diff --git a/doc/api.rst b/doc/api.rst
index d4e441f00698cd6bbb8bbf42c233f0b3048f8c46..84df54ed4c6881f34eb37c5d29dc30fe4179d4a5 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -12,68 +12,135 @@ This section includes information for using the Python API of
``mednet``.
-.. _mednet.libs.classification.api.data:
+Common library
+--------------
+
+This common library contains methods and scripts that can be reused by more specialized libraries.
+
+.. _mednet.libs.common.api.data:
Data Methods
-------------
+^^^^^^^^^^^^
Auxiliary classes and methods to define raw dataset iterators.
.. autosummary::
:toctree: api/data
- mednet.libs.classification.data.augmentations
- mednet.libs.classification.data.datamodule
- mednet.libs.classification.data.image_utils
- mednet.libs.classification.data.split
- mednet.libs.classification.data.typing
+ mednet.libs.common.data.augmentations
+ mednet.libs.common.data.datamodule
+ mednet.libs.common.data.image_utils
+ mednet.libs.common.data.split
+ mednet.libs.common.data.typing
-.. _mednet.libs.classification.api.models:
+.. _mednet.libs.common.api.engines:
+
+Command engines
+^^^^^^^^^^^^^^^
+
+Functions to actuate on the data.
+
+.. autosummary::
+ :toctree: api/engine
+
+ mednet.libs.common.engine.callbacks
+ mednet.libs.common.engine.device
+ mednet.libs.common.engine.loggers
+ mednet.libs.common.engine.trainer
+
+
+.. _mednet.libs.common.api.models:
Models
-------
+^^^^^^
-CNN and other models implemented.
+Common model utilities.
.. autosummary::
:toctree: api/models
- mednet.libs.classification.models.pasa
- mednet.libs.classification.models.alexnet
- mednet.libs.classification.models.densenet
- mednet.libs.classification.models.logistic_regression
- mednet.libs.classification.models.loss_weights
- mednet.libs.classification.models.mlp
- mednet.libs.classification.models.model
- mednet.libs.classification.models.normalizer
- mednet.libs.classification.models.separate
- mednet.libs.classification.models.transforms
- mednet.libs.classification.models.typing
+ mednet.libs.common.models.loss_weights
+ mednet.libs.common.models.model
+ mednet.libs.common.models.normalizer
+ mednet.libs.common.models.typing
+
+
+.. _mednet.libs.common.api.utils:
+
+Utils
+^^^^^
+
+Reusable auxiliary functions.
+
+.. autosummary::
+ :toctree: api/utils
+
+ mednet.libs.common.utils.checkpointer
+ mednet.libs.common.utils.gitlab
+ mednet.libs.common.utils.resources
+ mednet.libs.common.utils.summary
+ mednet.libs.common.utils.tensorboard
+
+
+Classification library
+----------------------
+
+Library for training models on classification tasks
+
+
+.. _mednet.libs.classification.api.data:
+
+Data
+^^^^
+
+Classification-specific data methods
+
+.. autosummary::
+ :toctree: api/data
+
+ mednet.libs.classification.data.typing
.. _mednet.libs.classification.api.engines:
Command engines
----------------
+^^^^^^^^^^^^^^^
Functions to actuate on the data.
.. autosummary::
:toctree: api/engine
- mednet.libs.classification.engine.callbacks
- mednet.libs.classification.engine.device
mednet.libs.classification.engine.evaluator
- mednet.libs.classification.engine.loggers
mednet.libs.classification.engine.predictor
- mednet.libs.classification.engine.trainer
+
+
+.. _mednet.libs.classification.api.models:
+
+Models
+^^^^^^
+
+CNN and other models implemented.
+
+.. autosummary::
+ :toctree: api/models
+
+ mednet.libs.classification.models.pasa
+ mednet.libs.classification.models.alexnet
+ mednet.libs.classification.models.densenet
+ mednet.libs.classification.models.loss_weights
+ mednet.libs.classification.models.logistic_regression
+ mednet.libs.classification.models.mlp
+ mednet.libs.classification.models.separate
+ mednet.libs.classification.models.transforms
+ mednet.libs.classification.models.typing
.. _mednet.libs.classification.api.saliency:
Saliency Map Generation and Analysis
-------------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Engines to generate and analyze saliency mapping techniques.
@@ -89,19 +156,79 @@ Engines to generate and analyze saliency mapping techniques.
.. _mednet.libs.classification.api.utils:
-Various utilities
------------------
+Utils
+^^^^^
-Reusable auxiliary functions.
+Classification-specific utilities.
.. autosummary::
:toctree: api/utils
- mednet.utils.checkpointer
- mednet.utils.gitlab
- mednet.utils.rc
- mednet.libs.common.utils.resources
- mednet.utils.tensorboard
+ mednet.libs.segmentation.utils.rc
+
+
+Segmentation library
+--------------------
+
+Library for training models on segmentation tasks
+
+
+.. _mednet.libs.segmentation.api.data:
+
+Data
+^^^^
+
+Segmentation-specific data methods
+
+.. autosummary::
+ :toctree: api/data
+
+ mednet.libs.segmentation.data.typing
+
+
+.. _mednet.libs.segmentation.api.engines:
+
+Command engines
+^^^^^^^^^^^^^^^
+
+Functions to actuate on the data.
+
+.. autosummary::
+ :toctree: api/engine
+
+ mednet.libs.segmentation.engine.evaluator
+
+
+.. _mednet.libs.segmentation.api.models:
+
+Models
+^^^^^^
+
+CNN and other models implemented.
+
+.. autosummary::
+ :toctree: api/models
+
+ mednet.libs.segmentation.models.losses
+ mednet.libs.segmentation.models.lwnet
+ mednet.libs.segmentation.models.separate
+ mednet.libs.segmentation.models.typing
+
+
+.. _mednet.libs.segmentation.api.utils:
+
+Utils
+^^^^^
+
+Segmentation-specific utilities.
+
+.. autosummary::
+ :toctree: api/utils
+
+ mednet.libs.segmentation.utils.measure
+ mednet.libs.segmentation.utils.plot
+ mednet.libs.segmentation.utils.rc
+ mednet.libs.segmentation.utils.table
.. include:: links.rst
diff --git a/doc/cli.rst b/doc/cli.rst
index c9f96653b97feec52dd3e683e41a476ccc3c3119..fd01e8a7a92859ec4757a293a02910c3b5268042 100644
--- a/doc/cli.rst
+++ b/doc/cli.rst
@@ -2,7 +2,7 @@
..
.. SPDX-License-Identifier: GPL-3.0-or-later
-.. _mednet.libs.classification.cli:
+.. _mednet.libs.common.cli:
========================
Command-line Interface
@@ -12,7 +12,7 @@ This section contains an overview of command-line applications shipped with
this package.
-.. click:: mednet.libs.classification.scripts.cli:cli
+.. click:: mednet.scripts.cli:cli
:prog: mednet
:nested: full
diff --git a/doc/conf.py b/doc/conf.py
index 1191dc74d9681e1ac13003c09032c07ab726bd16..6b179cc3a834e6ff3216cab0362578b17280f177 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -117,6 +117,7 @@ autodoc_default_options = {
auto_intersphinx_packages = [
"matplotlib",
"numpy",
+ "pandas",
"pillow",
"psutil",
"scipy",
diff --git a/doc/references.rst b/doc/references.rst
index f70d92d25853be6cc98de4c15813241b99f12d3e..6d5189cbb276963b532e7969f121804125d8dee4 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -81,3 +81,28 @@
**A Consistent and Efficient Evaluation Strategy for Attribution Methods** in
Proceedings of the 39th International Conference on Machine Learning, PMLR,
Jun. 2022, pp. 18770–18795. https://proceedings.mlr.press/v162/rong22a.html
+
+.. [IGLOVIKOV-2018] *V. Iglovikov, S. Seferbekov, A. Buslaev and A. Shvets*,
+ **TernausNetV2: Fully Convolutional Network for Instance Segmentation**,
+ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
+ Workshops (CVPRW), Salt Lake City, UT, 2018, pp. 228-2284.
+ https://doi.org/10.1109/CVPRW.2018.00042
+
+.. [XIE-2015] *S. Xie and Z. Tu*, **Holistically-Nested Edge Detection**, 2015
+ IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp.
+ 1395-1403. https://doi.org/10.1109/ICCV.2015.164
+
+.. [MANINIS-2016] *K.-K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool*,
+ **Deep Retinal Image Understanding**, in Medical Image Computing and
+ Computer-Assisted Intervention – MICCAI 2016, Cham, 2016, pp. 140–148.
+ https://doi.org/10.1007/978-3-319-46723-8_17
+
+.. [GALDRAN-2020] *A. Galdran, A. Anjos, J. Dolz, H. Chakor, H. Lombaert, and
+ I. Ben Ayed*, **The Little W-Net That Could: State-of-the-Art Retinal Vessel
+ Segmentation with Minimalistic Models**, 2020.
+ https://arxiv.org/abs/2009.01907
+
+.. [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
diff --git a/doc/results/index.rst b/doc/results/index.rst
index 1fec86c3f6e384c816ed8805532bc2669530b06e..85b646d1b2333ffce3dcc238d4a9d728ec105c0c 100644
--- a/doc/results/index.rst
+++ b/doc/results/index.rst
@@ -59,9 +59,9 @@ 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 <mednet.cli>` to aggregate the
+ fold, then use the :ref:`aggregpred command <mednet.libs.common.cli>` to aggregate the
predictions together, and finally evaluate this new file using the
- :ref:`compare command <mednet.cli>`.
+ :ref:`compare command <mednet.libs.common.cli>`.
Pasa and DenseNet-121 (random initialization)
"""""""""""""""""""""""""""""""""""""""""""""
@@ -113,37 +113,37 @@ Thresholds used:
:scale: 50%
:alt: Testing sets ROC curves for Pasa model trained on normalized-kfold MC
- :py:mod:`Pasa <mednet.config.models.pasa>`: Pasa trained on normalized-kfold MC
+ :py:mod:`Pasa <mednet.libs.classification.config.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 <mednet.config.models.pasa>`: Pasa trained on normalized-kfold MC-CH
+ :py:mod:`Pasa <mednet.libs.classification.config.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 <mednet.config.models.pasa>`: Pasa trained on normalized-kfold MC-CH-IN
+ :py:mod:`Pasa <mednet.libs.classification.config.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 <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN
+ :py:mod:`DenseNet <mednet.libs.classification.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN
DenseNet-121 (pretrained on ImageNet)
"""""""""""""""""""""""""""""""""""""
@@ -180,19 +180,19 @@ Thresholds used:
:scale: 50%
:alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC
- :py:mod:`DenseNet <mednet.config.models.densenet>` DenseNet trained on normalized-kfold MC
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>` DenseNet trained on normalized-kfold MC-CH
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>` DenseNet trained on normalized-kfold MC-CH-IN
+ :py:mod:`DenseNet <mednet.libs.classification.config.models.densenet>` DenseNet trained on normalized-kfold MC-CH-IN
Logistic Regression Classifier
""""""""""""""""""""""""""""""
@@ -229,19 +229,19 @@ Thresholds used:
:scale: 50%
:alt: Testing sets ROC curves for LogReg model trained on normalized-kfold MC
- :py:mod:`LogReg <mednet.config.models.logistic_regression>`: LogReg trained on normalized-kfold MC
+ :py:mod:`LogReg <mednet.libs.classification.config.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 <mednet.config.models.logistic_regression>`: LogReg trained on normalized-kfold MC-CH
+ :py:mod:`LogReg <mednet.libs.classification.config.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 <mednet.config.models.logistic_regression>`: LogReg trained on normalized-kfold MC-CH-IN
+ :py:mod:`LogReg <mednet.libs.classification.config.models.logistic_regression>`: LogReg trained on normalized-kfold MC-CH-IN
DenseNet-121 (pretrained on ImageNet and NIH CXR14)
"""""""""""""""""""""""""""""""""""""""""""""""""""
@@ -278,19 +278,19 @@ Thresholds used:
:scale: 50%
:alt: Testing sets ROC curves for DenseNet model trained on normalized-kfold MC (pretrained on NIH)
- :py:mod:`DenseNet <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC (pretrained on NIH)
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH (pretrained on NIH)
+ :py:mod:`DenseNet <mednet.libs.classification.config.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 <mednet.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN (pretrained on NIH)
+ :py:mod:`DenseNet <mednet.libs.classification.config.models.densenet>`: DenseNet trained on normalized-kfold MC-CH-IN (pretrained on NIH)
Global sensitivity analysis (relevance)
@@ -301,7 +301,7 @@ Model used to generate the following figures: LogReg trained on MC-CH-IN fold 0
.. tip::
Use the ``--relevance-analysis`` argument of the :ref:`predict command
- <mednet.cli>` to generate the following plots.
+ <mednet.libs.common.cli>` to generate the following plots.
* Green color: likely TB
* Orange color: Could be TB
diff --git a/doc/usage/evaluation.rst b/doc/usage/evaluation.rst
index 1288bab2007f377d961a606e835feaef86eb5d45..45519265f27d221f2feed66f527cc24b4204b5a4 100644
--- a/doc/usage/evaluation.rst
+++ b/doc/usage/evaluation.rst
@@ -20,7 +20,7 @@ Inference
In inference (or prediction) mode, we input a model, a dataset, a model checkpoint generated during training, and output
a json file containing the prediction outputs for every input image.
-To run inference, use the sub-command :ref:`predict <mednet.libs.classification.cli>`.
+To run inference, use the sub-command :ref:`predict <mednet.libs.common.cli>`.
Examples
========
@@ -40,7 +40,7 @@ Evaluation
In evaluation, we input predictions to generate performance summaries that help analysis of a trained model.
The generated files are a .pdf containing various plots and a table of metrics for each dataset split.
-Evaluation is done using the :ref:`evaluate command <mednet.libs.classification.cli>` followed by the json file generated during
+Evaluation is done using the :ref:`evaluate command <mednet.libs.common.cli>` followed by the json file generated during
the inference step and a threshold.
Use ``mednet evaluate --help`` for more information.
diff --git a/doc/usage/experiment.rst b/doc/usage/experiment.rst
index 8dc75b9d76e432dd91ca13e94f9585daf7e4e2a9..597178bf6ab1773da236e72950c4e54910641362 100644
--- a/doc/usage/experiment.rst
+++ b/doc/usage/experiment.rst
@@ -8,7 +8,7 @@
Running complete experiments
==============================
-We provide an :ref:`experiment command <mednet.libs.classification.cli>`
+We provide an :ref:`experiment command <mednet.libs.common.cli>`
that runs training, followed by prediction and evaluation.
After running, you will be able to find results from model fitting,
prediction and evaluation under a single output directory.
diff --git a/doc/usage/index.rst b/doc/usage/index.rst
index ce1c5c065c24ad02bbf1fd97b768b2fd74d5032f..4b643aab8827939b059c962ea05324171a38157d 100644
--- a/doc/usage/index.rst
+++ b/doc/usage/index.rst
@@ -51,7 +51,7 @@ Direct detection
* Comparison: Use predictions results to compare performance of multiple
systems.
-We provide :ref:`command-line interfaces (CLI) <mednet.libs.classification.cli>` that implement
+We provide :ref:`command-line interfaces (CLI) <mednet.libs.common.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
diff --git a/doc/usage/saliency.rst b/doc/usage/saliency.rst
index ebd03bf72a2fd2967a992aa6100183c94104e617..178aca289f4a43de29a1363b00dd2683f5809a93 100644
--- a/doc/usage/saliency.rst
+++ b/doc/usage/saliency.rst
@@ -17,7 +17,7 @@ Some of the scripts require the use of a database with human-annotated saliency
Generation
----------
-Saliency maps can be generated with the :ref:`saliency generate command <mednet.libs.classification.cli>`.
+Saliency maps can be generated with the :ref:`saliency generate command <mednet.libs.common.cli>`.
They are represented as numpy arrays of the same size as thes images, with values in the range [0-1] and saved in .npy files.
Several mapping algorithms are available to choose from, which can be specified with the -s option.
@@ -36,7 +36,7 @@ objects on the output directory:
Viewing
-------
-To overlay saliency maps over the original images, use the :ref:`saliency view command <mednet.libs.classification.cli>`.
+To overlay saliency maps over the original images, use the :ref:`saliency view command <mednet.libs.common.cli>`.
Results are saved as PNG images in which brigter pixels correspond to areas with higher saliency.
Examples
diff --git a/doc/usage/training.rst b/doc/usage/training.rst
index a123fce48ffa5be39ca69cf0fa886ef3acdc79c6..380e5ef5233c9d7a02085940b74779cce32cd02b 100644
--- a/doc/usage/training.rst
+++ b/doc/usage/training.rst
@@ -71,7 +71,7 @@ Plotting training metrics
Various metrics are recorded at each epoch during training, such as the execution time, loss and resource usage.
These are saved in a Tensorboard file, located in a `logs` subdirectory of the training output folder.
-Mednet provides a :ref:`train-analysis <mednet.libs.classification.cli>` convenience script that graphs the scalars stored in these files and saves them in a .pdf file.
+Mednet provides a :ref:`train-analysis <mednet.libs.common.cli>` convenience script that graphs the scalars stored in these files and saves them in a .pdf file.
Examples
========
diff --git a/src/mednet/libs/common/scripts/upload.py b/src/mednet/libs/common/scripts/upload.py
index ccca1ce840f68010bbdb93658733a90cdf2b068c..409bcc36fdbf74e13359390cb2d55e845daaf4e0 100644
--- a/src/mednet/libs/common/scripts/upload.py
+++ b/src/mednet/libs/common/scripts/upload.py
@@ -14,7 +14,6 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
@click.command(
- entry_point_group="mednet.config",
cls=ConfigCommand,
epilog="""Examples:
diff --git a/src/mednet/libs/segmentation/models/losses.py b/src/mednet/libs/segmentation/models/losses.py
index 3af10f0dd434132c992197ec310b8e33042ffd3f..797da8371d2874637b715f02a6ad9b60e60b5b23 100644
--- a/src/mednet/libs/segmentation/models/losses.py
+++ b/src/mednet/libs/segmentation/models/losses.py
@@ -5,10 +5,9 @@
"""Loss implementations."""
import torch
-from torch.nn.modules.loss import _Loss
-class WeightedBCELogitsLoss(_Loss):
+class WeightedBCELogitsLoss(torch.nn.Module):
"""Calculates sum of weighted cross entropy loss.
Implements Equation 1 in [MANINIS-2016]_. The weight depends on the
@@ -29,17 +28,14 @@ class WeightedBCELogitsLoss(_Loss):
sample
Value produced by the model to be evaluated, with the shape ``[n, c,
h, w]``.
-
target
Ground-truth information with the shape ``[n, c, h, w]``.
-
mask
Mask to be use for specifying the region of interest where to
compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
- loss
The average loss for all input data.
"""
@@ -57,7 +53,7 @@ class WeightedBCELogitsLoss(_Loss):
)
-class SoftJaccardBCELogitsLoss(_Loss):
+class SoftJaccardBCELogitsLoss(torch.nn.Module):
r"""Implement the generalized loss function of Equation (3) in.
[IGLOVIKOV-2018]_, with J being the Jaccard distance, and H, the Binary
@@ -89,17 +85,14 @@ class SoftJaccardBCELogitsLoss(_Loss):
tensor
Value produced by the model to be evaluated, with the shape ``[n, c,
h, w]``.
-
target
Ground-truth information with the shape ``[n, c, h, w]``.
-
mask
Mask to be use for specifying the region of interest where to
compute the loss, with the shape ``[n, c, h, w]``.
Returns
-------
- loss
Loss, in a single entry.
"""
@@ -135,10 +128,8 @@ class MultiWeightedBCELogitsLoss(WeightedBCELogitsLoss):
tensor
Value produced by the model to be evaluated, with the shape ``[L,
n, c, h, w]``.
-
target
Ground-truth information with the shape ``[n, c, h, w]``.
-
mask
Mask to be use for specifying the region of interest where to
compute the loss, with the shape ``[n, c, h, w]``.
@@ -181,10 +172,8 @@ class MultiSoftJaccardBCELogitsLoss(SoftJaccardBCELogitsLoss):
tensor
Value produced by the model to be evaluated, with the shape ``[L,
n, c, h, w]``.
-
target
Ground-truth information with the shape ``[n, c, h, w]``.
-
mask
Mask to be use for specifying the region of interest where to
compute the loss, with the shape ``[n, c, h, w]``.
@@ -204,80 +193,76 @@ class MultiSoftJaccardBCELogitsLoss(SoftJaccardBCELogitsLoss):
).mean()
-class MixJacLoss(_Loss):
- """
- Parameters
- ----------
- lambda_u
- Determines the weighting of SoftJaccard and BCE.
-
- jacalpha
- Determines the weighting of J and H.
-
- size_average
- By default, the losses are averaged over each loss element in the batch. Note that for
- some losses, there are multiple elements per sample. If the field :attr:`size_average`
- is set to ``False``, the losses are instead summed for each minibatch. Ignored
- when :attr:`reduce` is ``False``. Default: ``True``.
-
- reduce
- By default, the losses are averaged or summed over observations for each minibatch depending
- on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
- batch element instead and ignores :attr:`size_average`. Default: ``True``.
-
- reduction
- Specifies the reduction to apply to the output:
- ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
- ``'mean'``: the sum of the output will be divided by the number of
- elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
- and :attr:`reduce` are in the process of being deprecated, and in the meantime,
- specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``.
- """
-
- def __init__(
- self,
- lambda_u: int = 100,
- jacalpha=0.7,
- size_average=None,
- reduce=None,
- reduction="mean",
- ):
- super().__init__(size_average, reduce, reduction)
- self.lambda_u = lambda_u
- self.labeled_loss = SoftJaccardBCELogitsLoss(alpha=jacalpha)
- self.unlabeled_loss = torch.nn.BCEWithLogitsLoss()
-
- def forward(
- self,
- tensor: torch.Tensor,
- target: torch.Tensor,
- unlabeled_tensor: torch.Tensor,
- unlabeled_target: torch.Tensor,
- ramp_up_factor: float,
- ) -> tuple:
- """Forward pass.
-
- Parameters
- ----------
- tensor
- Value produced by the model to be evaluated, with the shape ``[L,
- n, c, h, w]``.
-
- target
- Ground-truth information with the shape ``[n, c, h, w]``.
-
- unlabeled_tensor
-
- unlabeled_target
-
- ramp_up_factor
-
- Returns
- -------
- list
- """
- ll = self.labeled_loss(tensor, target)
- ul = self.unlabeled_loss(unlabeled_tensor, unlabeled_target)
-
- loss = ll + self.lambda_u * ramp_up_factor * ul
- return loss, ll, ul
+# class MixJacLoss(torch.nn.Module):
+# """Implements Mix Jaccard Loss.
+
+# Parameters
+# ----------
+# lambda_u
+# Determines the weighting of SoftJaccard and BCE.
+# jacalpha
+# Determines the weighting of J and H.
+# size_average
+# By default, the losses are averaged over each loss element in the batch. Note that for
+# some losses, there are multiple elements per sample. If the field `size_average`
+# is set to ``False``, the losses are instead summed for each minibatch. Ignored
+# when `reduce` is ``False``. Default: ``True``.
+# reduce
+# By default, the losses are averaged or summed over observations for each minibatch depending
+# on `size_average`. When `reduce` is ``False``, returns a loss per
+# batch element instead and ignores `size_average`. Default: ``True``.
+# reduction
+# Specifies the reduction to apply to the output:
+# ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
+# ``'mean'``: the sum of the output will be divided by the number of
+# elements in the output, ``'sum'``: the output will be summed. Note: `size_average`
+# and `reduce` are in the process of being deprecated, and in the meantime,
+# specifying either of those two args will override `reduction`. Default: ``'mean'``.
+# """
+
+# def __init__(
+# self,
+# lambda_u: int = 100,
+# jacalpha=0.7,
+# size_average=None,
+# reduce=None,
+# reduction="mean",
+# ):
+# super().__init__(size_average, reduce, reduction)
+# self.lambda_u = lambda_u
+# self.labeled_loss = SoftJaccardBCELogitsLoss(alpha=jacalpha)
+# self.unlabeled_loss = torch.nn.BCEWithLogitsLoss()
+
+# def forward(
+# self,
+# tensor: torch.Tensor,
+# target: torch.Tensor,
+# unlabeled_tensor: torch.Tensor,
+# unlabeled_target: torch.Tensor,
+# ramp_up_factor: float,
+# ) -> tuple:
+# """Forward pass.
+
+# Parameters
+# ----------
+# tensor
+# Value produced by the model to be evaluated, with the shape ``[L,
+# n, c, h, w]``.
+# target
+# Ground-truth information with the shape ``[n, c, h, w]``.
+
+# unlabeled_tensor
+
+# unlabeled_target
+
+# ramp_up_factor
+
+# Returns
+# -------
+# list
+# """
+# ll = self.labeled_loss(tensor, target)
+# ul = self.unlabeled_loss(unlabeled_tensor, unlabeled_target)
+
+# loss = ll + self.lambda_u * ramp_up_factor * ul
+# return loss, ll, ul
diff --git a/src/mednet/libs/segmentation/models/lwnet.py b/src/mednet/libs/segmentation/models/lwnet.py
index 593c2f9675b77ae8872bbe7f6e73827231f0699c..a5cfc9bd711c552dece3a5724b60290dff2603bc 100644
--- a/src/mednet/libs/segmentation/models/lwnet.py
+++ b/src/mednet/libs/segmentation/models/lwnet.py
@@ -34,6 +34,22 @@ def _conv1x1(in_planes, out_planes, stride=1):
class ConvBlock(torch.nn.Module):
+ """Convolution block.
+
+ Parameters
+ ----------
+ in_c
+ Number of input channels.
+ out_c
+ Number of output channels.
+ k_sz
+ Kernel Size.
+ shortcut
+ If True, adds a Conv2d layer.
+ pool
+ If True, adds a MaxPool2d layer.
+ """
+
def __init__(self, in_c, out_c, k_sz=3, shortcut=False, pool=True):
super().__init__()
if shortcut is True:
@@ -74,6 +90,18 @@ class ConvBlock(torch.nn.Module):
class UpsampleBlock(torch.nn.Module):
+ """Upsample block implementation.
+
+ Parameters
+ ----------
+ in_c
+ Number of input channels.
+ out_c
+ Number of output channels.
+ up_mode
+ Upsampling mode.
+ """
+
def __init__(self, in_c, out_c, up_mode="transp_conv"):
super().__init__()
block = []
@@ -98,6 +126,16 @@ class UpsampleBlock(torch.nn.Module):
class ConvBridgeBlock(torch.nn.Module):
+ """ConvBridgeBlock implementation.
+
+ Parameters
+ ----------
+ channels
+ Number of channels.
+ k_sz
+ Kernel Size.
+ """
+
def __init__(self, channels, k_sz=3):
super().__init__()
pad = (k_sz - 1) // 2
@@ -116,6 +154,24 @@ class ConvBridgeBlock(torch.nn.Module):
class UpConvBlock(torch.nn.Module):
+ """UpConvBlock implementation.
+
+ Parameters
+ ----------
+ in_c
+ Number of input channels.
+ out_c
+ Number of output channels.
+ k_sz
+ Kernel Size.
+ up_mode
+ Upsampling mode.
+ conv_bridge
+ If True, adds a ConvBridgeBlock layer.
+ shortcut
+ If True, adds a Conv2d layer.
+ """
+
def __init__(
self,
in_c,
@@ -150,19 +206,19 @@ class LittleUNet(torch.nn.Module):
Parameters
----------
in_c
-
+ Number of input channels.
n_classes
Number of outputs (classes) for this model.
-
layers
-
+ Number of layers of the model.
k_sz
-
+ Kernel Size.
up_mode
-
+ Upsampling mode.
conv_bridge
-
+ If True, adds a ConvBridgeBlock layer.
shortcut
+ If True, adds a Conv2d layer.
"""
def __init__(
@@ -327,16 +383,3 @@ class LittleWNet(Model):
return self._optimizer_type(
self.parameters(), **self._optimizer_arguments
)
-
- """def configure_optimizers(self):
- optimizer = getattr(
- self, 'optimizer', Adam(self.parameters(), lr=1e-3)
- )
- if optimizer is None:
- raise ValueError("Optimizer not found. Please provide an optimizer.")
-
- scheduler = getattr(self, 'scheduler', None)
- if scheduler is None:
- return {'optimizer': optimizer}
- else:
- return {'optimizer': optimizer, 'lr_scheduler': scheduler}"""
diff --git a/src/mednet/libs/segmentation/utils/plot.py b/src/mednet/libs/segmentation/utils/plot.py
index 746a1877cef8c87d3d2ab49535e537c47ccf88c3..1cd19b320b7ce740fef05500d17cbe29f0ee7033 100644
--- a/src/mednet/libs/segmentation/utils/plot.py
+++ b/src/mednet/libs/segmentation/utils/plot.py
@@ -152,7 +152,6 @@ def precision_recall_f1iso(
Returns
-------
- figure
A matplotlib figure you can save or display (uses an ``agg`` backend).
"""
@@ -258,7 +257,6 @@ def loss_curve(df: pandas.DataFrame) -> matplotlib.figure.Figure:
Returns
-------
- figure
A figure, that may be saved or displayed.
"""
diff --git a/src/mednet/libs/segmentation/utils/table.py b/src/mednet/libs/segmentation/utils/table.py
index eaf3ee4e43490cb4e76445d7bd3b59874675a38a..f433835ec04df9461f7dcfdc19d0061bd418b352 100644
--- a/src/mednet/libs/segmentation/utils/table.py
+++ b/src/mednet/libs/segmentation/utils/table.py
@@ -44,7 +44,6 @@ def performance_table(data: dict[str, dict[str, typing.Any]], fmt: str) -> str:
Returns
-------
- table
A table in a specific format.
"""