| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from torch.autograd import Variable |
|
|
|
|
| def conv3x3(in_planes, out_planes, stride=1): |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, in_planes, planes, stride=1): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(in_planes, planes, stride) |
| self.bn1 = nn.BatchNorm2d(planes) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = nn.BatchNorm2d(planes) |
|
|
| 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) |
| ) |
|
|
| 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 |
|
|
|
|
| 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 = conv3x3(in_planes, planes, stride) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.conv2 = conv3x3(planes, planes) |
|
|
| 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) |
| ) |
|
|
| def forward(self, x): |
| out = F.relu(self.bn1(x)) |
| shortcut = self.shortcut(out) |
| out = self.conv1(out) |
| out = self.conv2(F.relu(self.bn2(out))) |
| out += shortcut |
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| expansion = 4 |
|
|
| 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) |
|
|
| 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) |
| ) |
|
|
| 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 |
|
|
|
|
| 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) |
|
|
| 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) |
| ) |
|
|
| def forward(self, x): |
| out = F.relu(self.bn1(x)) |
| shortcut = self.shortcut(out) |
| 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 ResNet(nn.Module): |
| def __init__(self, block, num_blocks, num_classes=10): |
| super(ResNet, self).__init__() |
| self.in_planes = 64 |
|
|
| self.conv1 = conv3x3(3,64) |
| 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) |
|
|
| 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, lin=0, lout=5, feat_out=False): |
| out = x |
| if lin < 1 and lout > -1: |
| out = self.conv1(out) |
| out = self.bn1(out) |
| out = F.relu(out) |
| if lin < 2 and lout > 0: |
| out = self.layer1(out) |
| if lin < 3 and lout > 1: |
| out = self.layer2(out) |
| if lin < 4 and lout > 2: |
| out = self.layer3(out) |
| if lin < 5 and lout > 3: |
| out = self.layer4(out) |
| if lout > 4: |
| out = F.avg_pool2d(out, 4) |
| feat = out.view(out.size(0), -1) |
| out = self.linear(feat) |
| if feat_out: |
| return out, feat |
| else: |
| return out |
|
|
|
|
| def ResNet18(num_classes=10): |
| return ResNet(PreActBlock, [2,2,2,2], num_classes=num_classes) |
|
|
| def ResNet34(num_classes=10): |
| return ResNet(BasicBlock, [3,4,6,3], num_classes=num_classes) |
|
|
| def ResNet50(num_classes=10): |
| return ResNet(Bottleneck, [3,4,6,3], num_classes=num_classes) |
|
|
| def ResNet101(num_classes=10): |
| return ResNet(Bottleneck, [3,4,23,3], num_classes=num_classes) |
|
|
| def ResNet152(num_classes=10): |
| return ResNet(Bottleneck, [3,8,36,3], num_classes=num_classes) |
|
|
|
|
| def test(): |
| net = ResNet18() |
| y = net(Variable(torch.randn(1,3,32,32))) |
| print(y.size()) |
|
|
