用Python从零开始构建ResNet

import torchimport torch.nn as nn

class block(nn.Module):def __init__(self, in_channels, intermediate_channels, identity_downsample=None, stride=1):super(block, self).__init__()self.expansion = 4self.conv1 = nn.Conv2d(in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False)self.bn1 = nn.BatchNorm2d(intermediate_channels)self.conv2 = nn.Conv2d(intermediate_channels,intermediate_channels,kernel_size=3,stride=stride,padding=1,bias=False)self.bn2 = nn.BatchNorm2d(intermediate_channels)self.conv3 = nn.Conv2d(intermediate_channels,intermediate_channels * self.expansion,kernel_size=1,stride=1,padding=0,bias=False)self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)self.relu = nn.ReLU()self.identity_downsample = identity_downsampleself.stride = stridedef forward(self, x):identity = x.clone()x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.conv2(x)x = self.bn2(x)x = self.relu(x)x = self.conv3(x)x = self.bn3(x)if self.identity_downsample is not None:identity = self.identity_downsample(identity)x += identityx = self.relu(x)return x

class ResNet(nn.Module):def __init__(self, block, layers, image_channels, num_classes):super(ResNet, self).__init__()self.in_channels = 64self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)self.bn1 = nn.BatchNorm2d(64)self.relu = nn.ReLU()self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

# Essentially the entire ResNet architecture are in these 4 lines belowself.layer1 = self._make_layer(block, layers[0], intermediate_channels=64, stride=1)self.layer2 = self._make_layer(block, layers[1], intermediate_channels=128, stride=2)self.layer3 = self._make_layer(block, layers[2], intermediate_channels=256, stride=2)self.layer4 = self._make_layer(block, layers[3], intermediate_channels=512, stride=2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512 * 4, num_classes)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avgpool(x)x = x.reshape(x.shape[0], -1)x = self.fc(x)return xdef _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):identity_downsample = Nonelayers = []# Either if we half the input space for ex, 56x56 -> 28x28 (stride=2), or channels changes# we need to adapt the Identity (skip connection) so it will be able to be added# to the layer that's aheadif stride != 1 or self.in_channels != intermediate_channels * 4:identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels,intermediate_channels * 4,kernel_size=1,stride=stride,bias=False),nn.BatchNorm2d(intermediate_channels * 4),)layers.append(block(self.in_channels, intermediate_channels, identity_downsample, stride))# The expansion size is always 4 for ResNet 50,101,152self.in_channels = intermediate_channels * 4# For example for first resnet layer: 256 will be mapped to 64 as intermediate layer,# then finally back to 256. Hence no identity downsample is needed, since stride = 1,# and also same amount of channels.for i in range(num_residual_blocks - 1):layers.append(block(self.in_channels, intermediate_channels))

```plaindef ResNet101(img_channel=3, num_classes=1000):return ResNet(block, [3, 4, 23, 3], img_channel, num_classes)def ResNet152(img_channel=3, num_classes=1000):return ResNet(block, [3, 8, 36, 3], img_channel, num_classes)

def test():net = ResNet101(img_channel=3, num_classes=1000)device = "cuda" if torch.cuda.is_available() else "cpu"y = net(torch.randn(4, 3, 224, 224)).to(device)print(y.size())

test()