[segmentation.models] Remove resnet model

ResNet was ununsed and non functional in deepdraw, there is no need to
port it over.

pyproject.toml

@@ -429,7 +429,6 @@ driu-pix = "mednet.libs.segmentation.config.models.driu_pix"
 hed = "mednet.libs.segmentation.config.models.hed"
 lwnet = "mednet.libs.segmentation.config.models.lwnet"
 m2unet = "mednet.libs.segmentation.config.models.m2unet"
-#resunet = "mednet.libs.segmentation.config.models.resunet"
 unet = "mednet.libs.segmentation.config.models.unet"
 
 # chase-db1 - retinography

src/mednet/libs/segmentation/models/backbones/resnet.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-import torchvision.models
-
-try:
-    # pytorch >= 1.12
-    from torch.hub import load_state_dict_from_url
-except ImportError:
-    # pytorch < 1.12
-    from torchvision.models.utils import load_state_dict_from_url
-
-
-class ResNet4Segmentation(torchvision.models.resnet.ResNet):
-    """Adaptation of base ResNet functionality to U-Net style segmentation.
-
-    This version of ResNet is slightly modified so it can be used through
-    torchvision's API.  It outputs intermediate features which are normally not
-    output by the base ResNet implementation, but are required for segmentation
-    operations.
-
-    Parameters
-    ----------
-    *args
-        Arguments to be passed to the parent ResNet model.
-    **kwargs
-        Keyword arguments to be passed to the parent ResNet model.
-        return_features : :py:class:`list`, Optional
-            A list of integers indicating the feature layers to be returned from
-            the original module.
-    """
-
-    def __init__(self, *args, **kwargs):
-        self._return_features = kwargs.pop("return_features")
-        super().__init__(*args, **kwargs)
-
-    def forward(self, x):
-        outputs = []
-        # hardwiring of input
-        outputs.append(x.shape[2:4])
-        for index, m in enumerate(self.features):
-            x = m(x)
-            # extract layers
-            if index in self.return_features:
-                outputs.append(x)
-        return outputs
-
-
-def resnet50_for_segmentation(pretrained=False, progress=True, **kwargs):
-    """Create ResNet for segmentation task.
-
-    Parameters
-    ----------
-    pretrained
-        If True, uses ResNet50 pretrained weights.
-    progress
-        If True, shows a progress bar when downloading the pretrained weights.
-    **kwargs
-        Keyword arguments to be passed to the parent ResNet model.
-        return_features : :py:class:`list`, Optional
-            A list of integers indicating the feature layers to be returned from
-            the original module.
-
-    Returns
-    -------
-        Instance of the ResNet model for segmentation.
-    """
-    model = ResNet4Segmentation(
-        torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], **kwargs
-    )
-
-    if pretrained:
-        state_dict = load_state_dict_from_url(
-            torchvision.models.resnet.ResNet50_Weights.DEFAULT.url,
-            progress=progress,
-        )
-        model.load_state_dict(state_dict)
-
-    # erase ResNet head (for classification), not used for segmentation
-    delattr(model, "avgpool")
-    delattr(model, "fc")
-
-    return model
-
-
-resnet50_for_segmentation.__doc__ = torchvision.models.resnet50.__doc__