train.py

import torchvision
from torch.utils.data import DataLoader
from torch import nn
import torch
from model import classify_model, vgg_16

# 定义设备
device = torch.device("cuda" if torch.cuda.is_available else "cpu")
print("当前设备 {}".format(device))

# 准备数据集
train_dataset = torchvision.datasets.CIFAR10("dataset",train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_dataset = torchvision.datasets.CIFAR10("dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)

# 数据集长度
train_data_size = len(train_dataset)
test_data_size = len(test_dataset)
print("训练数据集的长度 {}".format(train_data_size))
print("测试数据集的长度 {}".format(test_data_size))

# 加载数据集
batch_size = 64
train_dataloader = DataLoader(train_dataset,batch_size=batch_size)
test_dataloader = DataLoader(test_dataset,batch_size=batch_size)

model_name = "vgg16"            # vgg16 / classify_model
# model = classify_model()        # 自定义神经网络
model = vgg_16()                  # 利用现有的网络
model.to(device)
print(model)

# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn.to(device)

# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)

# 设置网络参数
total_train_step = 0
total_test_step = 0
# 训练轮数
epoch = 100

for i in range(epoch):
    print("-------第{}轮训练开始--------".format(i+1))

    # 训练步骤开始
    model.train()       # 只对特定层起作用,如Dropout
    for data in train_dataloader:
        imgs, targets = data 
        imgs = imgs.to(device)
        targets = targets.to(device)
        output = model(imgs)
        loss = loss_fn(output, targets)

        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step = total_train_step + 1
        if(total_train_step % 100 == 0):
            print("训练次数 {},Loss: {}".format(total_train_step, loss.item()))

    # 测试步骤开始
    total_test_loss =  0
    total_test_acc = 0
    model.eval()        
    with torch.no_grad():
        for data in test_dataloader:
            imgs, targets = data 
            imgs = imgs.to(device)
            targets = targets.to(device)
            output = model(imgs)
            loss = loss_fn(output, targets)
            total_test_loss = total_test_loss + loss.item()
            acc = (output.argmax(1) == targets).sum()
            total_test_acc = total_test_acc + acc

    print("整体测试集上的Loss: {}".format(total_test_loss))
    print("整体测试集上的Acc: {}".format(total_test_acc/test_data_size))

    torch.save(model.state_dict(), "./check_point/{}/classify_model_{}.pth".format((model_name), (i+1)))
    print("模型已保存")