【PyTorch】（基础七）---- 完整训练流程

Python资料网推荐【PyTorch】（基础七）---- 完整训练流程这篇文章给大家，欢迎收藏Python资料网享受知识的乐趣

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 = data
        if 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+1
        if 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 = data
            if 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 + accuracy

    print("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 nn

image_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 x

model = 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))

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