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'''Pre-activation ResNet in PyTorch. |
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Reference: |
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun |
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Identity Mappings in Deep Residual Networks. arXiv:1603.05027 |
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''' |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class PreActBlock(nn.Module): |
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'''Pre-activation version of the BasicBlock.''' |
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expansion = 1 |
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def __init__(self, in_planes, planes, stride=1): |
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super(PreActBlock, self).__init__() |
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self.bn1 = nn.BatchNorm2d(in_planes) |
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) |
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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(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False) |
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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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'''Pre-activation version of the original Bottleneck module.''' |
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expansion = 4 |
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def __init__(self, in_planes, planes, stride=1): |
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super(PreActBottleneck, self).__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(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False) |
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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(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False) |
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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(nn.Module): |
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def __init__(self, block, num_blocks, num_classes=10): |
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super(PreActResNet, self).__init__() |
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self.in_planes = 64 |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) |
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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.linear = nn.Linear(512*block.expansion, num_classes) |
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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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out = self.linear(out) |
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return out |
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def PreActResNet18(): |
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return PreActResNet(PreActBlock, [2,2,2,2]) |
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def PreActResNet34(): |
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return PreActResNet(PreActBlock, [3,4,6,3]) |
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def PreActResNet50(): |
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return PreActResNet(PreActBottleneck, [3,4,6,3]) |
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def PreActResNet101(): |
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return PreActResNet(PreActBottleneck, [3,4,23,3]) |
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def PreActResNet152(): |
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return PreActResNet(PreActBottleneck, [3,8,36,3]) |
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def test(): |
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net = PreActResNet18() |
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y = net((torch.randn(1,3,32,32))) |
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print(y.size()) |
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