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import torch.nn as nn |
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import torch.utils.model_zoo as model_zoo |
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from .build import BACKBONE_REGISTRY |
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from .backbone import Backbone |
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model_urls = { |
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"resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", |
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"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", |
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"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", |
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"resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", |
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"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", |
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} |
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def conv3x3(in_planes, out_planes, stride=1): |
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"""3x3 convolution with padding""" |
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return nn.Conv2d( |
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in_planes, |
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out_planes, |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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bias=False |
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) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super().__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super().__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d( |
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planes, |
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planes, |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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bias=False |
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) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d( |
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planes, planes * self.expansion, kernel_size=1, bias=False |
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) |
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self.bn3 = nn.BatchNorm2d(planes * self.expansion) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class ResNet(Backbone): |
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def __init__( |
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self, |
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block, |
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layers, |
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ms_class=None, |
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ms_layers=[], |
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ms_p=0.5, |
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ms_a=0.1, |
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**kwargs |
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): |
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self.inplanes = 64 |
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super().__init__() |
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self.conv1 = nn.Conv2d( |
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3, 64, kernel_size=7, stride=2, padding=3, bias=False |
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) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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self.global_avgpool = nn.AdaptiveAvgPool2d(1) |
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self._out_features = 512 * block.expansion |
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self.mixstyle = None |
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if ms_layers: |
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self.mixstyle = ms_class(p=ms_p, alpha=ms_a) |
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for layer_name in ms_layers: |
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assert layer_name in ["layer1", "layer2", "layer3"] |
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print( |
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f"Insert {self.mixstyle.__class__.__name__} after {ms_layers}" |
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) |
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self.ms_layers = ms_layers |
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self._init_params() |
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def _make_layer(self, block, planes, blocks, stride=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d( |
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self.inplanes, |
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planes * block.expansion, |
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kernel_size=1, |
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stride=stride, |
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bias=False, |
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), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample)) |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes)) |
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return nn.Sequential(*layers) |
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def _init_params(self): |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_( |
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m.weight, mode="fan_out", nonlinearity="relu" |
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) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.BatchNorm1d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.Linear): |
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nn.init.normal_(m.weight, 0, 0.01) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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def featuremaps(self, x): |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.relu(x) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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if "layer1" in self.ms_layers: |
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x = self.mixstyle(x) |
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x = self.layer2(x) |
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if "layer2" in self.ms_layers: |
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x = self.mixstyle(x) |
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x = self.layer3(x) |
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if "layer3" in self.ms_layers: |
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x = self.mixstyle(x) |
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return self.layer4(x) |
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def forward(self, x): |
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f = self.featuremaps(x) |
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v = self.global_avgpool(f) |
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return v.view(v.size(0), -1) |
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def init_pretrained_weights(model, model_url): |
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pretrain_dict = model_zoo.load_url(model_url) |
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model.load_state_dict(pretrain_dict, strict=False) |
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""" |
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Residual network configurations: |
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-- |
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resnet18: block=BasicBlock, layers=[2, 2, 2, 2] |
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resnet34: block=BasicBlock, layers=[3, 4, 6, 3] |
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resnet50: block=Bottleneck, layers=[3, 4, 6, 3] |
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resnet101: block=Bottleneck, layers=[3, 4, 23, 3] |
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resnet152: block=Bottleneck, layers=[3, 8, 36, 3] |
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""" |
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@BACKBONE_REGISTRY.register() |
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def resnet18(pretrained=True, **kwargs): |
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model = ResNet(block=BasicBlock, layers=[2, 2, 2, 2]) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet18"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet34(pretrained=True, **kwargs): |
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model = ResNet(block=BasicBlock, layers=[3, 4, 6, 3]) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet34"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet50(pretrained=True, **kwargs): |
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model = ResNet(block=Bottleneck, layers=[3, 4, 6, 3]) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet101(pretrained=True, **kwargs): |
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model = ResNet(block=Bottleneck, layers=[3, 4, 23, 3]) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet101"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet152(pretrained=True, **kwargs): |
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model = ResNet(block=Bottleneck, layers=[3, 8, 36, 3]) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet152"]) |
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return model |
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""" |
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Residual networks with mixstyle |
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""" |
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@BACKBONE_REGISTRY.register() |
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def resnet18_ms_l123(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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model = ResNet( |
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block=BasicBlock, |
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layers=[2, 2, 2, 2], |
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ms_class=MixStyle, |
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ms_layers=["layer1", "layer2", "layer3"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet18"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet18_ms_l12(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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model = ResNet( |
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block=BasicBlock, |
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layers=[2, 2, 2, 2], |
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ms_class=MixStyle, |
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ms_layers=["layer1", "layer2"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet18"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet18_ms_l1(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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|
model = ResNet( |
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block=BasicBlock, |
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layers=[2, 2, 2, 2], |
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ms_class=MixStyle, |
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ms_layers=["layer1"] |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet18"]) |
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return model |
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|
|
|
@BACKBONE_REGISTRY.register() |
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def resnet50_ms_l123(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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|
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|
model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 6, 3], |
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ms_class=MixStyle, |
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ms_layers=["layer1", "layer2", "layer3"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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|
@BACKBONE_REGISTRY.register() |
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def resnet50_ms_l12(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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|
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|
model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 6, 3], |
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ms_class=MixStyle, |
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ms_layers=["layer1", "layer2"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet50_ms_l1(pretrained=True, **kwargs): |
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from dassl.modeling.ops import MixStyle |
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|
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|
model = ResNet( |
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|
block=Bottleneck, |
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layers=[3, 4, 6, 3], |
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ms_class=MixStyle, |
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ms_layers=["layer1"] |
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) |
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|
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if pretrained: |
|
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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|
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|
|
@BACKBONE_REGISTRY.register() |
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|
def resnet101_ms_l123(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import MixStyle |
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|
|
|
|
model = ResNet( |
|
|
block=Bottleneck, |
|
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layers=[3, 4, 23, 3], |
|
|
ms_class=MixStyle, |
|
|
ms_layers=["layer1", "layer2", "layer3"], |
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) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet101"]) |
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|
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|
return model |
|
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|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet101_ms_l12(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import MixStyle |
|
|
|
|
|
model = ResNet( |
|
|
block=Bottleneck, |
|
|
layers=[3, 4, 23, 3], |
|
|
ms_class=MixStyle, |
|
|
ms_layers=["layer1", "layer2"], |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet101"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet101_ms_l1(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import MixStyle |
|
|
|
|
|
model = ResNet( |
|
|
block=Bottleneck, |
|
|
layers=[3, 4, 23, 3], |
|
|
ms_class=MixStyle, |
|
|
ms_layers=["layer1"] |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet101"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
""" |
|
|
Residual networks with efdmix |
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|
""" |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet18_efdmix_l123(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import EFDMix |
|
|
|
|
|
model = ResNet( |
|
|
block=BasicBlock, |
|
|
layers=[2, 2, 2, 2], |
|
|
ms_class=EFDMix, |
|
|
ms_layers=["layer1", "layer2", "layer3"], |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet18"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet18_efdmix_l12(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import EFDMix |
|
|
|
|
|
model = ResNet( |
|
|
block=BasicBlock, |
|
|
layers=[2, 2, 2, 2], |
|
|
ms_class=EFDMix, |
|
|
ms_layers=["layer1", "layer2"], |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet18"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet18_efdmix_l1(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import EFDMix |
|
|
|
|
|
model = ResNet( |
|
|
block=BasicBlock, |
|
|
layers=[2, 2, 2, 2], |
|
|
ms_class=EFDMix, |
|
|
ms_layers=["layer1"] |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet18"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet50_efdmix_l123(pretrained=True, **kwargs): |
|
|
from dassl.modeling.ops import EFDMix |
|
|
|
|
|
model = ResNet( |
|
|
block=Bottleneck, |
|
|
layers=[3, 4, 6, 3], |
|
|
ms_class=EFDMix, |
|
|
ms_layers=["layer1", "layer2", "layer3"], |
|
|
) |
|
|
|
|
|
if pretrained: |
|
|
init_pretrained_weights(model, model_urls["resnet50"]) |
|
|
|
|
|
return model |
|
|
|
|
|
|
|
|
@BACKBONE_REGISTRY.register() |
|
|
def resnet50_efdmix_l12(pretrained=True, **kwargs): |
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from dassl.modeling.ops import EFDMix |
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|
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model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 6, 3], |
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ms_class=EFDMix, |
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ms_layers=["layer1", "layer2"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet50_efdmix_l1(pretrained=True, **kwargs): |
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from dassl.modeling.ops import EFDMix |
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|
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model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 6, 3], |
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ms_class=EFDMix, |
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ms_layers=["layer1"] |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet50"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet101_efdmix_l123(pretrained=True, **kwargs): |
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from dassl.modeling.ops import EFDMix |
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|
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model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 23, 3], |
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ms_class=EFDMix, |
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ms_layers=["layer1", "layer2", "layer3"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet101"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet101_efdmix_l12(pretrained=True, **kwargs): |
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from dassl.modeling.ops import EFDMix |
|
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|
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model = ResNet( |
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block=Bottleneck, |
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layers=[3, 4, 23, 3], |
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ms_class=EFDMix, |
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|
ms_layers=["layer1", "layer2"], |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet101"]) |
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return model |
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@BACKBONE_REGISTRY.register() |
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def resnet101_efdmix_l1(pretrained=True, **kwargs): |
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from dassl.modeling.ops import EFDMix |
|
|
|
|
|
model = ResNet( |
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|
block=Bottleneck, |
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|
layers=[3, 4, 23, 3], |
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|
ms_class=EFDMix, |
|
|
ms_layers=["layer1"] |
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) |
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if pretrained: |
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init_pretrained_weights(model, model_urls["resnet101"]) |
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return model |
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|
