Pytorch学习之源码理解：pytorch/examples/mnists

Posted 2021-03-24 jiading

Pytorch学习之源码理解：pytorch/examples/mnists

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()#调用父类的构造方法
        self.conv1 = nn.Conv2d(1, 32, 3, 1)#输入1个channel,输出32个channels,kernel_size=3,stride(步长）=1
        self.conv2 = nn.Conv2d(32, 64, 3, 1)#再变成64channels
        self.dropout1 = nn.Dropout2d(0.25)#以0.25的概率dropout
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)#9216->128
        self.fc2 = nn.Linear(128, 10)
    #定义网络各层
    def forward(self, x):
        x = self.conv1(x)
        #线性整流函数（Rectified Linear Unit, ReLU）是一个激活函数，这是当成一层了
        #卷积神经网络中，若不采用非线性激活，会导致神经网络只能拟合线性可分的数据，因此通常会在卷积操作后，添加非线性激活单元，其中包括logistic-sigmoid、tanh-sigmoid、ReLU等。
        x = F.relu(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output


def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    #这是两种模式
    #model.train() ：启用 BatchNormalization 和 Dropout
    #model.eval() ：不启用 BatchNormalization 和 Dropout
    #model.eval()，pytorch会自动把BN和DropOut固定住，不会取平均，而是用训练好的值。
    # 不然的话，一旦test的batch_size过小，很容易就会被BN层导致生成图片颜色失真极大；在模型测试阶段使用
    #trainloader对每一个batch加了id
    for batch_idx, (data, target) in enumerate(train_loader):
        #读入数据到device中，之后就用新的变量表示就可，对程序不影响（物理层和应用层）
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()#初始化优化器参数
        output = model(data)
        loss = F.nll_loss(output, target)#计算loss
        loss.backward()#反向传播，计算梯度和
        optimizer.step()#调整参数。
        #上面的方法都是共享一个参数空间的，所以不需要传递参数
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


def main():
    # Training settings
    #都是可选参数，是为了调参用的
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')#加上参数描述，在--help中输出
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=14, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')

    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()#获取参数，从这里就可以开始调用这些参数了。没有输入也没有设置默认值的就是null,用在布尔表达式里面也可以表示false
    use_cuda = not args.no_cuda and torch.cuda.is_available()#有cuda并且没设置参数说不用才用cuda

    torch.manual_seed(args.seed)#设置随机种子，以便于生成随机数

    device = torch.device("cuda" if use_cuda else "cpu")#决定用cpu还是GPU

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
    #载入训练集
    train_loader = torch.utils.data.DataLoader(
        #torchvision下的datasets模块，如果没发现本地有这个包就下载
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),#输出tensor类型
                           transforms.Normalize((0.1307,), (0.3081,))#do normalize
                       ])),
        batch_size=args.batch_size, shuffle=True, **kwargs)#一次读多少
    #载入测试集
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.test_batch_size, shuffle=True, **kwargs)
    #将模型读入device
    model = Net().to(device)
    #设置优化器，这里使用的是Adagrad优化方法(Adaptive Gradient)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
    #等间隔调整学习率 StepLR
    #等间隔调整学习率，调整倍数为 gamma 倍，调整间隔为 step_size。间隔单位是step。需要注意的是， step 通常是指 epoch，不要弄成 iteration 了。
    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):#迭代次数
        train(args, model, device, train_loader, optimizer, epoch)
        test(args, model, device, test_loader)
        scheduler.step()#每次迭代之后调整学习率

    if args.save_model:#保存模型
        torch.save(model.state_dict(), "mnist_cnn.pt")


if __name__ == '__main__':
    main()