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'''ResNet in PyTorch. | ||
For Pre-activation ResNet, see 'preact_resnet.py'. | ||
Reference: | ||
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | ||
Deep Residual Learning for Image Recognition. arXiv:1512.03385 | ||
''' | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
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class BasicBlock(nn.Module): | ||
expansion = 1 | ||
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def __init__(self, in_planes, planes, stride=1): | ||
super(BasicBlock, self).__init__() | ||
self.conv1 = nn.Conv2d( | ||
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) | ||
self.bn1 = nn.BatchNorm2d(planes) | ||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, | ||
stride=1, padding=1, bias=False) | ||
self.bn2 = nn.BatchNorm2d(planes) | ||
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self.shortcut = nn.Sequential() | ||
if stride != 1 or in_planes != self.expansion*planes: | ||
self.shortcut = nn.Sequential( | ||
nn.Conv2d(in_planes, self.expansion*planes, | ||
kernel_size=1, stride=stride, bias=False), | ||
nn.BatchNorm2d(self.expansion*planes) | ||
) | ||
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def forward(self, x): | ||
out = F.relu(self.bn1(self.conv1(x))) | ||
out = self.bn2(self.conv2(out)) | ||
out += self.shortcut(x) | ||
out = F.relu(out) | ||
return out | ||
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class Bottleneck(nn.Module): | ||
expansion = 4 | ||
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def __init__(self, in_planes, planes, stride=1): | ||
super(Bottleneck, self).__init__() | ||
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) | ||
self.bn1 = nn.BatchNorm2d(planes) | ||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, | ||
stride=stride, padding=1, bias=False) | ||
self.bn2 = nn.BatchNorm2d(planes) | ||
self.conv3 = nn.Conv2d(planes, self.expansion * | ||
planes, kernel_size=1, bias=False) | ||
self.bn3 = nn.BatchNorm2d(self.expansion*planes) | ||
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self.shortcut = nn.Sequential() | ||
if stride != 1 or in_planes != self.expansion*planes: | ||
self.shortcut = nn.Sequential( | ||
nn.Conv2d(in_planes, self.expansion*planes, | ||
kernel_size=1, stride=stride, bias=False), | ||
nn.BatchNorm2d(self.expansion*planes) | ||
) | ||
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def forward(self, x): | ||
out = F.relu(self.bn1(self.conv1(x))) | ||
out = F.relu(self.bn2(self.conv2(out))) | ||
out = self.bn3(self.conv3(out)) | ||
out += self.shortcut(x) | ||
out = F.relu(out) | ||
return out | ||
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class ResNet(nn.Module): | ||
def __init__(self, block, num_blocks, num_classes=10): | ||
super(ResNet, self).__init__() | ||
self.in_planes = 64 | ||
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, | ||
stride=1, padding=1, bias=False) | ||
self.bn1 = nn.BatchNorm2d(64) | ||
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) | ||
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) | ||
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) | ||
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) | ||
self.linear = nn.Linear(512*block.expansion, num_classes) | ||
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def _make_layer(self, block, planes, num_blocks, stride): | ||
strides = [stride] + [1]*(num_blocks-1) | ||
layers = [] | ||
for stride in strides: | ||
layers.append(block(self.in_planes, planes, stride)) | ||
self.in_planes = planes * block.expansion | ||
return nn.Sequential(*layers) | ||
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def forward(self, x): | ||
out = F.relu(self.bn1(self.conv1(x))) | ||
out = self.layer1(out) | ||
out = self.layer2(out) | ||
out = self.layer3(out) | ||
out = self.layer4(out) | ||
out = F.avg_pool2d(out, 4) | ||
out = out.view(out.size(0), -1) | ||
out = self.linear(out) | ||
return out | ||
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def ResNet18(num_classes=10): | ||
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes) | ||
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def ResNet34(num_classes=10): | ||
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes) | ||
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def ResNet50(num_classes=10): | ||
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes) | ||
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def ResNet101(num_classes=10): | ||
return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes) | ||
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def ResNet152(num_classes=10): | ||
return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes) |
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