70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
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import torch.nn as nn
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import torch.nn.functional as F
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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(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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class BasicBlock(nn.Module):
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def __init__(self, in_chan, out_chan, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(in_chan, out_chan, stride)
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self.bn1 = nn.BatchNorm2d(out_chan)
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self.conv2 = conv3x3(out_chan, out_chan)
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self.bn2 = nn.BatchNorm2d(out_chan)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = None
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if in_chan != out_chan or stride != 1:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_chan),
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)
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def forward(self, x):
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residual = self.conv1(x)
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residual = F.relu(self.bn1(residual))
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residual = self.conv2(residual)
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residual = self.bn2(residual)
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shortcut = x
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if self.downsample is not None:
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shortcut = self.downsample(x)
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out = shortcut + residual
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out = self.relu(out)
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return out
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def create_layer_basic(in_chan, out_chan, bnum, stride=1):
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layers = [BasicBlock(in_chan, out_chan, stride=stride)]
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for i in range(bnum - 1):
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layers.append(BasicBlock(out_chan, out_chan, stride=1))
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return nn.Sequential(*layers)
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class ResNet18(nn.Module):
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def __init__(self):
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super(ResNet18, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
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self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
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self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
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self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(self.bn1(x))
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x = self.maxpool(x)
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x = self.layer1(x)
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feat8 = self.layer2(x) # 1/8
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feat16 = self.layer3(feat8) # 1/16
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feat32 = self.layer4(feat16) # 1/32
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return feat8, feat16, feat32
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