第P6周—好莱坞明星识别（2）-编程知识

五、模型训练

# 训练循环def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)   # 批次数目train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 计算预测误差pred = model(X)          # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失# 反向传播optimizer.zero_grad()  # grad属性归零loss.backward()        # 反向传播optimizer.step()       # 每一步自动更新# 记录acc与losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc  /= sizetrain_loss /= num_batchesreturn train_acc, train_loss# 测试函数def test(dataloader, model, loss_fn):size        = len(dataloader.dataset)  # 测试集的大小num_batches = len(dataloader)          # 批次数目，(size/batch_size，向上取整)test_loss, test_acc = 0, 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 计算losstarget_pred = model(imgs)loss        = loss_fn(target_pred, target)test_loss += loss.item()test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_acc  /= sizetest_loss /= num_batchesreturn test_acc, test_loss''' 自定义设置动态学习率 '''def adjust_learning_rate(optimizer, epoch, start_lr):# 每 2 个epoch衰减到原来的 0.92lr = start_lr * (0.92 ** (epoch // 2))for param_group in optimizer.param_groups:param_group['lr'] = lr# 设置初始学习率learn_rate = 1e-4optimizer  = torch.optim.SGD(model.parameters(), lr=learn_rate)# 定义学习率调整函数lambda1 = lambda epoch: 0.92 ** (epoch // 4)scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)  # 选定调整方法# 定义损失函数loss_fn = nn.CrossEntropyLoss()# 定义训练参数epochs = 40train_loss = []train_acc = []test_loss = []test_acc = []best_acc = 0  # 用于保存最佳模型的准确率for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)scheduler.step()  # 更新学习率（调用官方动态学习率接口时使用）model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到best modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, lr:{:.2E}')print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))# 保存最佳模型到文件中PATH = './best_model.pth'  # 保存的参数文件名torch.save(best_model.state_dict(), PATH)print('Done')

在非实时编译器中运行出现Python 脚本中使用多进程相关问题报错，问题通常发生在没有正确使用 if __name__ == '__main__': 块的情况下。为了解决这个问题，我将完整代码修改如下：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os, PIL, random, pathlib, warnings
import copywarnings.filterwarnings("ignore")def main():# 您的现有代码放在这里device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(device)import os, PIL, random, pathlibdata_dir = 'D:/P6/48-data/'data_dir = pathlib.Path(data_dir)data_path = list(data_dir.glob('*'))print(data_path)classname = [str(path).split("\\")[3] for path in data_path]print(classname)train_transforms = transforms.Compose([transforms.Resize([224, 224]),transforms.ToTensor(),transforms.Normalize(mean=[0.39354826, 0.41713402, 0.48036146],std=[0.25076334, 0.25809455, 0.28359835])])total_data = datasets.ImageFolder("D:/P6/48-data/", transform=train_transforms)print(total_data)print(total_data.class_to_idx)train_size = int(0.8 * len(total_data))test_size = len(total_data) - train_sizetrain_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])print(train_dataset, test_dataset)batch_size = 32train_dl = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True,num_workers=1)test_dl = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True,num_workers=1)from torchvision.models import vgg16device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print("Using {} device\n".format(device))''' 调用官方的VGG-16模型 '''# 加载预训练模型，并且对模型进行微调model = vgg16(pretrained=True).to(device)  # 加载预训练的vgg16模型for param in model.parameters():param.requires_grad = False  # 冻结模型的参数，这样子在训练的时候只训练最后一层的参数# 修改classifier模块的第6层（即：(6): Linear(in_features=4096, out_features=2, bias=True)）# 注意查看我们下方打印出来的模型model.classifier._modules['6'] = nn.Linear(4096, 17)  # 修改vgg16模型中最后一层全连接层，输出目标类别个数model.to(device)print(model)# 训练循环def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)   # 批次数目train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 计算预测误差pred = model(X)          # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失# 反向传播optimizer.zero_grad()  # grad属性归零loss.backward()        # 反向传播optimizer.step()       # 每一步自动更新# 记录acc与losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc  /= sizetrain_loss /= num_batchesreturn train_acc, train_loss# 测试函数def test(dataloader, model, loss_fn):size        = len(dataloader.dataset)  # 测试集的大小num_batches = len(dataloader)          # 批次数目，(size/batch_size，向上取整)test_loss, test_acc = 0, 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 计算losstarget_pred = model(imgs)loss        = loss_fn(target_pred, target)test_loss += loss.item()test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_acc  /= sizetest_loss /= num_batchesreturn test_acc, test_loss''' 自定义设置动态学习率 '''def adjust_learning_rate(optimizer, epoch, start_lr):# 每 2 个epoch衰减到原来的 0.92lr = start_lr * (0.92 ** (epoch // 2))for param_group in optimizer.param_groups:param_group['lr'] = lr# 设置初始学习率learn_rate = 1e-4optimizer  = torch.optim.SGD(model.parameters(), lr=learn_rate)# 定义学习率调整函数lambda1 = lambda epoch: 0.92 ** (epoch // 4)scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)  # 选定调整方法# 定义损失函数loss_fn = nn.CrossEntropyLoss()# 定义训练参数epochs = 40train_loss = []train_acc = []test_loss = []test_acc = []best_acc = 0  # 用于保存最佳模型的准确率for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)scheduler.step()  # 更新学习率（调用官方动态学习率接口时使用）model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到best modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, lr:{:.2E}')print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))# 保存最佳模型到文件中PATH = './best_model.pth'  # 保存的参数文件名torch.save(best_model.state_dict(), PATH)print('Done')
if __name__ == '__main__':main()