torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,num_workers=4, pin_memory=True)

num_workers 参数是 DataLoader 类的一个参数，它指定了数据加载器使用的子进程数量。通过增加 num_workers 的数量，可以并行地读取和预处理数据，从而提高数据加载的速度。

通常情况下，增加 num_workers 的数量可以提高数据加载的效率，因为它可以使数据加载和预处理工作在多个进程中同时进行。然而，当 num_workers 的数量超过一定阈值时，增加更多的进程可能不会再带来更多的性能提升，甚至可能会导致性能下降。

这是因为增加 num_workers 的数量也会增加进程间通信的开销。当 num_workers 的数量过多时，进程间通信的开销可能会超过并行化所带来的收益，从而导致性能下降。

此外，还需要考虑到计算机硬件的限制。如果你的计算机 CPU 核心数量有限，增加 num_workers 的数量也可能会导致性能下降，因为每个进程需要占用 CPU 核心资源。

因此，对于 num_workers 参数的设置，需要根据具体情况进行调整和优化。通常情况下，一个合理的 num_workers 值应该在 2 到 8 之间，具体取决于你的计算机硬件配置和数据集大小等因素。在实际应用中，可以通过尝试不同的 num_workers 值来找到最优的配置。

综上所述，当 num_workers 的值从 4 增加到 8 时，如果你的计算机硬件配置和数据集大小等因素没有发生变化，那么两者之间的性能差异可能会很小，或者甚至没有显著差异。

测试代码如下

import torch
import torchvision
import matplotlib.pyplot as plt
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
import torch.multiprocessing as mp
import timeif __name__ == '__main__':mp.freeze_support()train_on_gpu = torch.cuda.is_available()if not train_on_gpu:print('CUDA is not available. Training on CPU...')else:print('CUDA is available! Training on GPU...')device = torch.device("cuda" if torch.cuda.is_available() else "cpu")batch_size = 4# 设置数据预处理的转换transform = torchvision.transforms.Compose([torchvision.transforms.Resize((512,512)),  # 调整图像大小为 224x224torchvision.transforms.ToTensor(),  # 转换为张量torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 归一化])dataset = torchvision.datasets.ImageFolder('C:\\Users\\ASUS\\PycharmProjects\\pythonProject1\\cats_and_dogs_train',transform=transform)val_ratio = 0.2val_size = int(len(dataset) * val_ratio)train_size = len(dataset) - val_sizetrain_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])train_dataset = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,num_workers=4, pin_memory=True)val_dataset = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True,num_workers=4, pin_memory=True)model = models.resnet18()num_classes = 2for param in model.parameters():param.requires_grad = Falsemodel.fc = nn.Sequential(nn.Dropout(),nn.Linear(model.fc.in_features, num_classes),nn.LogSoftmax(dim=1))optimizer = optim.Adam(model.parameters(), lr=0.001)criterion = nn.CrossEntropyLoss().to(device)model.to(device)filename = "recognize_cats_and_dogs.pt"def save_checkpoint(epoch, model, optimizer, filename):checkpoint = {'epoch': epoch,'model_state_dict': model.state_dict(),'optimizer_state_dict': optimizer.state_dict(),'loss': loss,}torch.save(checkpoint, filename)num_epochs = 3train_loss = []for epoch in range(num_epochs):running_loss = 0correct = 0total = 0epoch_start_time = time.time()for i, (inputs, labels) in enumerate(train_dataset):# 将数据放到设备上inputs, labels = inputs.to(device), labels.to(device)# 前向计算outputs = model(inputs)# 计算损失和梯度loss = criterion(outputs, labels)optimizer.zero_grad()loss.backward()# 更新模型参数optimizer.step()# 记录损失和准确率running_loss += loss.item()train_loss.append(loss.item())_, predicted = torch.max(outputs.data, 1)correct += (predicted == labels).sum().item()total += labels.size(0)accuracy_train = 100 * correct / total# 在测试集上计算准确率with torch.no_grad():running_loss_test = 0correct_test = 0total_test = 0for inputs, labels in val_dataset:inputs, labels = inputs.to(device), labels.to(device)outputs = model(inputs)loss = criterion(outputs, labels)running_loss_test += loss.item()_, predicted = torch.max(outputs.data, 1)correct_test += (predicted == labels).sum().item()total_test += labels.size(0)accuracy_test = 100 * correct_test / total_test# 输出每个 epoch 的损失和准确率epoch_end_time = time.time()epoch_time = epoch_end_time - epoch_start_timeprint("Epoch [{}/{}], Time: {:.4f}s, Loss: {:.4f}, Train Accuracy: {:.2f}%, Loss: {:.4f}, Test Accuracy: {:.2f}%".format(epoch + 1, num_epochs,epoch_time,running_loss / len(val_dataset),accuracy_train, running_loss_test / len(val_dataset), accuracy_test))save_checkpoint(epoch, model, optimizer, filename)plt.plot(train_loss, label='Train Loss')# 添加图例和标签plt.legend()plt.xlabel('Epochs')plt.ylabel('Loss')plt.title('Training Loss')# 显示图形plt.show()

不同num_workers的结果如下