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| from functools import partial | |
| from typing import Any, Callable, List, Optional, Type, Union | |
| import torch | |
| import torch.nn as nn | |
| from torch import Tensor | |
| 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: | |
| 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__() | |
| # _log_api_usage_once(self) | |
| 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 resnet18(weights=None): | |
| # weights: path | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=4) | |
| if weights is not None: | |
| model.load_state_dict(torch.load(weights)) | |
| return model | |
| def resnet10(): | |
| return ResNet(BasicBlock, [1, 1, 1, 1], num_classes=4) | |