|
- from functools import partial
- from typing import Any, Callable, List, Optional, Type, Union
-
- import torch
- import torch.nn as nn
- from torch import Tensor
-
-
- __all__ = [
- "ResNet",
- "resnet18",
- "resnet34",
- "resnet50",
- "resnet101",
- "resnet152",
- ]
-
-
- def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
- """3x3 convolution with padding"""
- return nn.Conv2d(
- in_planes,
- out_planes,
- kernel_size=3,
- stride=stride,
- padding=dilation,
- groups=groups,
- bias=False,
- dilation=dilation,
- )
-
-
- def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
- """1x1 convolution"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
- class BasicBlock(nn.Module):
- expansion: int = 1
-
- def __init__(
- self,
- inplanes: int,
- planes: int,
- stride: int = 1,
- downsample: Optional[nn.Module] = None,
- groups: int = 1,
- base_width: int = 64,
- dilation: int = 1,
- norm_layer: Optional[Callable[..., nn.Module]] = None,
- ) -> None:
- super().__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- if groups != 1 or base_width != 64:
- raise ValueError("BasicBlock only supports groups=1 and base_width=64")
- if dilation > 1:
- raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
- # Both self.conv1 and self.downsample layers downsample the input when stride != 1
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = norm_layer(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = norm_layer(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x: Tensor) -> Tensor:
- identity = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- print(x.shape)
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
-
- class Bottleneck(nn.Module):
- # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
- # while original implementation places the stride at the first 1x1 convolution(self.conv1)
- # according to "Deep residual learning for image recognition" https://arxiv.org/abs/1512.03385.
- # This variant is also known as ResNet V1.5 and improves accuracy according to
- # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
- expansion: int = 4
-
- def __init__(
- self,
- inplanes: int,
- planes: int,
- stride: int = 1,
- downsample: Optional[nn.Module] = None,
- groups: int = 1,
- base_width: int = 64,
- dilation: int = 1,
- norm_layer: Optional[Callable[..., nn.Module]] = None,
- ) -> None:
- super().__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- width = int(planes * (base_width / 64.0)) * groups
- # Both self.conv2 and self.downsample layers downsample the input when stride != 1
- self.conv1 = conv1x1(inplanes, width)
- self.bn1 = norm_layer(width)
- self.conv2 = conv3x3(width, width, stride, groups, dilation)
- self.bn2 = norm_layer(width)
- self.conv3 = conv1x1(width, planes * self.expansion)
- self.bn3 = norm_layer(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x: Tensor) -> Tensor:
- identity = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
-
- class ResNet(nn.Module):
- def __init__(
- self,
- block: Type[Union[BasicBlock, Bottleneck]],
- layers: List[int],
- num_classes: int = 1000,
- zero_init_residual: bool = False,
- groups: int = 1,
- width_per_group: int = 64,
- replace_stride_with_dilation: Optional[List[bool]] = None,
- norm_layer: Optional[Callable[..., nn.Module]] = None,
- ) -> None:
- super().__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- self._norm_layer = norm_layer
-
- self.inplanes = 64
- self.dilation = 1
- if replace_stride_with_dilation is None:
- # each element in the tuple indicates if we should replace
- # the 2x2 stride with a dilated convolution instead
- replace_stride_with_dilation = [False, False, False]
- if len(replace_stride_with_dilation) != 3:
- raise ValueError(
- "replace_stride_with_dilation should be None "
- f"or a 3-element tuple, got {replace_stride_with_dilation}"
- )
- self.groups = groups
- self.base_width = width_per_group
- self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
- self.bn1 = norm_layer(self.inplanes)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
- elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
- # Zero-initialize the last BN in each residual branch,
- # so that the residual branch starts with zeros, and each residual block behaves like an identity.
- # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
- if zero_init_residual:
- for m in self.modules():
- if isinstance(m, Bottleneck) and m.bn3.weight is not None:
- nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
- elif isinstance(m, BasicBlock) and m.bn2.weight is not None:
- nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
-
- def _make_layer(
- self,
- block: Type[Union[BasicBlock, Bottleneck]],
- planes: int,
- blocks: int,
- stride: int = 1,
- dilate: bool = False,
- ) -> nn.Sequential:
- norm_layer = self._norm_layer
- downsample = None
- previous_dilation = self.dilation
- if dilate:
- self.dilation *= stride
- stride = 1
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- conv1x1(self.inplanes, planes * block.expansion, stride),
- norm_layer(planes * block.expansion),
- )
-
- layers = []
- layers.append(
- block(
- self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer
- )
- )
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(
- block(
- self.inplanes,
- planes,
- groups=self.groups,
- base_width=self.base_width,
- dilation=self.dilation,
- norm_layer=norm_layer,
- )
- )
-
- return nn.Sequential(*layers)
-
- def _forward_impl(self, x: Tensor) -> Tensor:
- # See note [TorchScript super()]
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.avgpool(x)
- x = torch.flatten(x, 1)
- x = self.fc(x)
-
- return x
-
- def forward(self, x: Tensor) -> Tensor:
- return self._forward_impl(x)
-
-
- def _resnet(
- block: Type[Union[BasicBlock, Bottleneck]],
- layers: List[int],
- weights,
- progress: bool,
- **kwargs: Any,
- ) -> ResNet:
-
-
- model = ResNet(block, layers, **kwargs)
-
- if weights is not None:
- model.load_state_dict(weights.get_state_dict(progress=progress))
-
- return model
-
-
-
-
- def resnet18(*, weights = None, progress: bool = True, **kwargs: Any) -> ResNet:
- """ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
- Args:
- weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The
- pretrained weights to use. See
- :class:`~torchvision.models.ResNet18_Weights` below for
- more details, and possible values. By default, no pre-trained
- weights are used.
- progress (bool, optional): If True, displays a progress bar of the
- download to stderr. Default is True.
- **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
- base class. Please refer to the `source code
- <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
- for more details about this class.
- .. autoclass:: torchvision.models.ResNet18_Weights
- :members:
- """
-
-
- return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs)
-
-
-
- def resnet34(*, weights = None, progress: bool = True, **kwargs: Any) -> ResNet:
- """ResNet-34 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
- Args:
- weights (:class:`~torchvision.models.ResNet34_Weights`, optional): The
- pretrained weights to use. See
- :class:`~torchvision.models.ResNet34_Weights` below for
- more details, and possible values. By default, no pre-trained
- weights are used.
- progress (bool, optional): If True, displays a progress bar of the
- download to stderr. Default is True.
- **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
- base class. Please refer to the `source code
- <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
- for more details about this class.
- .. autoclass:: torchvision.models.ResNet34_Weights
- :members:
- """
-
-
- return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs)
-
-
-
- def resnet50(*, weights = None, progress: bool = True, **kwargs: Any) -> ResNet:
- """ResNet-50 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
- .. note::
- The bottleneck of TorchVision places the stride for downsampling to the second 3x3
- convolution while the original paper places it to the first 1x1 convolution.
- This variant improves the accuracy and is known as `ResNet V1.5
- <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_.
- Args:
- weights (:class:`~torchvision.models.ResNet50_Weights`, optional): The
- pretrained weights to use. See
- :class:`~torchvision.models.ResNet50_Weights` below for
- more details, and possible values. By default, no pre-trained
- weights are used.
- progress (bool, optional): If True, displays a progress bar of the
- download to stderr. Default is True.
- **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
- base class. Please refer to the `source code
- <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
- for more details about this class.
- .. autoclass:: torchvision.models.ResNet50_Weights
- :members:
- """
-
-
- return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
-
-
-
- def resnet101(*, weights = None, progress: bool = True, **kwargs: Any) -> ResNet:
- """ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
- .. note::
- The bottleneck of TorchVision places the stride for downsampling to the second 3x3
- convolution while the original paper places it to the first 1x1 convolution.
- This variant improves the accuracy and is known as `ResNet V1.5
- <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_.
- Args:
- weights (:class:`~torchvision.models.ResNet101_Weights`, optional): The
- pretrained weights to use. See
- :class:`~torchvision.models.ResNet101_Weights` below for
- more details, and possible values. By default, no pre-trained
- weights are used.
- progress (bool, optional): If True, displays a progress bar of the
- download to stderr. Default is True.
- **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
- base class. Please refer to the `source code
- <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
- for more details about this class.
- .. autoclass:: torchvision.models.ResNet101_Weights
- :members:
- """
-
-
- return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)
-
-
-
- def resnet152(*, weights = None, progress: bool = True, **kwargs: Any) -> ResNet:
- """ResNet-152 from `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`__.
- .. note::
- The bottleneck of TorchVision places the stride for downsampling to the second 3x3
- convolution while the original paper places it to the first 1x1 convolution.
- This variant improves the accuracy and is known as `ResNet V1.5
- <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_.
- Args:
- weights (:class:`~torchvision.models.ResNet152_Weights`, optional): The
- pretrained weights to use. See
- :class:`~torchvision.models.ResNet152_Weights` below for
- more details, and possible values. By default, no pre-trained
- weights are used.
- progress (bool, optional): If True, displays a progress bar of the
- download to stderr. Default is True.
- **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
- base class. Please refer to the `source code
- <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
- for more details about this class.
- .. autoclass:: torchvision.models.ResNet152_Weights
- :members:
- """
-
-
- return _resnet(Bottleneck, [3, 8, 36, 3], weights, progress, **kwargs)
-
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