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import torch.nn as nn |
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import torch.nn.functional as F |
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from .build import BACKBONE_REGISTRY |
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from .backbone import Backbone |
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class PreActBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, in_planes, planes, stride=1): |
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super().__init__() |
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self.bn1 = nn.BatchNorm2d(in_planes) |
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self.conv1 = nn.Conv2d( |
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in_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.conv2 = nn.Conv2d( |
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planes, planes, kernel_size=3, stride=1, padding=1, bias=False |
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) |
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if stride != 1 or in_planes != self.expansion * planes: |
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self.shortcut = nn.Sequential( |
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nn.Conv2d( |
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in_planes, |
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self.expansion * planes, |
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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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) |
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def forward(self, x): |
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out = F.relu(self.bn1(x)) |
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shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x |
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out = self.conv1(out) |
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out = self.conv2(F.relu(self.bn2(out))) |
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out += shortcut |
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return out |
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class PreActBottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, in_planes, planes, stride=1): |
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super().__init__() |
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self.bn1 = nn.BatchNorm2d(in_planes) |
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) |
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self.bn2 = 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.bn3 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d( |
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planes, self.expansion * planes, kernel_size=1, bias=False |
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) |
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if stride != 1 or in_planes != self.expansion * planes: |
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self.shortcut = nn.Sequential( |
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nn.Conv2d( |
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in_planes, |
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self.expansion * planes, |
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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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) |
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def forward(self, x): |
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out = F.relu(self.bn1(x)) |
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shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x |
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out = self.conv1(out) |
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out = self.conv2(F.relu(self.bn2(out))) |
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out = self.conv3(F.relu(self.bn3(out))) |
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out += shortcut |
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return out |
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class PreActResNet(Backbone): |
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def __init__(self, block, num_blocks): |
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super().__init__() |
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self.in_planes = 64 |
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self.conv1 = nn.Conv2d( |
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3, 64, kernel_size=3, stride=1, padding=1, bias=False |
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) |
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) |
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) |
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self._out_features = 512 * block.expansion |
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def _make_layer(self, block, planes, num_blocks, stride): |
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strides = [stride] + [1] * (num_blocks-1) |
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layers = [] |
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for stride in strides: |
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layers.append(block(self.in_planes, planes, stride)) |
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self.in_planes = planes * block.expansion |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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out = self.conv1(x) |
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out = self.layer1(out) |
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out = self.layer2(out) |
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out = self.layer3(out) |
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out = self.layer4(out) |
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out = F.avg_pool2d(out, 4) |
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out = out.view(out.size(0), -1) |
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return out |
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""" |
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Preact-ResNet18 was used for the CIFAR10 and |
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SVHN datasets (both are SSL tasks) in |
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- Wang et al. Semi-Supervised Learning by |
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Augmented Distribution Alignment. ICCV 2019. |
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""" |
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@BACKBONE_REGISTRY.register() |
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def preact_resnet18(**kwargs): |
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return PreActResNet(PreActBlock, [2, 2, 2, 2]) |
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