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| import torch | |
| import torch.nn as nn | |
| from torch.hub import load_state_dict_from_url | |
| __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', | |
| 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', | |
| 'wide_resnet50_2', 'wide_resnet101_2'] | |
| model_urls = { | |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', | |
| 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', | |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', | |
| 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', | |
| 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.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, out_planes, stride=1, groups=1, dilation=1): | |
| """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, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, | |
| base_width=64, dilation=1, norm_layer=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") | |
| # 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): | |
| 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 = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, | |
| base_width=64, dilation=1, norm_layer=None): | |
| super(Bottleneck, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| width = int(planes * (base_width / 64.)) * 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): | |
| 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): | |
| """ | |
| ResNet encoder for conditional VAE. | |
| Args: | |
| block: Type of residual block (BasicBlock or Bottleneck) | |
| layers: List of integers indicating the number of blocks in each layer | |
| embed_dim: Dimension of the latent embedding (output dimension) | |
| zero_init_residual: Whether to zero-initialize the last BN in each residual branch | |
| groups: Number of groups for grouped convolutions | |
| width_per_group: Width per group for grouped convolutions | |
| replace_stride_with_dilation: List of booleans for using dilated convolutions | |
| norm_layer: Normalization layer to use (default: BatchNorm2d) | |
| cond_embed_dim: Dimension of the conditional embedding input | |
| dp_rate: Dropout rate applied before the final linear layers | |
| Inputs: | |
| x: Input tensor of shape (batch_size, 1, height, width) | |
| cond: Conditional embedding of shape (batch_size, cond_embed_dim) | |
| Outputs: | |
| mu: Mean of the latent distribution, shape (batch_size, embed_dim) | |
| log_var: Log variance of the latent distribution, shape (batch_size, embed_dim) | |
| """ | |
| def __init__(self, block, layers, embed_dim=256, zero_init_residual=False, | |
| groups=1, width_per_group=64, replace_stride_with_dilation=None, | |
| norm_layer=None, cond_embed_dim=256, dp_rate=0.3): | |
| 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: | |
| # 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 " | |
| "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) | |
| self.groups = groups | |
| self.base_width = width_per_group | |
| self.conv1 = nn.Conv2d(1, 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)) | |
| # Condition embedding network to align with visual features | |
| self.cond_fc = nn.Sequential( | |
| nn.Linear(cond_embed_dim, 512*block.expansion), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(dp_rate) | |
| ) | |
| # Shared fusion layer before mu and log_var prediction | |
| fusion_dim = 512 * block.expansion | |
| self.fusion_fc = nn.Sequential( | |
| nn.Linear(512*block.expansion*2, fusion_dim), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(dp_rate) | |
| ) | |
| # Separate heads for mu and log_var with independent dropout | |
| self.fc_mu = nn.Sequential( | |
| nn.Dropout(dp_rate), | |
| nn.Linear(fusion_dim, embed_dim) | |
| ) | |
| self.fc_logvar = nn.Sequential( | |
| nn.Dropout(dp_rate), | |
| nn.Linear(fusion_dim, embed_dim) | |
| ) | |
| 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): | |
| nn.init.constant_(m.bn3.weight, 0) | |
| elif isinstance(m, BasicBlock): | |
| nn.init.constant_(m.bn2.weight, 0) | |
| def _make_layer(self, block, planes, blocks, stride=1, dilate=False): | |
| 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, cond): | |
| # Extract visual features | |
| 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) | |
| # Process condition through embedding network | |
| cond_embed = self.cond_fc(cond) | |
| # Concatenate visual features and condition embedding | |
| x = torch.cat((x, cond_embed), dim=1) | |
| # Shared fusion | |
| x = self.fusion_fc(x) | |
| # Predict mu and log_var with independent dropout | |
| mu = self.fc_mu(x) | |
| log_var = self.fc_logvar(x) | |
| return mu, log_var | |
| def forward(self, x, cond): | |
| return self._forward_impl(x, cond) | |
| def _resnet(arch, block, layers, pretrained, progress, **kwargs): | |
| model = ResNet(block, layers, **kwargs) | |
| if pretrained: | |
| state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) | |
| # Handle the mismatch in conv1 (3 channels -> 1 channel) | |
| if 'conv1.weight' in state_dict: | |
| pretrained_conv1 = state_dict['conv1.weight'] | |
| # Average the weights across RGB channels for single channel input | |
| state_dict['conv1.weight'] = pretrained_conv1.mean(dim=1, keepdim=True) | |
| # Remove classifier weights as we have custom fc layers | |
| state_dict = {k: v for k, v in state_dict.items() if not k.startswith('fc')} | |
| model.load_state_dict(state_dict, strict=False) | |
| return model | |
| def resnet18(pretrained=False, progress=True, **kwargs): | |
| 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=False, progress=True, **kwargs): | |
| 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=False, progress=True, **kwargs): | |
| 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=False, progress=True, **kwargs): | |
| 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=False, progress=True, **kwargs): | |
| 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) | |
| def resnext50_32x4d(pretrained=False, progress=True, **kwargs): | |
| r"""ResNeXt-50 32x4d model from | |
| `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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 | |
| """ | |
| kwargs['groups'] = 32 | |
| kwargs['width_per_group'] = 4 | |
| return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], | |
| pretrained, progress, **kwargs) | |
| def resnext101_32x8d(pretrained=False, progress=True, **kwargs): | |
| r"""ResNeXt-101 32x8d model from | |
| `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.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 | |
| """ | |
| kwargs['groups'] = 32 | |
| kwargs['width_per_group'] = 8 | |
| return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], | |
| pretrained, progress, **kwargs) | |
| def wide_resnet50_2(pretrained=False, progress=True, **kwargs): | |
| r"""Wide ResNet-50-2 model from | |
| `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ | |
| The model is the same as ResNet except for the bottleneck number of channels | |
| which is twice larger in every block. The number of channels in outer 1x1 | |
| convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 | |
| channels, and in Wide ResNet-50-2 has 2048-1024-2048. | |
| 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 | |
| """ | |
| kwargs['width_per_group'] = 64 * 2 | |
| return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], | |
| pretrained, progress, **kwargs) | |
| def wide_resnet101_2(pretrained=False, progress=True, **kwargs): | |
| r"""Wide ResNet-101-2 model from | |
| `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ | |
| The model is the same as ResNet except for the bottleneck number of channels | |
| which is twice larger in every block. The number of channels in outer 1x1 | |
| convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 | |
| channels, and in Wide ResNet-50-2 has 2048-1024-2048. | |
| 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 | |
| """ | |
| kwargs['width_per_group'] = 64 * 2 | |
| return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], | |
| pretrained, progress, **kwargs) | |
| if __name__ == "__main__": | |
| a = torch.randn(8, 1, 110, 37) | |
| cond = torch.randn(8, 85) | |
| encoder = resnet18(embed_dim=256, cond_embed_dim=85) | |
| b, var = encoder(a, cond) | |
| print(b.shape) | |
| print(var.shape) | |
| encoder = resnet34(embed_dim=256, cond_embed_dim=85) | |
| b, var = encoder(a, cond) | |
| print(b.shape) | |
| print(var.shape) | |
| encoder = resnet50(embed_dim=1024, cond_embed_dim=85) | |
| b, var = encoder(a, cond) | |
| print(b.shape) | |
| print(var.shape) | |