pytorch利用LeNet5和ResNet18训练Cifar10数据集

CIFAR10数据集

  该数据集共有60000张彩色图像，这些图像是32*32，分为10个类，每类6000张图。这里面有50000张用于训练，构成了5个训练批，每一批10000张图；另外10000用于测试，单独构成一批。测试批的数据里，取自10类中的每一类，每一类随机取1000张。抽剩下的就随机排列组成了训练批。注意一个训练批中的各类图像并不一定数量相同，总的来看训练批，每一类都有5000张图。需要说明的是，这10类都是各自独立的，不会出现重叠。

LeNet5网络

  LeNet5网络是一种用于手写体字符识别的非常高效的卷积神经网络。LeNet-5共有7层，不包含输入，每层都包含可训练参数；每个层有多个Feature Map，每个FeatureMap通过一种卷积滤波器提取输入的一种特征，然后每个FeatureMap有多个神经元。
  lenet5.py，代码如下：

import torch
from torch import nn


class Lenet5(nn.Module):
    """
    for cifar10 dataset.
    """

    def __init__(self):
        super(Lenet5, self).__init__()

        self.conv_unit = nn.Sequential(
            # x: [b, 3, 32, 32] => [b, 32, 5, 5]
            nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=0),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=0),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
        )
        # flatten
        # fc unit
        self.fc_unit = nn.Sequential(
            nn.Linear(32 * 5 * 5, 32),
            nn.ReLU(),
            nn.Linear(32, 10)
        )

        # [b, 3, 32, 32]
        tmp = torch.randn(2, 3, 32, 32)
        out = self.conv_unit(tmp)

        # [b, 32, 5, 5]
        print('conv out:', out.shape)

    def forward(self, x):
        """

        param x: [b, 3, 32, 32]
        return: logits: [b, 10]

        """
        # batchsz = b
        batchsz = x.size(0)

        # [b, 3, 32, 32] => [b, 32, 5, 5]
        x = self.conv_unit(x)

        # [b, 32, 5, 5] => [b, 32 * 5 * 5]
        x = x.view(batchsz, 32 * 5 * 5)

        # [b, 32 * 5 * 5] => [b, 10]
        logits = self.fc_unit(x)

        return logits


def main():
    net = Lenet5()
    tmp = torch.randn(2, 3, 32, 32)
    out = net(tmp)
    print('lenet out:', out.shape)

	# 查看模型结构
    print(net)

if __name__ == '__main__':
    main()

输出结果为：

conv out: torch.Size([2, 32, 5, 5])
lenet out: torch.Size([2, 10])
Lenet5(
  (conv_unit): Sequential(
    (0): Conv2d(3, 16, kernel_size=(5, 5), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc_unit): Sequential(
    (0): Linear(in_features=800, out_features=32, bias=True)
    (1): ReLU()
    (2): Linear(in_features=32, out_features=10, bias=True)
  )
)

ResNet18网络

pytorch画出

  resnet.py代码如下：

import torch
from torch import nn
from torch.nn import functional as F


class ResBlk(nn.Module):
    """
    resnet block
    """

    def __init__(self, ch_in, ch_out, stride=1):
        """

        :param ch_in:
        :param ch_out:
        """
        super(ResBlk, self).__init__()

        # we add stride support for resbok, which is distinct from tutorials.
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)

        self.extra = nn.Sequential()
        if ch_out != ch_in:
            # [b, ch_in, h, w] => [b, ch_out, h, w]
            self.extra = nn.Sequential(
                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
                nn.BatchNorm2d(ch_out)
            )

    def forward(self, x):
        """

        :param x: [b, ch, h, w]
        :return:
        """
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        # short cut.
        # extra module: [b, ch_in, h, w] => [b, ch_out, h, w]
        # element-wise add:
        out = self.extra(x) + out
        out = F.relu(out)

        return out


class ResNet18(nn.Module):

    def __init__(self):
        super(ResNet18, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(64)
        )
        # followed 4 blocks
        # [b, 64, h, w] => [b, 128, h ,w]
        self.blk1 = ResBlk(64, 128, stride=2)
        # [b, 128, h, w] => [b, 256, h, w]
        self.blk2 = ResBlk(128, 256, stride=2)
        # # [b, 256, h, w] => [b, 512, h, w]
        self.blk3 = ResBlk(256, 512, stride=2)
        # # [b, 512, h, w] => [b, 1024, h, w]
        self.blk4 = ResBlk(512, 512, stride=2)

        self.outlayer = nn.Linear(512 * 1 * 1, 10)

    def forward(self, x):
        """

        :param x:
        :return:
        """
        x = F.relu(self.conv1(x))

        # [b, 64, h, w] => [b, 1024, h, w]
        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)

        # print('after conv:', x.shape) #[b, 512, 2, 2]
        # [b, 512, h, w] => [b, 512, 1, 1]
        x = F.adaptive_avg_pool2d(x, [1, 1])
        # print('after pool:', x.shape)
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)

        return x


def main():
    blk = ResBlk(64, 128, stride=4)
    tmp = torch.randn(2, 64, 32, 32)
    out = blk(tmp)
    print('block:', out.shape)

    x = torch.randn(2, 3, 32, 32)
    model = ResNet18()
    out = model(x)
    print('resnet:', out.shape)

	# 查看模型结构
    print(blk)


if __name__ == '__main__':
    main()

结果：

block: torch.Size([2, 128, 8, 8])
resnet: torch.Size([2, 10])
ResBlk(
  (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(4, 4), padding=(1, 1))
  (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (extra): Sequential(
    (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(4, 4))
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
)

  main.py代码：

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from torch import nn, optim

from lenet5 import Lenet5
from resnet import ResNet18


def main():
    batchsz = 256

    cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ]), download=True)
    # DataLoader一次加载多张图片
    cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)

    cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ]), download=True)

    cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)

    x, label = iter(cifar_train).next()
    print('x:', x.shape, 'label:', label.shape)

    device = torch.device('cuda')
    model = Lenet5().to(device)
    # model = ResNet18().to(device)

    criteon = nn.CrossEntropyLoss().to(device)
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    print(model)

    for epoch in range(1000):

        # 训练
        model.train()
        for batchidx, (x, label) in enumerate(cifar_train):
            # [b, 3, 32, 32]
            # [b]
            x, label = x.to(device), label.to(device)

            logits = model(x)
            # logits: [b, 10]
            # label:  [b]
            # loss: tensor scalar
            loss = criteon(logits, label)

            # backprop
            optimizer.zero_grad()  # 先把梯度清零
            loss.backward()
            optimizer.step()

        print(epoch, 'loss:', loss.item())

        # 测试
        model.eval()

        # 测试是不需要计算梯度的，所以放在不需要计算梯度里面，这样节约资源和时间
        with torch.no_grad():
            # test
            total_correct = 0
            total_num = 0
            for x, label in cifar_test:
                # x: [b, 3, 32, 32]
                # label: [b]
                x, label = x.to(device), label.to(device)

                # [b, 10]
                logits = model(x)
                # [b]
                pred = logits.argmax(dim=1)
                # [b] vs [b] => scalar tensor
                correct = torch.eq(pred, label).float().sum().item()
                total_correct += correct
                total_num += x.size(0)
                # print(correct)

            acc = total_correct / total_num
            print(epoch, 'test acc:', acc)


if __name__ == '__main__':
    main()

实验部分结果：

Files already downloaded and verified
Files already downloaded and verified
x: torch.Size([256, 3, 32, 32]) label: torch.Size([256])
conv out: torch.Size([2, 32, 5, 5])
Lenet5(
  (conv_unit): Sequential(
    (0): Conv2d(3, 16, kernel_size=(5, 5), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc_unit): Sequential(
    (0): Linear(in_features=800, out_features=32, bias=True)
    (1): ReLU()
    (2): Linear(in_features=32, out_features=10, bias=True)
  )
)
0 loss: 1.4234744310379028
0 test acc: 0.5079
1 loss: 0.9590504765510559
1 test acc: 0.5635
2 loss: 1.1572571992874146
2 test acc: 0.578
3 loss: 1.0135480165481567
3 test acc: 0.6045

1、model.train()与model.eval()的用法

  在训练开始之前写上model.trian()，在测试时写上model.eval()。
  如果模型中有BN层(Batch Normalization）和Dropout，需要在训练时添加model.train()，在测试时添加model.eval()。其中model.train()是保证BN层用每一批数据的均值和方差，而model.eval()是保证BN用全部训练数据的均值和方差；而对于Dropout，model.train()是随机取一部分网络连接来训练更新参数，而model.eval()是利用到了所有网络连接。

2、Dropout

  dropout常常用于抑制过拟合，在pytorch中，dropout中的参数p是节点丢失概率，比如torch.nn.Dropout(0.4)，这里的0.4是指该层的神经元每次迭代训练时有40%的概率被丢失。而在tensorflow中，keras.layers.Dropout(0.4)，这里0.4是被保留的概率，所以每次迭代训练时有60%的概率被丢失。
  keras.layers.AlphaDropout(rate=0.4)，主流使用AlphaDropout：1、均值和方差不变 2、归一化性质也不变。