71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
'''ResNet18 in PyTorch.
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Modified based on (https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py)
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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 BasicBlock(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(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(
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in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
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stride=1, padding=1, bias=False)
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self.shortcut = nn.Sequential()
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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,
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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.conv1(x))
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out = self.conv2(out)
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10):
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super(ResNet, self).__init__()
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
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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.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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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 = F.relu(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 = self.avgpool(out)
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out = torch.flatten(out, 1) # 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 ResNet18():
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return ResNet(BasicBlock, [2, 2, 2, 2])
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