| import torch.nn as nn |
| import math |
| import torch |
| import torch.utils.model_zoo as model_zoo |
| import torch.nn.functional as F |
| from einops import rearrange |
|
|
|
|
| __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', |
| 'resnet152'] |
|
|
|
|
| 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', |
| } |
|
|
|
|
| def conv3x3(in_planes, out_planes, stride=1): |
| """3x3 convolution with padding""" |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
| padding=1, bias=False) |
|
|
|
|
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.relu = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| residual = 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: |
| residual = self.downsample(x) |
|
|
| out += residual |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
| padding=1, bias=False) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(planes * 4) |
| self.relu = nn.ReLU(inplace=True) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| residual = 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: |
| residual = self.downsample(x) |
|
|
| out += residual |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class ResNet(nn.Module): |
|
|
| def __init__(self, block, layers, num_classes=1000): |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, |
| bias=False) |
| self.bn1 = nn.BatchNorm2d(64) |
| 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) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
| self.avgpool = nn.AvgPool2d(7, stride=1) |
| self.fc = nn.Linear(512 * block.expansion, num_classes) |
|
|
| def _make_layer(self, block, planes, blocks, stride=1): |
| downsample = None |
| if stride != 1 or self.inplanes != planes * block.expansion: |
| downsample = nn.Sequential( |
| nn.Conv2d(self.inplanes, planes * block.expansion, |
| kernel_size=1, stride=stride, bias=False), |
| nn.BatchNorm2d(planes * block.expansion), |
| ) |
|
|
| layers = [] |
| layers.append(block(self.inplanes, planes, stride, downsample)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def forward(self, x): |
| 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 = x.view(x.size(0), -1) |
| x = self.fc(x) |
|
|
| return x |
|
|
|
|
| def resnet18(pretrained=False, **kwargs): |
| """Constructs a ResNet-18 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) |
| return model |
|
|
|
|
| def resnet34(pretrained=False, **kwargs): |
| """Constructs a ResNet-34 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) |
| return model |
|
|
|
|
| def resnet50(pretrained=False, **kwargs): |
| """Constructs a ResNet-50 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) |
| return model |
|
|
|
|
| def resnet101(pretrained=False, **kwargs): |
| """Constructs a ResNet-101 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) |
| return model |
|
|
|
|
| def resnet152(pretrained=False, **kwargs): |
| """Constructs a ResNet-152 model. |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| """ |
| model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) |
| return model |
|
|
|
|
| if __name__ == '__main__': |
| m = resnet18(pretrained=True, vit_dim=768) |
| x = torch.rand(1, 3, 256, 256) |
| vit = [torch.rand(1, 256, 768), torch.rand(1, 256, 768), torch.rand(1, 256, 768)] |
| x2, x3, x4 = m(x, vit) |
| print(x2.shape, x3.shape, x4.shape) |
