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
[docs]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
==========
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(ResNet4Segmentation, self).__init__(*args, **kwargs)
[docs] 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
[docs]def resnet50_for_segmentation(pretrained=False, progress=True, **kwargs):
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__