| | import torch |
| | from torch import Tensor |
| | import torch.nn as nn |
| | from typing import Type, Any, Callable, Union, List, Optional |
| |
|
| | try: |
| | from torch.hub import load_state_dict_from_url |
| | except ImportError: |
| | from torch.utils.model_zoo import load_url as load_state_dict_from_url |
| |
|
| |
|
| | model_urls = { |
| | 'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth', |
| | 'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth', |
| | 'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth', |
| | 'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth', |
| | 'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth', |
| | 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', |
| | 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', |
| | 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', |
| | 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', |
| | } |
| |
|
| |
|
| |
|
| |
|
| | 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(BasicBlock, self).__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") |
| | |
| | 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: |
| | identity = self.downsample(x) |
| |
|
| | out += identity |
| | out = self.relu(out) |
| |
|
| | return out |
| |
|
| |
|
| | class Bottleneck(nn.Module): |
| | |
| | |
| | |
| | |
| | |
| |
|
| | 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(Bottleneck, self).__init__() |
| | if norm_layer is None: |
| | norm_layer = nn.BatchNorm2d |
| | width = int(planes * (base_width / 64.)) * groups |
| | |
| | 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(ResNet, self).__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: |
| | |
| | |
| | replace_stride_with_dilation = [False, False, False] |
| | if len(replace_stride_with_dilation) != 3: |
| | raise ValueError("replace_stride_with_dilation should be None " |
| | "or a 3-element tuple, got {}".format(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) |
| |
|
| | |
| | |
| | |
| | if zero_init_residual: |
| | for m in self.modules(): |
| | if isinstance(m, Bottleneck): |
| | nn.init.constant_(m.bn3.weight, 0) |
| | elif isinstance(m, BasicBlock): |
| | nn.init.constant_(m.bn2.weight, 0) |
| |
|
| | 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): |
| | |
| | out = {} |
| | x = self.conv1(x) |
| | x = self.bn1(x) |
| | x = self.relu(x) |
| | x = self.maxpool(x) |
| | out['f0'] = x |
| |
|
| | x = self.layer1(x) |
| | out['f1'] = x |
| |
|
| | x = self.layer2(x) |
| | out['f2'] = x |
| |
|
| | x = self.layer3(x) |
| | out['f3'] = x |
| | |
| | x = self.layer4(x) |
| | out['f4'] = x |
| |
|
| | x = self.avgpool(x) |
| | x = torch.flatten(x, 1) |
| | out['penultimate'] = x |
| |
|
| | x = self.fc(x) |
| | out['logits'] = x |
| |
|
| | |
| | return out |
| |
|
| | |
| | |
| |
|
| | def forward(self, x): |
| | return self._forward_impl(x) |
| |
|
| |
|
| | def _resnet( |
| | arch: str, |
| | block: Type[Union[BasicBlock, Bottleneck]], |
| | layers: List[int], |
| | pretrained: bool, |
| | progress: bool, |
| | **kwargs: Any |
| | ) -> ResNet: |
| | model = ResNet(block, 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) -> ResNet: |
| | r"""ResNet-18 model from |
| | `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. |
| | |
| | Args: |
| | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| | progress (bool): If True, displays a progress bar of the download to stderr |
| | """ |
| | return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) |
| |
|
| |
|
| | def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| | r"""ResNet-34 model from |
| | `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. |
| | |
| | Args: |
| | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| | progress (bool): If True, displays a progress bar of the download to stderr |
| | """ |
| | return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) |
| |
|
| |
|
| | def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| | r"""ResNet-50 model from |
| | `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. |
| | |
| | Args: |
| | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| | progress (bool): If True, displays a progress bar of the download to stderr |
| | """ |
| | return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) |
| |
|
| |
|
| | def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| | r"""ResNet-101 model from |
| | `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. |
| | |
| | Args: |
| | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| | progress (bool): If True, displays a progress bar of the download to stderr |
| | """ |
| | return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) |
| |
|
| |
|
| | def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| | r"""ResNet-152 model from |
| | `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. |
| | |
| | Args: |
| | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| | progress (bool): If True, displays a progress bar of the download to stderr |
| | """ |
| | return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) |
| |
|
| |
|