| ''' |
| Pytorch implementation of ResNet models. |
| |
| Reference: |
| [1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, 2016. |
| ''' |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, in_planes, planes, stride=1): |
| super(BasicBlock, self).__init__() |
| self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) |
| self.bn2 = nn.BatchNorm2d(planes) |
|
|
| self.shortcut = nn.Sequential() |
| if stride != 1 or in_planes != self.expansion*planes: |
| self.shortcut = nn.Sequential( |
| nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False), |
| nn.BatchNorm2d(self.expansion*planes) |
| ) |
|
|
| def forward(self, x): |
| out = F.relu(self.bn1(self.conv1(x))) |
| out = self.bn2(self.conv2(out)) |
| out += self.shortcut(x) |
| out = F.relu(out) |
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| def __init__(self, in_planes, planes, stride=1): |
| super(Bottleneck, self).__init__() |
| self.conv1 = nn.Conv2d(in_planes, 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, self.expansion*planes, kernel_size=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(self.expansion*planes) |
|
|
| self.shortcut = nn.Sequential() |
| if stride != 1 or in_planes != self.expansion*planes: |
| self.shortcut = nn.Sequential( |
| nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False), |
| nn.BatchNorm2d(self.expansion*planes) |
| ) |
|
|
| def forward(self, x): |
| out = F.relu(self.bn1(self.conv1(x))) |
| out = F.relu(self.bn2(self.conv2(out))) |
| out = self.bn3(self.conv3(out)) |
| out += self.shortcut(x) |
| out = F.relu(out) |
| return out |
|
|
|
|
| class ResNet(nn.Module): |
| def __init__(self, block, num_blocks, num_classes=10, temp=1.0): |
| super(ResNet, self).__init__() |
| self.in_planes = 64 |
|
|
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(64) |
| self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) |
| self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) |
| self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) |
| self.fc = nn.Linear(512*block.expansion, num_classes) |
| self.temp = temp |
|
|
| def _make_layer(self, block, planes, num_blocks, stride): |
| strides = [stride] + [1]*(num_blocks-1) |
| layers = [] |
| for stride in strides: |
| layers.append(block(self.in_planes, planes, stride)) |
| self.in_planes = planes * block.expansion |
| return nn.Sequential(*layers) |
|
|
| def forward(self, x, return_features=False): |
| out0 = F.relu(self.bn1(self.conv1(x))) |
| out1 = self.layer1(out0) |
| out2 = self.layer2(out1) |
| out3 = self.layer3(out2) |
| out4 = self.layer4(out3) |
| out = F.avg_pool2d(out4, 4) |
| feature = out.view(out.size(0), -1) |
| logits = self.fc(feature) / self.temp |
| if return_features: |
| return logits, {'out1': out1, 'out2': out2, 'out3': out3, 'out4': out4, 'feature': feature} |
| return logits |
|
|
| def classifier(self, x): |
| return self.fc(x) / self.temp |
|
|
|
|
| def resnet18(temp=1.0, **kwargs): |
| model = ResNet(BasicBlock, [2, 2, 2, 2], temp=temp, **kwargs) |
| return model |
|
|
|
|
| def resnet34(temp=1.0, **kwargs): |
| model = ResNet(BasicBlock, [3, 4, 6, 3], temp=temp, **kwargs) |
| return model |
|
|
|
|
| def resnet50(temp=1.0, **kwargs): |
| model = ResNet(Bottleneck, [3, 4, 6, 3], temp=temp, **kwargs) |
| return model |
|
|
|
|
| def resnet101(temp=1.0, **kwargs): |
| model = ResNet(Bottleneck, [3, 4, 23, 3], temp=temp, **kwargs) |
| return model |
|
|
|
|
| def resnet110(temp=1.0, **kwargs): |
| model = ResNet(Bottleneck, [3, 4, 26, 3], temp=temp, **kwargs) |
| return model |
|
|
|
|
| def resnet152(temp=1.0, **kwargs): |
| model = ResNet(Bottleneck, [3, 8, 36, 3], temp=temp, **kwargs) |
| return model |
