import torch import torchvision import numpy as np from tqdm import tqdm import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision.transforms as transforms import matplotlib.pyplot as plt ################### 数据集初始化与读入 ################### train_transform = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2), transforms.ToTensor() ]) train_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,download=False,transform=train_transform) test_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,download=False,transform=transforms.ToTensor()) train_loader = torch.utils.data.DataLoader(train_dset, batch_size=128, shuffle=True, num_workers=0) test_loader = torch.utils.data.DataLoader(test_dset, batch_size=128, shuffle=False, num_workers=0) ####################################################### ################### 构建模型 ################### class Net(nn.Module): def __init__(self,act): super(Net, self).__init__() # 卷积层 (32x32x3的图像) self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # 卷积层(16x16x16) self.conv2 = nn.Conv2d(16, 32, 3, padding=1) # 卷积层(8x8x32) self.conv3 = nn.Conv2d(32, 64, 3, padding=1) # 最大池化层 self.pool = nn.MaxPool2d(2, 2) # linear layer (64 * 4 * 4 -> 500) self.fc1 = nn.Linear(64 * 4 * 4, 500) # linear layer (500 -> 10) self.fc2 = nn.Linear(500, 10) if act == 'relu': self.act = F.relu elif act == 'tanh': self.act = torch.tanh elif act == 'sigmoid': self.act = F.sigmoid def forward(self, x): # add sequence of convolutional and max pooling layers x = self.pool(self.act(self.conv1(x))) x = self.pool(self.act(self.conv2(x))) x = self.pool(self.act(self.conv3(x))) # flatten image input x = x.view(-1, 64 * 4 * 4) x = self.act(self.fc1(x)) x = self.fc2(x) return x ####################################################### ################### 模型加入batchnorm ################### class BnNet(nn.Module): def __init__(self, act): super(BnNet, self).__init__() # 卷积层 (32x32x3的图像) self.conv1 = nn.Conv2d(3, 16, 3, padding=1) self.bn1 = nn.BatchNorm2d(16) # 卷积层(16x16x16) self.conv2 = nn.Conv2d(16, 32, 3, padding=1) self.bn2 = nn.BatchNorm2d(32) # 卷积层(8x8x32) self.conv3 = nn.Conv2d(32, 64, 3, padding=1) self.bn3 = nn.BatchNorm2d(64) # 最大池化层 self.pool = nn.MaxPool2d(2, 2) # linear layer (64 * 4 * 4 -> 500) self.fc1 = nn.Linear(64 * 4 * 4, 500) self.bn4 = nn.BatchNorm1d(500) # linear layer (500 -> 10) self.fc2 = nn.Linear(500, 10) if act == 'relu': self.act = F.relu elif act == 'tanh': self.act = torch.tanh elif act == 'sigmoid': self.act = F.sigmoid def forward(self, x): # add sequence of convolutional and max pooling layers x = self.pool(self.act(self.bn1(self.conv1(x)))) x = self.pool(self.act(self.bn2(self.conv2(x)))) x = self.pool(self.act(self.bn3(self.conv3(x)))) # flatten image input x = x.view(-1, 64 * 4 * 4) x = self.act(self.bn4(self.fc1(x))) x = self.fc2(x) return x ################### 构建模型 ################### class DeepNet(nn.Module): def __init__(self,act): super(DeepNet, self).__init__() ################### 代码填空：请在此填补模型定义代码 ################### # 卷积层 (32x32x3的图像) self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # 卷积层(32x32x16) self.conv2 = nn.Conv2d(16, 32, 3, padding=1) # 卷积层(16x16x32) self.conv3 = nn.Conv2d(32, 64, 3, padding=1) # 卷积层(16x16x64) self.conv4 = nn.Conv2d(64, 128, 3, padding=1) # 卷积层(8x8x128) self.conv5 = nn.Conv2d(128, 256, 3, padding=1) # 卷积层(8x8x256) self.conv6 = nn.Conv2d(256, 512, 3, padding=1) # 最大池化层 self.pool = nn.MaxPool2d(2, 2) # 自适应平均池化层 self.apool = nn.AdaptiveAvgPool2d((1, 1)) # linear layer (512 -> 256) self.fc1 = nn.Linear(512, 256) # linear layer (256 -> 128) self.fc2 = nn.Linear(256, 128) # linear layer (128 -> 10) self.fc3 = nn.Linear(128, 10) if act == 'relu': self.act = F.relu elif act == 'tanh': self.act = torch.tanh elif act == 'sigmoid': self.act = F.sigmoid ################################################################## def forward(self, x): # convolutional layers ################### 代码填空：请在此填补前向计算代码 ################### x = self.pool(self.act(self.conv2(self.act(self.conv1(x))))) x = self.pool(self.act(self.conv4(self.act(self.conv3(x))))) x = self.apool(self.act(self.conv6(self.act(self.conv5(x))))) # flatten image input x = x.view(-1, 512) x = self.act(self.fc1(x)) x = self.act(self.fc2(x)) x = self.fc3(x) return x ################################################################## class BnDeepNet(nn.Module): def __init__(self,act): super(BnDeepNet, self).__init__() ################### 代码填空：请在此填补模型定义代码 ################### # 卷积层 (32x32x3的图像) self.conv1 = nn.Conv2d(3, 16, 3, padding=1) self.bn1 = nn.BatchNorm2d(16) # 卷积层(32x32x16) self.conv2 = nn.Conv2d(16, 32, 3, padding=1) self.bn2 = nn.BatchNorm2d(32) # 卷积层(16x16x32) self.conv3 = nn.Conv2d(32, 64, 3, padding=1) self.bn3 = nn.BatchNorm2d(64) # 卷积层(16x16x64) self.conv4 = nn.Conv2d(64, 128, 3, padding=1) self.bn4 = nn.BatchNorm2d(128) # 卷积层(8x8x128) self.conv5 = nn.Conv2d(128, 256, 3, padding=1) self.bn5 = nn.BatchNorm2d(256) # 卷积层(8x8x256) self.conv6 = nn.Conv2d(256, 512, 3, padding=1) self.bn6 = nn.BatchNorm2d(512) # 最大池化层 self.pool = nn.MaxPool2d(2, 2) # 自适应平均池化层 self.apool = nn.AdaptiveAvgPool2d((1, 1)) # linear layer (512 -> 256) self.fc1 = nn.Linear(512, 256) self.bn7 = nn.BatchNorm1d(256) # linear layer (256 -> 128) self.fc2 = nn.Linear(256, 128) self.bn8 = nn.BatchNorm1d(128) # linear layer (128 -> 10) self.fc3 = nn.Linear(128, 10) if act == 'relu': self.act = F.relu elif act == 'tanh': self.act = torch.tanh elif act == 'sigmoid': self.act = F.sigmoid ################################################################### def forward(self, x): # convolutional layers ################### 代码填空：请在此填补前向计算代码 ################### x = self.pool(self.act(self.bn2(self.conv2(self.act(self.bn1(self.conv1(x))))))) x = self.pool(self.act(self.bn4(self.conv4(self.act(self.bn3(self.conv3(x))))))) x = self.apool(self.act(self.bn6(self.conv6(self.act(self.bn5(self.conv5(x))))))) # flatten image input x = x.view(-1, 512) x = self.act(self.bn7(self.fc1(x))) x = self.act(self.bn8(self.fc2(x))) x = self.fc3(x) return x ################################################################## ################### 训练前准备 ################### # model = Net('tanh') # model = BnNet('relu') # model = DeepNet('tanh') model = BnDeepNet('relu') device=torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) criterion = nn.CrossEntropyLoss() optimizer_type = "SGD" # optimizer_type = "Adam" if optimizer_type == "SGD": optimizer = optim.SGD(model.parameters(), lr=0.001) elif optimizer_type == "Adam": ########## 代码填空：请在此填补Adam优化器计算代码, lr=0.0001 ########### optimizer = optim.Adam(model.parameters(), lr = 0.0001) ################################################################## n_epochs = 100 train_losses = [] valid_losses = [] accuracies = [] ################################################ ################### 训练+验证 ################### for epoch in range(n_epochs): train_loss = 0.0 valid_loss = 0.0 model.train() for idx,(img,label) in tqdm(enumerate(train_loader)): img, label=img.to(device), label.to(device) optimizer.zero_grad() output = model(img) loss = criterion(output,label) loss.backward() optimizer.step() train_loss += loss.item() * img.shape[0] model.eval() correct = 0 total = 0 for idx,(img,label) in tqdm(enumerate(test_loader)): img, label=img.to(device), label.to(device) output = model(img) loss = criterion(output, label) valid_loss += loss.item() * img.shape[0] _, predicted = torch.max(output.data, 1) total += label.size(0) correct += (predicted == label).sum().item() train_loss = train_loss / len(train_dset) valid_loss = valid_loss / len(test_dset) train_losses.append(train_loss) valid_losses.append(valid_loss) accuracy = correct / total accuracies.append(accuracy) print(f"Epoch:{epoch}, Acc:{correct/total}, Train Loss:{train_loss}, Test Loss:{valid_loss}") ################################################ ################### 曲线绘制 ################### print("MAX ACC: ",np.max(accuracies)) plt.plot(range(n_epochs), train_losses, label='Train Loss') plt.plot(range(n_epochs), valid_losses, label='Valid Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.savefig("Loss.png") plt.clf() # 绘制验证集准确率随epoch的变化曲线 plt.plot(range(n_epochs), accuracies, label='Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend() plt.savefig("Acc.png") ################################################