|
- '''Pre-activation ResNet in PyTorch.
-
- Reference:
- [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
- Identity Mappings in Deep Residual Networks. arXiv:1603.05027
- '''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class PreActBlock(nn.Module):
- '''Pre-activation version of the BasicBlock.'''
- expansion = 1
-
- def __init__(self, in_planes, planes, stride=1):
- super(PreActBlock, self).__init__()
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
-
- 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)
- )
-
- def forward(self, x):
- out = F.relu(self.bn1(x))
- shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
- out = self.conv1(out)
- out = self.conv2(F.relu(self.bn2(out)))
- out += shortcut
- return out
-
-
- class PreActBottleneck(nn.Module):
- '''Pre-activation version of the original Bottleneck module.'''
- expansion = 4
-
- def __init__(self, in_planes, planes, stride=1):
- super(PreActBottleneck, self).__init__()
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
-
- 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)
- )
-
- def forward(self, x):
- out = F.relu(self.bn1(x))
- shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
- out = self.conv1(out)
- out = self.conv2(F.relu(self.bn2(out)))
- out = self.conv3(F.relu(self.bn3(out)))
- out += shortcut
- return out
-
-
- class PreActResNet(nn.Module):
- def __init__(self, block, num_blocks, num_classes=10):
- super(PreActResNet, self).__init__()
- self.in_planes = 64
-
- self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
- 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)
-
- 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)
-
- def forward(self, x):
- out = 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
-
-
- def PreActResNet18():
- return PreActResNet(PreActBlock, [2,2,2,2])
-
- def PreActResNet34():
- return PreActResNet(PreActBlock, [3,4,6,3])
-
- def PreActResNet50():
- return PreActResNet(PreActBottleneck, [3,4,6,3])
-
- def PreActResNet101():
- return PreActResNet(PreActBottleneck, [3,4,23,3])
-
- def PreActResNet152():
- return PreActResNet(PreActBottleneck, [3,8,36,3])
-
-
- def test():
- net = PreActResNet18()
- y = net((torch.randn(1,3,32,32)))
- print(y.size())
-
- # test()
|