--- a
+++ b/biovil_t/resnet.py
@@ -0,0 +1,80 @@
+#  -------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  -------------------------------------------------------------------------------------------
+
+from typing import Any, List, Tuple, Type, Union
+
+import torch
+from torch.hub import load_state_dict_from_url
+from torchvision.models.resnet import model_urls, ResNet, BasicBlock, Bottleneck
+
+TypeSkipConnections = Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+
+
+class ResNetHIML(ResNet):
+    """Wrapper class of the original torchvision ResNet model.
+
+    The forward function is updated to return the penultimate layer
+    activations, which are required to obtain image patch embeddings.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+
+    def forward(self, x: torch.Tensor,
+                return_intermediate_layers: bool = False) -> Union[torch.Tensor, TypeSkipConnections]:
+        """ResNetHIML forward pass. Optionally returns intermediate layers using the
+        ``return_intermediate_layers`` argument.
+
+        :param return_intermediate_layers: If ``True``, return layers x0-x4 as a tuple,
+            otherwise return x4 only.
+        """
+
+        x0 = self.conv1(x)
+        x0 = self.bn1(x0)
+        x0 = self.relu(x0)
+        x0 = self.maxpool(x0)
+
+        x1 = self.layer1(x0)
+        x2 = self.layer2(x1)
+        x3 = self.layer3(x2)
+        x4 = self.layer4(x3)
+
+        if return_intermediate_layers:
+            return x0, x1, x2, x3, x4
+        else:
+            return x4
+
+
+def _resnet(arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int],
+            pretrained: bool, progress: bool, **kwargs: Any) -> ResNetHIML:
+    """Instantiate a custom :class:`ResNet` model.
+
+    Adapted from :mod:`torchvision.models.resnet`.
+    """
+    model = ResNetHIML(block=block, layers=layers, **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
+        model.load_state_dict(state_dict)
+    return model
+
+
+def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNetHIML:
+    r"""ResNet-18 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+
+    :param pretrained: If ``True``, returns a model pre-trained on ImageNet.
+    :param progress: If ``True``, displays a progress bar of the download to ``stderr``.
+    """
+    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)
+
+
+def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNetHIML:
+    r"""ResNet-50 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
+
+    :param pretrained: If ``True``, returns a model pre-trained on ImageNet
+    :param progress: If ``True``, displays a progress bar of the download to ``stderr``.
+    """
+    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)