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首先要明确一点，我们在编写模型、训练和使用模型的时候通常都是分开的，所以应该把Module的编写以及train方法和test方法分开编写。

调用gpu进行训练：在网络模型，数据，损失函数对象后面都使用.cuda（）方法，如loss_fn = loss_fn.cuda()

【代码示例】完成完整CIFAR10模型的训练

按照官网给出的模型结构进行构建：

# model.py
class myModule(nn.Module):def __init__(self):super().__init__()self.model = nn.Sequential(nn.Conv2d(3, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 64, 5, 1, 2),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(64*4*4, 64),nn.Linear(64, 10))def forward(self, ingput):output = self.model(ingput)return output

导入自己创建的模型，实例化一个模型对象之后，导入CIFAR10数据集进行训练

# train.py
import torchvision
from torch.utils.tensorboard import SummaryWriter
from module import *
from torch import nn
from torch.utils.data import DataLoader# 使用Dataset来下载数据集
train_data = torchvision.datasets.CIFAR10(root="dataset/CIFAR10", train=True, transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root="dataset/CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),download=True)# 数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))# 利用 DataLoader 来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)# 创建网络模型，实例化自定义的模型
mymodule = myModule()
if torch.cuda.is_available():mymodule = mymodule.cuda()# 定义损失函数为交叉熵损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():loss_fn = loss_fn.cuda()# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(mymodule.parameters(), lr=learning_rate)# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10# tensorboard配置日志目录
writer = SummaryWriter("logs_train")for i in range(epoch):print("-------第 {} 轮训练开始-------".format(i+1))# 训练步骤开始mymodule.train()for data in train_dataloader:imgs, targets = dataif torch.cuda.is_available():imgs = imgs.cuda()targets = targets.cuda()outputs = mymodule(imgs)loss = loss_fn(outputs, targets)# 优化器优化模型optimizer.zero_grad()loss.backward()optimizer.step()total_train_step = total_train_step + 1   # 每读取一次图片+1if total_train_step % 100 == 0:print("训练次数：{}, Loss: {}".format(total_train_step, loss.item()))writer.add_scalar("train_loss", loss.item(), total_train_step)# 测试步骤开始mymodule.eval()total_test_loss = 0    # 损失函数值total_accuracy = 0  # 准确率with torch.no_grad():for data in test_dataloader:imgs, targets = dataif torch.cuda.is_available():imgs = imgs.cuda()targets = targets.cuda()outputs = mymodule(imgs)loss = loss_fn(outputs, targets)total_test_loss = total_test_loss + loss.item()accuracy = (outputs.argmax(1) == targets).sum()total_accuracy = total_accuracy + accuracyprint("整体测试集上的Loss: {}".format(total_test_loss))print("整体测试集上的正确率: {}".format(total_accuracy/test_data_size))writer.add_scalar("test_loss", total_test_loss, total_test_step)writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)total_test_step = total_test_step + 1# 每轮都保存模型torch.save(mymodule, "mymodule{}.pth".format(i))print("模型已保存")writer.close()

# test.py
import torch
import torchvision
from PIL import Image
from torch import nnimage_path = "imgs/airplane.png"
image = Image.open(image_path)
print(image)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),torchvision.transforms.ToTensor()])image = transform(image)
print(image.shape)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.model = nn.Sequential(nn.Conv2d(3, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 64, 5, 1, 2),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(64*4*4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return xmodel = torch.load("mymodule9.pth", map_location=torch.device('cpu'))
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
model.eval()
with torch.no_grad():output = model(image)
print(output)print(output.argmax(1))