[doc] Add segmentation documentation

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

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
 

doc/conf.py

@@ -117,6 +117,7 @@ autodoc_default_options = {
 auto_intersphinx_packages = [
     "matplotlib",
     "numpy",
+    "pandas",
     "pillow",
     "psutil",
     "scipy",

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

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

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.

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.

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

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

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
 ========

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:
 

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

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}"""

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.
     """
 

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.
     """