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import torch |
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import torch.nn as nn |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, in_channels, out_channels, i_downsample=None, stride=1): |
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super(Bottleneck, self).__init__() |
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) |
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self.batch_norm1 = nn.BatchNorm2d(out_channels) |
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1) |
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self.batch_norm2 = nn.BatchNorm2d(out_channels) |
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self.conv3 = nn.Conv2d(out_channels, out_channels*self.expansion, kernel_size=1, stride=1, padding=0) |
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self.batch_norm3 = nn.BatchNorm2d(out_channels*self.expansion) |
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self.i_downsample = i_downsample |
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self.stride = stride |
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self.relu = nn.ReLU() |
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def forward(self, x): |
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identity = x.clone() |
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x = self.relu(self.batch_norm1(self.conv1(x))) |
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x = self.relu(self.batch_norm2(self.conv2(x))) |
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x = self.conv3(x) |
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x = self.batch_norm3(x) |
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if self.i_downsample is not None: |
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identity = self.i_downsample(identity) |
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x+=identity |
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x=self.relu(x) |
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return x |
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class Block(nn.Module): |
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expansion = 1 |
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def __init__(self, in_channels, out_channels, i_downsample=None, stride=1): |
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super(Block, self).__init__() |
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride, bias=False) |
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self.batch_norm1 = nn.BatchNorm2d(out_channels) |
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, stride=stride, bias=False) |
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self.batch_norm2 = nn.BatchNorm2d(out_channels) |
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self.i_downsample = i_downsample |
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self.stride = stride |
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self.relu = nn.ReLU() |
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def forward(self, x): |
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identity = x.clone() |
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x = self.relu(self.batch_norm2(self.conv1(x))) |
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x = self.batch_norm2(self.conv2(x)) |
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if self.i_downsample is not None: |
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identity = self.i_downsample(identity) |
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print(x.shape) |
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print(identity.shape) |
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x += identity |
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x = self.relu(x) |
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return x |
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class ResNet(nn.Module): |
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def __init__(self, ResBlock, layer_list, num_classes, num_channels=3): |
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super(ResNet, self).__init__() |
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self.in_channels = 64 |
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self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) |
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self.batch_norm1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU() |
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self.max_pool = nn.MaxPool2d(kernel_size = 3, stride=2, padding=1) |
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self.layer1 = self._make_layer(ResBlock, layer_list[0], planes=64) |
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self.layer2 = self._make_layer(ResBlock, layer_list[1], planes=128, stride=2) |
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self.layer3 = self._make_layer(ResBlock, layer_list[2], planes=256, stride=2) |
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self.layer4 = self._make_layer(ResBlock, layer_list[3], planes=512, stride=2) |
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self.avgpool = nn.AdaptiveAvgPool2d((1,1)) |
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self.fc = nn.Linear(512*ResBlock.expansion, num_classes) |
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def forward(self, x): |
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x = self.relu(self.batch_norm1(self.conv1(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.avgpool(x) |
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x = x.reshape(x.shape[0], -1) |
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x = self.fc(x) |
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return x |
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def _make_layer(self, ResBlock, blocks, planes, stride=1): |
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ii_downsample = None |
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layers = [] |
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if stride != 1 or self.in_channels != planes*ResBlock.expansion: |
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ii_downsample = nn.Sequential( |
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nn.Conv2d(self.in_channels, planes*ResBlock.expansion, kernel_size=1, stride=stride), |
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nn.BatchNorm2d(planes*ResBlock.expansion) |
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) |
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layers.append(ResBlock(self.in_channels, planes, i_downsample=ii_downsample, stride=stride)) |
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self.in_channels = planes*ResBlock.expansion |
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for i in range(blocks-1): |
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layers.append(ResBlock(self.in_channels, planes)) |
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return nn.Sequential(*layers) |
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def ResNet50(num_classes, channels=3): |
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return ResNet(Bottleneck, [3,4,6,3], num_classes, channels) |
