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| """ResNet in PyTorch. | |
| ImageNet-Style ResNet | |
| [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | |
| Deep Residual Learning for Image Recognition. arXiv:1512.03385 | |
| Adapted from: https://github.com/bearpaw/pytorch-classification | |
| """ | |
| 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, is_last=False): | |
| super(BasicBlock, self).__init__() | |
| self.is_last = is_last | |
| 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) | |
| preact = out | |
| out = F.relu(out) | |
| if self.is_last: | |
| return out, preact | |
| else: | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, in_planes, planes, stride=1, is_last=False): | |
| super(Bottleneck, self).__init__() | |
| self.is_last = is_last | |
| 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) | |
| preact = out | |
| out = F.relu(out) | |
| if self.is_last: | |
| return out, preact | |
| else: | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False, pool=False): | |
| super(ResNet, self).__init__() | |
| self.in_planes = 64 | |
| if pool: | |
| self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| else: | |
| self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) if pool else nn.Identity() | |
| 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.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| 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, num_blocks, stride): | |
| strides = [stride] + [1] * (num_blocks - 1) | |
| layers = [] | |
| for i in range(num_blocks): | |
| stride = strides[i] | |
| layers.append(block(self.in_planes, planes, stride)) | |
| self.in_planes = planes * block.expansion | |
| return nn.Sequential(*layers) | |
| def forward(self, x, layer=100): | |
| out = self.maxpool(F.relu(self.bn1(self.conv1(x)))) | |
| out = self.layer1(out) | |
| out = self.layer2(out) | |
| out = self.layer3(out) | |
| out = self.layer4(out) | |
| out = self.avgpool(out) | |
| out = torch.flatten(out, 1) | |
| return out | |
| def resnet18(**kwargs): | |
| return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
| def resnet34(**kwargs): | |
| return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) | |
| def resnet50(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| def resnet101(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) | |
| model_dict = { | |
| 'resnet18': [resnet18, 512], | |
| 'resnet34': [resnet34, 512], | |
| 'resnet50': [resnet50, 2048], | |
| 'resnet101': [resnet101, 2048], | |
| } | |
| class LinearBatchNorm(nn.Module): | |
| """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" | |
| def __init__(self, dim, affine=True): | |
| super(LinearBatchNorm, self).__init__() | |
| self.dim = dim | |
| self.bn = nn.BatchNorm2d(dim, affine=affine) | |
| def forward(self, x): | |
| x = x.view(-1, self.dim, 1, 1) | |
| x = self.bn(x) | |
| x = x.view(-1, self.dim) | |
| return x | |
| class SupConResNet(nn.Module): | |
| """backbone + projection head""" | |
| def __init__(self, name='resnet50', head='mlp', feat_dim=128, pool=False): | |
| super(SupConResNet, self).__init__() | |
| model_fun, dim_in = model_dict[name] | |
| self.encoder = model_fun(pool=pool) | |
| if head == 'linear': | |
| self.head = nn.Linear(dim_in, feat_dim) | |
| elif head == 'mlp': | |
| self.head = nn.Sequential( | |
| nn.Linear(dim_in, dim_in), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(dim_in, feat_dim) | |
| ) | |
| else: | |
| raise NotImplementedError( | |
| 'head not supported: {}'.format(head)) | |
| def forward(self, x): | |
| feat = self.encoder(x) | |
| feat = F.normalize(self.head(feat), dim=1) | |
| return feat | |
| class SupCEResNet(nn.Module): | |
| """encoder + classifier""" | |
| def __init__(self, name='resnet50', num_classes=10, pool=False): | |
| super(SupCEResNet, self).__init__() | |
| model_fun, dim_in = model_dict[name] | |
| self.encoder = model_fun(pool=pool) | |
| self.fc = nn.Linear(dim_in, num_classes) | |
| def forward(self, x): | |
| return self.fc(self.encoder(x)) | |
| class LinearClassifier(nn.Module): | |
| """Linear classifier""" | |
| def __init__(self, name='resnet50', num_classes=10): | |
| super(LinearClassifier, self).__init__() | |
| _, feat_dim = model_dict[name] | |
| self.fc = nn.Linear(feat_dim, num_classes) | |
| def forward(self, features): | |
| return self.fc(features) | |