1.39.ResNet (深度殘差網絡)

Why call Residual

# -*- coding: UTF-8 -*-import torch.nn as nn import torch.nn.functional as Fclass ResBlk(nn.Module):def __init__(self, ch_in, ch_out):self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)self.bn1 = nn.BatchNorm2d(ch_out)self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)self.bn2 = nn.BatchNorm2d(ch_out)self.extra = nn.Sequential()if ch_out != ch_in:# [b, ch_in, h, w] => [b, ch_out, h, w]self.extra = nn.Sequential(nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1),nn.BatchNorm2d(ch_out))def forward(self, x):out = F.relu(self.bn1(self.conv1(x)))out = self.bn2(self.conv2(out))out = self.extra(x) + outreturn out

# -*- coding: UTF-8 -*-import torch from torch.nn import functional as F from torch.utils.data import DataLoader from torchvision import datasets from torchvision import transforms from torch import nn, optim# from torchvision.models import resnet18 class ResBlk(nn.Module):"""resnet block"""def __init__(self, ch_in, ch_out):""":param ch_in::param ch_out:"""super(ResBlk, self).__init__()self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)self.bn1 = nn.BatchNorm2d(ch_out)self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)self.bn2 = nn.BatchNorm2d(ch_out)self.extra = nn.Sequential()if ch_out != ch_in:# [b, ch_in, h, w] => [b, ch_out, h, w]self.extra = nn.Sequential(nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=1),nn.BatchNorm2d(ch_out))def forward(self, x):""":param x: [b, ch, h, w]:return:"""out = F.relu(self.bn1(self.conv1(x)))out = self.bn2(self.conv2(out))# short cut.# extra module: [b, ch_in, h, w] => [b, ch_out, h, w]# element-wise add:out = self.extra(x) + outreturn outclass ResNet18(nn.Module):def __init__(self):super(ResNet18, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(16))# followed 4 blocks# [b, 64, h, w] => [b, 128, h ,w]self.blk1 = ResBlk(16, 16)# [b, 128, h, w] => [b, 256, h, w]self.blk2 = ResBlk(16, 32)# # [b, 256, h, w] => [b, 512, h, w]# self.blk3 = ResBlk(128, 256)# # [b, 512, h, w] => [b, 1024, h, w]# self.blk4 = ResBlk(256, 512)self.outlayer = nn.Linear(32 * 32 * 32, 10)def forward(self, x):""":param x::return:"""x = F.relu(self.conv1(x))# [b, 64, h, w] => [b, 1024, h, w]x = self.blk1(x)x = self.blk2(x)# x = self.blk3(x)# x = self.blk4(x)# print(x.shape)x = x.view(x.size(0), -1)x = self.outlayer(x)return xdef main():batchsz = 32cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor()]), download=True)cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)x, label = iter(cifar_train).next()print('x:', x.shape, 'label:', label.shape)device = torch.device('cuda')# model = Lenet5().to(device)model = ResNet18().to(device)criteon = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(), lr=1e-3)print(model)for epoch in range(1000):model.train()for batchidx, (x, label) in enumerate(cifar_train):# [b, 3, 32, 32]# [b]x, label = x.to(device), label.to(device)logits = model(x)# logits: [b, 10]# label: [b]# loss: tensor scalarloss = criteon(logits, label)# backpropoptimizer.zero_grad()loss.backward()optimizer.step()print(epoch, 'loss:', loss.item())model.eval()with torch.no_grad():# testtotal_correct = 0total_num = 0for x, label in cifar_test:# [b, 3, 32, 32]# [b]x, label = x.to(device), label.to(device)# [b, 10]logits = model(x)# [b]pred = logits.argmax(dim=1)# [b] vs [b] => scalar tensorcorrect = torch.eq(pred, label).float().sum().item()total_correct += correcttotal_num += x.size(0)# print(correct)acc = total_correct / total_numprint(epoch, 'acc:', acc)if __name__ == '__main__':main()

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