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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.nn as nn
- import torch.nn.functional as F
-
-
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, builder, in_planes, planes, stride=1):
- super(BasicBlock, self).__init__()
- self.convbn1 = builder.convbn3x3(in_planes, planes, stride=stride)
- self.convbn2 = builder.convbn3x3(planes, planes, stride=1)
-
- self.shortcut = nn.Sequential()
- if stride != 1 or in_planes != self.expansion * planes:
- self.shortcut = nn.Sequential(
- builder.convbn1x1(in_planes, self.expansion * planes, stride=stride),
- )
-
- def forward(self, x):
- out = F.relu(self.convbn1(x))
- out = self.convbn2(out)
- out += self.shortcut(x)
- out = F.relu(out)
- return out
-
-
- class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, builder, in_planes, planes, stride=1):
- super(Bottleneck, self).__init__()
- self.convbn1 = builder.convbn1x1(in_planes, planes)
- self.convbn2 = builder.convbn3x3(planes, planes, stride=stride)
- self.convbn3 = builder.convbn1x1(planes, self.expansion * planes)
-
- self.shortcut = nn.Sequential()
- if stride != 1 or in_planes != self.expansion * planes:
- self.shortcut = nn.Sequential(
- builder.convbn1x1(in_planes, self.expansion * planes, stride=stride),
- )
-
- def forward(self, x):
- out = F.relu(self.convbn1(x))
- out = F.relu(self.convbn2(out))
- out = self.convbn3(out)
- out += self.shortcut(x)
- out = F.relu(out)
-
- return out
-
-
- class ResNet(nn.Module):
- def __init__(self, builder, block, num_blocks, num_classes):
- super(ResNet, self).__init__()
- self.in_planes = 64
- self.builder = builder
- 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.avgpool = nn.AdaptiveAvgPool2d(1)
-
- self.fc = nn.Conv2d(512 * block.expansion, num_classes, 1)
-
- def _make_layer(self, block, planes, num_blocks, stride):
- strides = [stride] + [1] * (num_blocks - 1)
- layers = []
- for stride in strides:
- layers.append(block(self.builder, self.in_planes, planes, stride))
- self.in_planes = planes * block.expansion
-
- return nn.Sequential(*layers)
-
- 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 = self.fc(out)
- return out.flatten(1)
-
-
- def c_resnet18(pretrained=False, builder=None, num_classes=10):
- if pretrained:
- raise NotImplementedError
- return ResNet(builder, BasicBlock, [2, 2, 2, 2], num_classes)
-
-
- def c_resnet34(pretrained=False, builder=None, num_classes=10):
- if pretrained:
- raise NotImplementedError
- return ResNet(builder, BasicBlock, [3, 4, 6, 3], num_classes)
-
-
- def c_resnet50(pretrained=False, builder=None, num_classes=10):
- if pretrained:
- raise NotImplementedError
- return ResNet(builder, Bottleneck, [3, 4, 6, 3], num_classes)
-
-
- def c_resnet101(pretrained=False, builder=None, num_classes=10):
- if pretrained:
- raise NotImplementedError
- return ResNet(builder, Bottleneck, [3, 4, 23, 3], num_classes)
-
-
- def c_resnet152(pretrained=False, builder=None, num_classes=10):
- if pretrained:
- raise NotImplementedError
- return ResNet(builder, Bottleneck, [3, 8, 36, 3], num_classes)
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