1.44.Cifar10與ResNet18實戰

Pytorch工程中建立pytorch，在pytorch里面創建lenet5.py、main.py、resnet.py。

1.44.1.lenet5.py

# -*- coding: UTF-8 -*-import torch from torch import nnclass Lenet5(nn.Module):"""for cifar10 dataset."""def __init__(self):super(Lenet5, self).__init__()self.conv_unit = nn.Sequential(# x: [b, 3, 32, 32] => [b, 16, ]nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),nn.MaxPool2d(kernel_size=2, stride=2, padding=0),nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),nn.MaxPool2d(kernel_size=2, stride=2, padding=0),)# flatten# fc unitself.fc_unit = nn.Sequential(nn.Linear(32 * 5 * 5, 32),nn.ReLU(),# nn.Linear(120, 84),# nn.ReLU(),nn.Linear(32, 10))# [b, 3, 32, 32]tmp = torch.randn(2, 3, 32, 32)out = self.conv_unit(tmp)# [b, 16, 5, 5]print('conv out:', out.shape)# # use Cross Entropy Loss# self.criteon = nn.CrossEntropyLoss()def forward(self, x):""":param x: [b, 3, 32, 32]:return:"""batchsz = x.size(0)# [b, 3, 32, 32] => [b, 16, 5, 5]x = self.conv_unit(x)# [b, 16, 5, 5] => [b, 16*5*5]x = x.view(batchsz, 32 * 5 * 5)# [b, 16*5*5] => [b, 10]logits = self.fc_unit(x)# # [b, 10]# pred = F.softmax(logits, dim=1)# loss = self.criteon(logits, y)return logitsdef main():net = Lenet5()tmp = torch.randn(2, 3, 32, 32)out = net(tmp)print('lenet out:', out.shape)if __name__ == '__main__':main()

1.44.2.Resnet.py

# -*- coding: UTF-8 -*-import torch from torch import nn from torch.nn import functional as Fclass ResBlk(nn.Module):"""resnet block"""def __init__(self, ch_in, ch_out, stride=1):""":param ch_in::param ch_out:"""super(ResBlk, self).__init__()# we add stride support for resbok, which is distinct from tutorials.self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, 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=stride),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) + outout = F.relu(out)return outclass ResNet18(nn.Module):def __init__(self):super(ResNet18, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),nn.BatchNorm2d(64))# followed 4 blocks# [b, 64, h, w] => [b, 128, h ,w]self.blk1 = ResBlk(64, 128, stride=2)# [b, 128, h, w] => [b, 256, h, w]self.blk2 = ResBlk(128, 256, stride=2)# # [b, 256, h, w] => [b, 512, h, w]self.blk3 = ResBlk(256, 512, stride=2)# # [b, 512, h, w] => [b, 1024, h, w]self.blk4 = ResBlk(512, 512, stride=2)self.outlayer = nn.Linear(512 * 1 * 1, 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('after conv:', x.shape) #[b, 512, 2, 2]# [b, 512, h, w] => [b, 512, 1, 1]x = F.adaptive_avg_pool2d(x, [1, 1])# print('after pool:', x.shape)x = x.view(x.size(0), -1)x = self.outlayer(x)return xdef main():blk = ResBlk(64, 128, stride=4)tmp = torch.randn(2, 64, 32, 32)out = blk(tmp)print('block:', out.shape)x = torch.randn(2, 3, 32, 32)model = ResNet18()out = model(x)print('resnet:', out.shape)if __name__ == '__main__':main()

1.44.3.main.py

# -*- coding: UTF-8 -*-import torch from torch.utils.data import DataLoader from torchvision import datasets from torchvision import transforms from torch import nn, optimfrom pytorch.lenet5 import Lenet5 from pytorch.resnet import ResNet18def main():batchsz = 128cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), 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(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), 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, 'test acc:', acc)if __name__ == '__main__':main()

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