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="logistic-regression-classifier">
-<h4>Logistic Regression Classifier<a class="headerlink" href="#logistic-regression-classifier" title="Permalink to this heading">#</a></h4>
-<p>Thresholds used:</p>
-<ul class="simple">
-<li><p>LogReg trained on MC, test on MC, mean threshold: 0.534</p></li>
-<li><p>LogReg trained on MC-CH, test on MC-CH, mean threshold: 0.2838</p></li>
-<li><p>LogReg trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.2371</p></li>
-</ul>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>AUC</p></td>
-<td><p>MC test</p></td>
-<td><p>CH test</p></td>
-<td><p>IN test</p></td>
-</tr>
-<tr class="row-even"><td><p>Indirect (train: MC)</p></td>
-<td><p>0.966</p></td>
-<td><p>0.867</p></td>
-<td><p>0.926</p></td>
-</tr>
-<tr class="row-odd"><td><p>Indirect (train: MC+CH)</p></td>
-<td><p>0.961</p></td>
-<td><p>0.901</p></td>
-<td><p>0.928</p></td>
-</tr>
-<tr class="row-even"><td><p>Indirect (train: MC+CH+IN)</p></td>
-<td><p>0.951</p></td>
-<td><p>0.895</p></td>
-<td><p>0.920</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><figure class="align-center" id="id10">
-<a class="reference internal image-reference" href="../_images/compare_logreg_mc_kfold_150.jpg"><img alt="Testing sets ROC curves for LogReg model trained on normalized-kfold MC" src="../_images/compare_logreg_mc_kfold_150.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 10 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression" title="ptbench.configs.models.logistic_regression"><code class="xref py py-mod docutils literal notranslate"><span class="pre">LogReg</span></code></a>: LogReg trained on normalized-kfold MC</span><a class="headerlink" href="#id10" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id11">
-<a class="reference internal image-reference" href="../_images/compare_logreg_mc_ch_kfold_100.jpg"><img alt="Testing sets ROC curves for LogReg model trained on normalized-kfold MC-CH" src="../_images/compare_logreg_mc_ch_kfold_100.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 11 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression" title="ptbench.configs.models.logistic_regression"><code class="xref py py-mod docutils literal notranslate"><span class="pre">LogReg</span></code></a>: LogReg trained on normalized-kfold MC-CH</span><a class="headerlink" href="#id11" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id12">
-<a class="reference internal image-reference" href="../_images/compare_logreg_mc_ch_in_kfold_100.jpg"><img alt="Testing sets ROC curves for LogReg model trained on normalized-kfold MC-CH-IN" src="../_images/compare_logreg_mc_ch_in_kfold_100.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 12 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.logistic_regression.html#module-ptbench.configs.models.logistic_regression" title="ptbench.configs.models.logistic_regression"><code class="xref py py-mod docutils literal notranslate"><span class="pre">LogReg</span></code></a>: LogReg trained on normalized-kfold MC-CH-IN</span><a class="headerlink" href="#id12" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="densenet-121-pretrained-on-imagenet-and-nih-cxr14">
-<h4>DenseNet-121 (pretrained on ImageNet and NIH CXR14)<a class="headerlink" href="#densenet-121-pretrained-on-imagenet-and-nih-cxr14" title="Permalink to this heading">#</a></h4>
-<p>Thresholds used:</p>
-<ul class="simple">
-<li><p>DenseNetPre trained on MC, test on MC, mean threshold: 0.4126</p></li>
-<li><p>DenseNetPre trained on MC-CH, test on MC-CH, mean threshold: 0.3711</p></li>
-<li><p>DenseNetPre trained on MC-CH-IN, test on MC-CH-IN, mean threshold: 0.4255</p></li>
-</ul>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>AUC</p></td>
-<td><p>MC test</p></td>
-<td><p>CH test</p></td>
-<td><p>IN test</p></td>
-</tr>
-<tr class="row-even"><td><p>DenseNet-121 (train: MC)</p></td>
-<td><p>0.966</p></td>
-<td><p>0.917</p></td>
-<td><p>0.901</p></td>
-</tr>
-<tr class="row-odd"><td><p>DenseNet-121 (train: MC+CH)</p></td>
-<td><p>0.984</p></td>
-<td><p>0.979</p></td>
-<td><p>0.869</p></td>
-</tr>
-<tr class="row-even"><td><p>DenseNet-121 (train: MC+CH+IN)</p></td>
-<td><p>0.965</p></td>
-<td><p>0.978</p></td>
-<td><p>0.931</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><figure class="align-center" id="id13">
-<a class="reference internal image-reference" href="../_images/compare_densenetpre_mc_kfold_300.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC (pretrained on NIH)" src="../_images/compare_densenetpre_mc_kfold_300.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 13 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC (pretrained on NIH)</span><a class="headerlink" href="#id13" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id14">
-<a class="reference internal image-reference" href="../_images/compare_densenetpre_mc_ch_kfold_300.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH (pretrained on NIH)" src="../_images/compare_densenetpre_mc_ch_kfold_300.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 14 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC-CH (pretrained on NIH)</span><a class="headerlink" href="#id14" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id15">
-<a class="reference internal image-reference" href="../_images/compare_densenetpre_mc_ch_in_kfold_300.jpg"><img alt="Testing sets ROC curves for DenseNet model trained on normalized-kfold MC-CH-IN (pretrained on NIH)" src="../_images/compare_densenetpre_mc_ch_in_kfold_300.jpg" style="width: 320.0px; height: 240.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 15 </span><span class="caption-text"><a class="reference internal" href="../api/configs/models/ptbench.configs.models.densenet.html#module-ptbench.configs.models.densenet" title="ptbench.configs.models.densenet"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DenseNet</span></code></a>: DenseNet trained on normalized-kfold MC-CH-IN (pretrained on NIH)</span><a class="headerlink" href="#id15" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-</section>
-<section id="global-sensitivity-analysis-relevance">
-<h2>Global sensitivity analysis (relevance)<a class="headerlink" href="#global-sensitivity-analysis-relevance" title="Permalink to this heading">#</a></h2>
-<p>Model used to generate the following figures: LogReg trained on MC-CH-IN fold 0 for 100 epochs.</p>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>Use the <code class="docutils literal notranslate"><span class="pre">--relevance-analysis</span></code> argument of the <a class="reference internal" href="../cli.html#ptbench-cli"><span class="std std-ref">predict command</span></a> to generate the following plots.</p>
-</div>
-<ul class="simple">
-<li><p>Green color: likely TB</p></li>
-<li><p>Orange color: Could be TB</p></li>
-<li><p>Dark red color: Unlikely TB</p></li>
-</ul>
-<p>As CH is the largest dataset, its relevance analysis is computed on more images
-and is supposed to be more stable. Similarly, train sets are larger.
-We notice the systematic importance of Nodule, Pleural Thickening, Fibrosis,
-Mass, Consolidation and Pleural Effusion.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><figure class="align-center" id="id16">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_mc_train.jpg"><img alt="Relevance analysis on train MC" src="../_images/logreg_mc_ch_in_f0_100_mc_train.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 16 </span><span class="caption-text">Relevance analysis on train MC</span><a class="headerlink" href="#id16" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id17">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_mc_validation.jpg"><img alt="Relevance analysis on validation MC" src="../_images/logreg_mc_ch_in_f0_100_mc_validation.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 17 </span><span class="caption-text">Relevance analysis on validation MC</span><a class="headerlink" href="#id17" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id18">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_mc_test.jpg"><img alt="Relevance analysis on test MC" src="../_images/logreg_mc_ch_in_f0_100_mc_test.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 18 </span><span class="caption-text">Relevance analysis on test MC</span><a class="headerlink" href="#id18" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-<tr class="row-even"><td><figure class="align-center" id="id19">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_ch_train.jpg"><img alt="Relevance analysis on train CH" src="../_images/logreg_mc_ch_in_f0_100_ch_train.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 19 </span><span class="caption-text">Relevance analysis on train CH</span><a class="headerlink" href="#id19" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id20">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_ch_validation.jpg"><img alt="Relevance analysis on validation CH" src="../_images/logreg_mc_ch_in_f0_100_ch_validation.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 20 </span><span class="caption-text">Relevance analysis on validation CH</span><a class="headerlink" href="#id20" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id21">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_ch_test.jpg"><img alt="Relevance analysis on test CH" src="../_images/logreg_mc_ch_in_f0_100_ch_test.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 21 </span><span class="caption-text">Relevance analysis on test CH</span><a class="headerlink" href="#id21" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-<tr class="row-odd"><td><figure class="align-center" id="id22">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_in_train.jpg"><img alt="Relevance analysis on train IN" src="../_images/logreg_mc_ch_in_f0_100_in_train.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 22 </span><span class="caption-text">Relevance analysis on train IN</span><a class="headerlink" href="#id22" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id23">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_in_validation.jpg"><img alt="Relevance analysis on validation IN" src="../_images/logreg_mc_ch_in_f0_100_in_validation.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 23 </span><span class="caption-text">Relevance analysis on validation IN</span><a class="headerlink" href="#id23" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-<td><figure class="align-center" id="id24">
-<a class="reference internal image-reference" href="../_images/logreg_mc_ch_in_f0_100_in_test.jpg"><img alt="Relevance analysis on test IN" src="../_images/logreg_mc_ch_in_f0_100_in_test.jpg" style="width: 390.0px; height: 390.0px;" /></a>
-<figcaption>
-<p><span class="caption-number">Fig. 24 </span><span class="caption-text">Relevance analysis on test IN</span><a class="headerlink" href="#id24" title="Permalink to this image">#</a></p>
-</figcaption>
-</figure>
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="ablation-study">
-<h2>Ablation study<a class="headerlink" href="#ablation-study" title="Permalink to this heading">#</a></h2>
-<p>Here, we removed the data of each sign, one after the other, from the dataset
-for both model training and prediction. LogReg trained on MC-CH-IN fold 0 for
-100 epochs has been used to generate the following plot.</p>
-<p>Predictive capabilities of our logistic regression model after removing the
-data for each radiological sign (d0-d13 correspond, in this order, to
-cardiomegaly, emphysema, effusion, hernia, infiltration, mass, nodule,
-atelectasis, pneumothorax, pleural thickening, pneumonia, fibrosis, edema, and
-consolidation).</p>
-<ul>
-<li><figure class="align-default">
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- <section id="densenet-model-optimization">
-<span id="ptbench-results-optimization-densenet"></span><h1>Densenet model optimization<a class="headerlink" href="#densenet-model-optimization" title="Permalink to this heading">#</a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The Densenet121 model contains 7’216’513 parameters.</p>
-</div>
-<section id="training-on-tb-datasets-from-scratch">
-<h2>Training on TB datasets from scratch<a class="headerlink" href="#training-on-tb-datasets-from-scratch" title="Permalink to this heading">#</a></h2>
-<p>To select the optimal learning rate and batch size for the training on the
-TB datasets from scratch (densenet not pretrained),
-we did a grid search with the following parameters.</p>
-<ul class="simple">
-<li><p>learning rate of 1e-4, 5e-5 and 1e-5</p></li>
-<li><p>batch size of 4 and 8</p></li>
-</ul>
-<p>We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.</p>
-<p><strong>The minimum validation loss we found is 0.3168 by using a learning rate of
-5e-5 and a batch size of 8</strong>.</p>
-<section id="minimum-validation-loss-grid-search">
-<h3>Minimum validation loss grid search<a class="headerlink" href="#minimum-validation-loss-grid-search" title="Permalink to this heading">#</a></h3>
-<p>This table indicates the minimum validation loss obtained for each combination
-of learning rate and batch size.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Learning rate</p></td>
-<td><p>Batch size of 4</p></td>
-<td><p>Batch size of 8</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-4 (training for 600 epochs)</p></td>
-<td><p>0.3658</p></td>
-<td><p>0.3676</p></td>
-</tr>
-<tr class="row-odd"><td><p>5e-5 (training for 150 epochs)</p></td>
-<td><p>0.3490</p></td>
-<td><p><strong>0.3168</strong></p></td>
-</tr>
-<tr class="row-even"><td><p>1e-5 (training for 1000 epochs)</p></td>
-<td><p>0.3791</p></td>
-<td><p>0.3831</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="thresholds-selection">
-<h3>Thresholds selection<a class="headerlink" href="#thresholds-selection" title="Permalink to this heading">#</a></h3>
-<p>The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.</p>
-<ul class="simple">
-<li><p>Threshold for Densenet trained on MC: 0.599</p></li>
-<li><p>Threshold for Densenet trained on MC-CH: 0.519</p></li>
-<li><p>Threshold for Densenet trained on MC-CH-IN: 0.472</p></li>
-</ul>
-</section>
-</section>
-<section id="pre-training-on-nih-cxr14">
-<h2>Pre-training on NIH CXR14<a class="headerlink" href="#pre-training-on-nih-cxr14" title="Permalink to this heading">#</a></h2>
-<p>We used the pretrained Densenet121 model provided by PyTorch. For the
-pretraining on the NIH CXR14 dataset, the hyperparameters from the CheXNeXt
-study were used: batch size of 8, learning rate 1e-4 and the default Adam
-optimizer parameters: beta_1=0.9, beta_2=0.999, epsilon = 1e-8.</p>
-</section>
-<section id="fine-tuning-on-tb-datasets">
-<h2>Fine-tuning on TB datasets<a class="headerlink" href="#fine-tuning-on-tb-datasets" title="Permalink to this heading">#</a></h2>
-<p>To select the optimal learning rate and batch size for the fine-tuning (after
-the pre-training on NIH CXR14), we did a grid search with the following
-parameters.</p>
-<ul class="simple">
-<li><p>learning rate of 1e-4, 1e-5, 5e-6, 1e-6</p></li>
-<li><p>batch size of 4, 8 and 16</p></li>
-</ul>
-<p>We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.</p>
-<p><strong>The minimum validation loss we found is 0.1511 by using a learning rate of
-1e-4 and a batch size of 8</strong>.</p>
-<section id="id1">
-<h3>Minimum validation loss grid search<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
-<p>This table indicates the minimum validation loss obtained for each combination
-of learning rate and batch size.</p>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Learning rate</p></td>
-<td><p>Batch size of 4</p></td>
-<td><p>Batch size of 8</p></td>
-<td><p>Batch size of 16</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-4 (training for 300 epochs)</p></td>
-<td><p>0.2053</p></td>
-<td><p><strong>0.1511</strong></p></td>
-<td><p>0.2372</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-5 (training for 500 epochs)</p></td>
-<td><p>0.1832</p></td>
-<td><p>0.1931</p></td>
-<td><p>0.2326</p></td>
-</tr>
-<tr class="row-even"><td><p>5e-6 (training for 300 epochs)</p></td>
-<td><p>0.1932</p></td>
-<td><p>0.2234</p></td>
-<td><p>0.2298</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-6 (training for 600 epochs)</p></td>
-<td><p>0.2086</p></td>
-<td><p>0.2139</p></td>
-<td><p>0.2138</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="id2">
-<h3>Thresholds selection<a class="headerlink" href="#id2" title="Permalink to this heading">#</a></h3>
-<p>The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.</p>
-<ul class="simple">
-<li><p>Threshold for Densenet trained on MC: 0.688</p></li>
-<li><p>Threshold for Densenet trained on MC-CH: 0.386</p></li>
-<li><p>Threshold for Densenet trained on MC-CH-IN: 0.432</p></li>
-</ul>
-</section>
-<section id="other-hyperparameters">
-<h3>Other hyperparameters<a class="headerlink" href="#other-hyperparameters" title="Permalink to this heading">#</a></h3>
-<p>The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.</p>
-</section>
-</section>
-</section>
-
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-<li><a class="reference internal" href="#minimum-validation-loss-grid-search">Minimum validation loss grid search</a></li>
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-<li><a class="reference internal" href="#fine-tuning-on-tb-datasets">Fine-tuning on TB datasets</a><ul>
-<li><a class="reference internal" href="#id1">Minimum validation loss grid search</a></li>
-<li><a class="reference internal" href="#id2">Thresholds selection</a></li>
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- <section id="logreg-model-optimization">
-<span id="ptbench-results-optimization-logreg"></span><h1>LogReg model optimization<a class="headerlink" href="#logreg-model-optimization" title="Permalink to this heading">#</a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The Logistic Regression model contains 15 parameters.</p>
-</div>
-<p>LogReg is a logistic regression model created to predict TB presence based on
-the fourteen radiological signs predicted by the DensenetRS model. To train
-this model, we created new features for the Montgomery, Shenzhen and Indian
-dataset by predicting the presence of radiological signs on each of them with
-DensenetRS. Those new datasets versions can be identified by the _RS
-(for Radiological Signs) in their name.</p>
-<p>To select the optimal learning rate and the optimal number of neurons for the
-LogReg model, we did a grid search with the following parameters.</p>
-<ul class="simple">
-<li><p>learning rate from 1e-1 to 1e-4</p></li>
-<li><p>batch size of 4, 8 and 16</p></li>
-</ul>
-<p>We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.</p>
-<p><strong>The minimum validation loss we found is 0.3835 by using a learning rate of
-1e-2 and a batch size of 4</strong>.</p>
-<section id="minimum-validation-loss-grid-search">
-<h2>Minimum validation loss grid search<a class="headerlink" href="#minimum-validation-loss-grid-search" title="Permalink to this heading">#</a></h2>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Learning rate</p></td>
-<td><p>Batch size of 4</p></td>
-<td><p>Batch size of 8</p></td>
-<td><p>Batch size of 16</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-1 (training for 50 epochs)</p></td>
-<td><p>0.3932</p></td>
-<td><p>0.4013</p></td>
-<td><p>0.4229</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-2 (training for 100 epochs)</p></td>
-<td><p><strong>0.3835</strong></p></td>
-<td><p>0.3998</p></td>
-<td><p>0.4126</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-3 (training for 200 epochs)</p></td>
-<td><p>0.3875</p></td>
-<td><p>0.4075</p></td>
-<td><p>0.4188</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-4 (training for 800 epochs)</p></td>
-<td><p>0.3942</p></td>
-<td><p>0.4059</p></td>
-<td><p>0.4123</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="thresholds-selection">
-<h2>Thresholds selection<a class="headerlink" href="#thresholds-selection" title="Permalink to this heading">#</a></h2>
-<p>The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.</p>
-<ul class="simple">
-<li><p>Threshold for LogReg trained on MC: 0.568</p></li>
-<li><p>Threshold for LogReg trained on MC-CH: 0.372</p></li>
-<li><p>Threshold for LogReg trained on MC-CH-IN: 0.430</p></li>
-</ul>
-<section id="other-hyperparameters">
-<h3>Other hyperparameters<a class="headerlink" href="#other-hyperparameters" title="Permalink to this heading">#</a></h3>
-<p>The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.</p>
-</section>
-</section>
-</section>
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-<li><a class="reference internal" href="#">LogReg model optimization</a><ul>
-<li><a class="reference internal" href="#minimum-validation-loss-grid-search">Minimum validation loss grid search</a></li>
-<li><a class="reference internal" href="#thresholds-selection">Thresholds selection</a><ul>
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-<span id="ptbench-results-optimization-pasa"></span><h1>Pasa model optimization<a class="headerlink" href="#pasa-model-optimization" title="Permalink to this heading">#</a></h1>
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-<p class="admonition-title">Note</p>
-<p>The Pasa model contains 201’905 parameters.</p>
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-<p>Model hyperparameters from the original study were used: batch size of 4,
-learning rate 8e-5 and the default Adam optimizer parameters: beta_1=0.9,
-beta_2=0.999, epsilon = 1e-8. The Pasa model has not been pretrained.</p>
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-<h2>Thresholds selection<a class="headerlink" href="#thresholds-selection" title="Permalink to this heading">#</a></h2>
-<p>The threshold was systematically selected on the validation set of the datasets
-on which the model was trained.</p>
-<ul class="simple">
-<li><p>Threshold for Pasa trained on MC: 0.577</p></li>
-<li><p>Threshold for Pasa trained on MC-CH: 0.417</p></li>
-<li><p>Threshold for Pasa trained on MC-CH-IN: 0.235</p></li>
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- <section id="signstotb-model-optimization">
-<span id="ptbench-results-optimization-signstotb"></span><h1>SignsToTB model optimization<a class="headerlink" href="#signstotb-model-optimization" title="Permalink to this heading">#</a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The SignsToTB model contains 161 parameters.</p>
-</div>
-<p>SignsToTB is a shallow model created to predict TB presence based on the
-fourteen radiological signs predicted by the DensenetRS model. To train this
-model, we created new features for the Montgomery, Shenzhen and Indian dataset
-by predicting the presence of radiological signs on each of them with
-DensenetRS. Those new datasets versions can be identified by the _RS (for
-Radiological Signs) in their name.</p>
-<p>To select the optimal learning rate and the optimal number of neurons for the
-SignsToTB model, we did a grid search with the following parameters.</p>
-<ul class="simple">
-<li><p>2, 5, 10 and 14 neurons</p></li>
-<li><p>learning rate of 1e-2, 1e-3, 1e-4 and 1e-5</p></li>
-<li><p>batch size of 4</p></li>
-<li><p>1’000 epochs</p></li>
-</ul>
-<p>We systematically used the training set of the combined dataset MC-CH-IN for
-this optimization.</p>
-<p><strong>The minimum validation loss we found is 0.307 by using a learning rate of
-1e-2 and 10 neurons.</strong></p>
-<section id="minimum-validation-loss-grid-search">
-<h2>Minimum validation loss grid search<a class="headerlink" href="#minimum-validation-loss-grid-search" title="Permalink to this heading">#</a></h2>
-<div class="table-wrapper docutils container">
-<table class="docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p>Learning rate</p></td>
-<td><p>2 neurons</p></td>
-<td><p>5 neurons</p></td>
-<td><p>10 neurons</p></td>
-<td><p>14 neurons</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-2</p></td>
-<td><p>0.310</p></td>
-<td><p>0.314</p></td>
-<td><p><strong>0.307</strong></p></td>
-<td><p>0.317</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-3</p></td>
-<td><p>0.336</p></td>
-<td><p>0.315</p></td>
-<td><p>0.314</p></td>
-<td><p>0.317</p></td>
-</tr>
-<tr class="row-even"><td><p>1e-4</p></td>
-<td><p>0.341</p></td>
-<td><p>0.309</p></td>
-<td><p>0.321</p></td>
-<td><p>0.313</p></td>
-</tr>
-<tr class="row-odd"><td><p>1e-5</p></td>
-<td><p>0.326</p></td>
-<td><p>0.357</p></td>
-<td><p>0.337</p></td>
-<td><p>0.323</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<section id="other-hyperparameters">
-<h3>Other hyperparameters<a class="headerlink" href="#other-hyperparameters" title="Permalink to this heading">#</a></h3>
-<p>The default Adam optimizer parameters were used: beta_1=0.9, beta_2=0.999,
-epsilon = 1e-8.</p>
-</section>
-</section>
-</section>
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- <section id="models-training-runtime-and-memory-footprint">
-<span id="ptbench-results-runtime"></span><h1>Models training runtime and memory footprint<a class="headerlink" href="#models-training-runtime-and-memory-footprint" title="Permalink to this heading">#</a></h1>
-<p>The Pasa and the Densenet models were trained on a machine equipped with an 11
-GB GeForce GTX 1080 Ti GPU, an 8-core processor, 48 GB of RAM and Debian 10.
-The Logistic Regression model was trained on a Macbook Pro with an 8-core
-processor, 32 GB of RAM and macOS Big Sur.</p>
-<section id="pasa">
-<h2>Pasa<a class="headerlink" href="#pasa" title="Permalink to this heading">#</a></h2>
-<ul class="simple">
-<li><p>Training on MC: 2’000 epochs in 2.5 hours, ~2 GB of CPU memory, ~0.75 GB of
-GPU memory</p></li>
-<li><p>Training on MC-CH: 2’000 epochs in 17 hours, ~2 GB of CPU memory, ~0.75 GB of
-GPU memory</p></li>
-<li><p>Training on MC-CH-IN: 2’000 epochs in 16.5 hours, ~2 GB of CPU memory, ~0.75
-GB of GPU memory</p></li>
-</ul>
-</section>
-<section id="densenet-pretraining">
-<h2>Densenet pretraining<a class="headerlink" href="#densenet-pretraining" title="Permalink to this heading">#</a></h2>
-<ul class="simple">
-<li><p>Training on NIH CXR14: 10 epochs in 12 hours, ~7.2 GB of CPU memory, ~6.4 GB
-of GPU memory</p></li>
-</ul>
-</section>
-<section id="densenet-fine-tuning">
-<h2>Densenet fine-tuning<a class="headerlink" href="#densenet-fine-tuning" title="Permalink to this heading">#</a></h2>
-<ul class="simple">
-<li><p>Training on MC: 300 epochs in 0.5 hours, ~2 GB of CPU memory, ~6.4 GB of GPU
-memory</p></li>
-<li><p>Training on MC-CH: 300 epochs in 2.5 hours, ~2 GB of CPU memory, ~6.4 GB of
-GPU memory</p></li>
-<li><p>Training on MC-CH-IN: 300 epochs in 3.5 hours, ~2 GB of CPU memory, ~6.4 GB
-of GPU memory</p></li>
-</ul>
-</section>
-<section id="logistic-regression">
-<h2>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this heading">#</a></h2>
-<ul class="simple">
-<li><p>Training on MC: 100 epochs in a few seconds, ~17 GB of CPU memory</p></li>
-<li><p>Training on MC-CH: 100 epochs in a few seconds, ~17 GB of CPU memory</p></li>
-<li><p>Training on MC-CH-IN: 100 epochs in a few seconds, ~17 GB of CPU memory</p></li>
-</ul>
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- <section id="inference-and-evaluation">
-<span id="ptbench-usage-evaluation"></span><h1>Inference and Evaluation<a class="headerlink" href="#inference-and-evaluation" title="Permalink to this heading">#</a></h1>
-<p>This guides explains how to run inference or a complete evaluation using
-command-line tools. Inference produces probability of TB presence for input
-images, while evaluation will analyze such output against existing annotations
-and produce performance figures.</p>
-<section id="inference">
-<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this heading">#</a></h2>
-<p>In inference (or prediction) mode, we input data, the trained model, and output
-a CSV file containing the prediction outputs for every input image.</p>
-<p>To run inference, use the sub-command <a class="reference internal" href="../cli.html#ptbench-cli"><span class="std std-ref">predict</span></a> to run
-prediction on an existing dataset:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>predict<span class="w"> </span>-vv<span class="w"> </span><model><span class="w"> </span>-w<span class="w"> </span><path/to/model.pth><span class="w"> </span><dataset>
-</pre></div>
-</div>
-<p>Replace <code class="docutils literal notranslate"><span class="pre"><model></span></code> and <code class="docutils literal notranslate"><span class="pre"><dataset></span></code> by the appropriate <a class="reference internal" href="../config.html#ptbench-config"><span class="std std-ref">configuration
-files</span></a>. Replace <code class="docutils literal notranslate"><span class="pre"><path/to/model.pth></span></code> to a path leading to
-the pre-trained model.</p>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>An option to generate grad-CAMs is available for the <a class="reference internal" href="../api/configs/models/ptbench.configs.models_datasets.densenet_rs.html#module-ptbench.configs.models_datasets.densenet_rs" title="ptbench.configs.models_datasets.densenet_rs"><code class="xref py py-mod docutils literal notranslate"><span class="pre">DensenetRS</span></code></a> model. To activate it, use
-the <code class="docutils literal notranslate"><span class="pre">--grad-cams</span></code> argument.</p>
-</div>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>An option to generate a relevance analysis plot is available. To activate
-it, use the <code class="docutils literal notranslate"><span class="pre">--relevance-analysis</span></code> argument.</p>
-</div>
-</section>
-<section id="evaluation">
-<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2>
-<p>In evaluation, we input a dataset and predictions to generate performance
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-<code class="docutils literal notranslate"><span class="pre">--weight</span></code> argument.</p>
-<p>Use <code class="docutils literal notranslate"><span class="pre">ptbench</span> <span class="pre">evaluate</span> <span class="pre">--help</span></code> for more information.</p>
-<p>E.g. run evaluation on predictions from the Montgomery set, do the following:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>evaluate<span class="w"> </span>-vv<span class="w"> </span>montgomery<span class="w"> </span>-p<span class="w"> </span>/predictions/folder<span class="w"> </span>-o<span class="w"> </span>/eval/results/folder
-</pre></div>
-</div>
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-<section id="comparing-systems">
-<h2>Comparing Systems<a class="headerlink" href="#comparing-systems" title="Permalink to this heading">#</a></h2>
-<p>To compare multiple systems together and generate combined plots and tables,
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-guide.</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>compare<span class="w"> </span>-vv<span class="w"> </span>A<span class="w"> </span>A/metrics.csv<span class="w"> </span>B<span class="w"> </span>B/metrics.csv<span class="w"> </span>--output-figure<span class="o">=</span>plot.pdf<span class="w"> </span>--output-table<span class="o">=</span>table.txt<span class="w"> </span>--threshold<span class="o">=</span><span class="m">0</span>.5
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-<li class="toctree-l1"><a class="reference internal" href="aggregpred.html">Aggregate multiple prediction files into a single one</a></li>
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- <section id="training">
-<span id="ptbench-usage-training"></span><h1>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h1>
-<section id="convolutional-neural-network-cnn">
-<h2>Convolutional Neural Network (CNN)<a class="headerlink" href="#convolutional-neural-network-cnn" title="Permalink to this heading">#</a></h2>
-<p>To train a new CNN, use the command-line interface (CLI) application <code class="docutils literal notranslate"><span class="pre">ptbench</span>
-<span class="pre">train</span></code>, available on your prompt. To use this CLI, you must define the input
-dataset that will be used to train the CNN, as well as the type of model that
-will be trained. You may issue <code class="docutils literal notranslate"><span class="pre">ptbench</span> <span class="pre">train</span> <span class="pre">--help</span></code> for a help message
-containing more detailed instructions.</p>
-<div class="admonition tip">
-<p class="admonition-title">Tip</p>
-<p>We strongly advice training with a GPU (using <code class="docutils literal notranslate"><span class="pre">--device="cuda:0"</span></code>).
-Depending on the available GPU memory you might have to adjust your batch
-size (<code class="docutils literal notranslate"><span class="pre">--batch</span></code>).</p>
-</div>
-<section id="examples">
-<h3>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">#</a></h3>
-<p>To train Pasa CNN on the Montgomery dataset:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span>-vv<span class="w"> </span>pasa<span class="w"> </span>montgomery<span class="w"> </span>--batch-size<span class="o">=</span><span class="m">4</span><span class="w"> </span>--epochs<span class="o">=</span><span class="m">150</span>
-</pre></div>
-</div>
-<p>To train DensenetRS CNN on the NIH CXR14 dataset:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span>-vv<span class="w"> </span>nih_cxr14<span class="w"> </span>densenet_rs<span class="w"> </span>--batch-size<span class="o">=</span><span class="m">8</span><span class="w"> </span>--epochs<span class="o">=</span><span class="m">10</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="logistic-regressor-or-shallow-network">
-<h2>Logistic regressor or shallow network<a class="headerlink" href="#logistic-regressor-or-shallow-network" title="Permalink to this heading">#</a></h2>
-<p>To train a logistic regressor or a shallow network, use the command-line
-interface (CLI) application <code class="docutils literal notranslate"><span class="pre">ptbench</span> <span class="pre">train</span></code>, available on your prompt. To use
-this CLI, you must define the input dataset that will be used to train the
-model, as well as the type of model that will be trained.
-You may issue <code class="docutils literal notranslate"><span class="pre">ptbench</span> <span class="pre">train</span> <span class="pre">--help</span></code> for a help message containing more
-detailed instructions.</p>
-<section id="id1">
-<h3>Examples<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
-<p>To train a logistic regressor using predictions from DensenetForRS on the
-Montgomery dataset:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span>-vv<span class="w"> </span>logistic_regression<span class="w"> </span>montgomery_rs<span class="w"> </span>--batch-size<span class="o">=</span><span class="m">4</span><span class="w"> </span>--epochs<span class="o">=</span><span class="m">20</span>
-</pre></div>
-</div>
-<p>To train Signs_to_TB using predictions from DensenetForRS on the Shenzhen
-dataset:</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>ptbench<span class="w"> </span>train<span class="w"> </span>-vv<span class="w"> </span>signs_to_tb<span class="w"> </span>shenzhen_rs<span class="w"> </span>--batch-size<span class="o">=</span><span class="m">4</span><span class="w"> </span>--epochs<span class="o">=</span><span class="m">20</span>
-</pre></div>
-</div>
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-<li><a class="reference internal" href="#">Training</a><ul>
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