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Pytorch implementation of ResNet models.
Reference:
[1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, 2016.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
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)
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 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 ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, temp=1.0, feature_clamp=1e6):
super(ResNet, self).__init__()
self.in_planes = 64
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.fc = nn.Linear(512*block.expansion, num_classes)
self.temp = temp
self.feature_clamp = feature_clamp
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, return_feature=False):
feature1 = F.relu(self.bn1(self.conv1(x)))
feature2 = self.layer1(feature1)
feature3 = self.layer2(feature2)
feature4 = self.layer3(feature3)
out = self.layer4(feature4)
out = F.avg_pool2d(out, 4)
feature = out.view(out.size(0), -1)
out = self.fc(feature) / self.temp
if return_feature:
return out, out
return out
def classifier(self, x):
return self.fc(x)
def feature_1_classifier(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
feature = out.view(out.size(0), -1)
out = self.fc(feature) / self.temp
return out
def feature_2_classifier(self, x):
out = self.layer2(x)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
feature = out.view(out.size(0), -1)
out = self.fc(feature) / self.temp
return out
def feature_3_classifier(self, x):
out = self.layer3(x)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
feature = out.view(out.size(0), -1)
out = self.fc(feature) / self.temp
return out
def feature_4_classifier(self, x):
out = self.layer4(x)
out = F.avg_pool2d(out, 4)
feature = out.view(out.size(0), -1)
out = self.fc(feature) / self.temp
return out
def resnet18(temp=1.0, **kwargs):
model = ResNet(BasicBlock, [2, 2, 2, 2], temp=temp, **kwargs)
return model
def resnet34(temp=1.0, **kwargs):
model = ResNet(BasicBlock, [3, 4, 6, 3], temp=temp, **kwargs)
return model
def resnet50_debug(temp=1.0, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], temp=temp, **kwargs)
return model
def resnet101(temp=1.0, **kwargs):
model = ResNet(Bottleneck, [3, 4, 23, 3], temp=temp, **kwargs)
return model
def resnet110(temp=1.0, **kwargs):
model = ResNet(Bottleneck, [3, 4, 26, 3], temp=temp, **kwargs)
return model
def resnet152(temp=1.0, **kwargs):
model = ResNet(Bottleneck, [3, 8, 36, 3], temp=temp, **kwargs)
return model |