Pytorch实现图像识别之数字识别(附详细注释)

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transfORMs
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
import cv2

# 下载训练集
train_dataset = datasets.MNIST(root='E:\mnist',
                               train=True,
                               transform=transforms.ToTensor(),
                               download=True)
# 下载测试集
test_dataset = datasets.MNIST(root='E:\mnist',
                              train=False,
                              transform=transforms.ToTensor(),
                              download=True)

# dataset 参数用于指定我们载入的数据集名称
# batch_size参数设置了每个包中的图片数据个数
# 在装载的过程会将数据随机打乱顺序并进打包
batch_size = 64
# 建立一个数据迭代器
# 装载训练集
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)
# 装载测试集
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True)


# 卷积层使用 torch.nn.Conv2d
# 激活层使用 torch.nn.ReLU
# 池化层使用 torch.nn.MaxPool2d
# 全连接层使用 torch.nn.Linear
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 3, 1, 2),
                                   nn.ReLU(), nn.MaxPool2d(2, 2))

        self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5), nn.ReLU(),
                                   nn.MaxPool2d(2, 2))

        self.fc1 = nn.Sequential(nn.Linear(16 * 5 * 5, 120),
                                 nn.BatchNorm1d(120), nn.ReLU())

        self.fc2 = nn.Sequential(
            nn.Linear(120, 84),
            nn.BatchNorm1d(84),
            nn.ReLU(),
            nn.Linear(84, 10))
        # 最后的结果一定要变为 10，因为数字的选项是 0 ~ 9

    def forward(self, x):
        x = self.conv1(x)
        # print("1:", x.shape)
        # 1: torch.Size([64, 6, 30, 30])
        # max pooling
        # 1: torch.Size([64, 6, 15, 15])
        x = self.conv2(x)
        # print("2:", x.shape)
        # 2: torch.Size([64, 16, 5, 5])
        # 对参数实现扁平化
        x = x.view(x.size()[0], -1)
        x = self.fc1(x)
        x = self.fc2(x)
        return x


def test_image_data(images, labels):
    # 初始输出为一段数字图像序列
    # 将一段图像序列整合到一张图片上 (make_grid会默认将图片变成三通道，默认值为0)
    # images: torch.Size([64, 1, 28, 28])
    img = torchvision.utils.make_grid(images)
    # img: torch.Size([3, 242, 242])
    # 将通道维度置在第三个维度
    img = img.numpy().transpose(1, 2, 0)
    # img: torch.Size([242, 242, 3])
    # 减小图像对比度
    std = [0.5, 0.5, 0.5]
    mean = [0.5, 0.5, 0.5]
    img = img * std + mean
    # print(labels)
    cv2.imshow('win2', img)
    key_pressed = cv2.waiTKEy(0)


# 初始化设备信息
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 学习速率
LR = 0.001
# 初始化网络
net = LeNet().to(device)
# 损失函数使用交叉熵
criterion = nn.CrossEntropyLoss()
# 优化函数使用 Adam 自适应优化算法
optimizer = optim.Adam(net.parameters(), lr=LR, )
epoch = 1
if __name__ == '__main__':
    for epoch in range(epoch):
        print("GPU:", torch.cuda.is_available())
        sum_loss = 0.0
        for i, data in enumerate(train_loader):
            inputs, labels = data
            # print(inputs.shape)
            # torch.Size([64, 1, 28, 28])
            # 将内存中的数据复制到gpu显存中去
            inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
            # 将梯度归零
            optimizer.zero_grad()
            # 将数据传入网络进行前向运算
            outputs = net(inputs)
            # 得到损失函数
            loss = criterion(outputs, labels)
            # 反向传播
            loss.backward()
            # 通过梯度做一步参数更新
            optimizer.step()
            # print(loss)
            sum_loss += loss.item()
            if i % 100 == 99:
                print('[%d,%d] loss:%.03f' % (epoch + 1, i + 1, sum_loss / 100))
                sum_loss = 0.0
                # 将模型变换为测试模式
        net.eval()
        correct = 0
        total = 0
        for data_test in test_loader:
            _images, _labels = data_test
            # 将内存中的数据复制到gpu显存中去
            images, labels = Variable(_images).cuda(), Variable(_labels).cuda()
            # 图像预测结果
            output_test = net(images)
            # torch.Size([64, 10])
            # 从每行中找到最大预测索引
            _, predicted = torch.max(output_test, 1)
            # 图像可视化
            # print("predicted:", predicted)
            # test_image_data(_images, _labels)
            # 预测数据的数量
            total += labels.size(0)
            # 预测正确的数量
            correct += (predicted == labels).sum()
        print("correct1: ", correct)
        print("Test acc: {0}".format(correct.item() / total))