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import torch |
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from torch import nn |
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class ResNet18(nn.Module): |
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def __init__(self, in_channels: int, num_classes: int): |
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super().__init__() |
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self.initial_conv = nn.Conv2d( |
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in_channels=in_channels, |
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out_channels=64, |
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kernel_size=7, |
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stride=2, |
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padding=3, |
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bias=False, |
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) |
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self.bn = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = nn.Sequential(BasicBlock(64, 64), BasicBlock(64, 64)) |
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self.layer2 = nn.Sequential( |
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BasicBlock(64, 128, stride=2, downsample=self._downsample(64, 128)), |
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BasicBlock(128, 128), |
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) |
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self.layer3 = nn.Sequential( |
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BasicBlock(128, 256, stride=2, downsample=self._downsample(128, 256)), |
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BasicBlock(256, 256), |
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) |
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self.layer4 = nn.Sequential( |
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BasicBlock(256, 512, stride=2, downsample=self._downsample(256, 512)), |
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BasicBlock(512, 512), |
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) |
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self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) |
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self.drop = nn.Dropout(0.15) |
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self.flatten = nn.Flatten(1) |
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self.fc = nn.Linear(512, num_classes) |
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@staticmethod |
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def _downsample(in_channels: int, out_channels: int) -> nn.Sequential: |
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return nn.Sequential( |
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nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=2, bias=False), |
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nn.BatchNorm2d(out_channels), |
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) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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x = self.initial_conv(x) |
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x = self.bn(x) |
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x = self.relu(x) |
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x = self.max_pool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.layer4(x) |
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x = self.avg_pool(x) |
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x = self.drop(x) |
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x = self.flatten(x) |
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x = self.fc(x) |
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return x |
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class BasicBlock(nn.Module): |
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def __init__( |
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self, |
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in_channels: int, |
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out_channels: int, |
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stride: int = 1, |
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downsample: nn.Module = None, |
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): |
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super().__init__() |
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self.downsample = downsample |
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self.conv1 = nn.Conv2d( |
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in_channels, |
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out_channels, |
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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.bn1 = nn.BatchNorm2d(out_channels) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = nn.Conv2d( |
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out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False |
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) |
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self.bn2 = nn.BatchNorm2d(out_channels) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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identity = x |
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output = self.conv1(x) |
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output = self.bn1(output) |
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output = self.relu(output) |
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output = self.conv2(output) |
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output = self.bn2(output) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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output += identity |
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output = self.relu(output) |
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return output |
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