DataWhale team punch learning camp task09-3 image classification Case 1

Image classification (CIFAR-10) on Kaggle
now, we will use in the preceding sections learned knowledge to participate in Kaggle contest, the contest was to solve the CIFAR-10 image classification. The competition website is https://www.kaggle.com/c/cifar-10

# 本节的网络需要较长的训练时间
# 可以在Kaggle访问：
# https://www.kaggle.com/boyuai/boyu-d2l-image-classification-cifar-10
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import os
import time

print("PyTorch Version: ",torch.__version__)#PyTorch Version:  1.3.0

Access and organize data sets

Game data into training and test sets. Training set contains 50,000 images. Test set contains 300,000 images. Two data sets are image format PNG, are 32 pixels height and width, and having three color channels (RGB). The image covers 10 categories: airplanes, cars, birds, cats, deer, dogs, frogs, horses, boats and trucks. To make it easier to get started, we offer a small sample of the above data sets. "Train_tiny.zip" contains 80 training samples, while "test_tiny.zip" contains 100 test samples. They uncompressed folder names are "train_tiny" and "test_tiny".

Image Enhancement

data_transform = transforms.Compose([
    transforms.Resize(40),
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32),
    transforms.ToTensor()
])
trainset = torchvision.datasets.ImageFolder(root='/home/kesci/input/CIFAR102891/cifar-10/train'
                                            , transform=data_transform)

trainset[0][0].shape

torch.Size([3, 32, 32])

data = [d[0].data.cpu().numpy() for d in trainset]
np.mean(data)

0.4676536

np.std(data)

0.23926772

# 图像增强
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),  #先四周填充0，再把图像随机裁剪成32*32
    transforms.RandomHorizontalFlip(),  #图像一半的概率翻转，一半的概率不翻转
    transforms.ToTensor(),
    transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)), #R,G,B每层的归一化用到的均值和方差
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4731, 0.4822, 0.4465), (0.2212, 0.1994, 0.2010)),
])

Import data set

train_dir = '/home/kesci/input/CIFAR102891/cifar-10/train'
test_dir = '/home/kesci/input/CIFAR102891/cifar-10/test'

trainset = torchvision.datasets.ImageFolder(root=train_dir, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=256, shuffle=True)

testset = torchvision.datasets.ImageFolder(root=test_dir, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=256, shuffle=False)

classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'forg', 'horse', 'ship', 'truck']

Definition Model

ResNet-18 network architecture: ResNet full name Residual Network residuals network. Kaiming He's "Deep Residual Learning for Image Recognition" won the best paper CVPR. He proposed depth of residual network in 2015 can be said to wash the major aspects of the game image, overwhelmingly we made a number of championship. And it in the network to ensure accuracy of the premise, the depth of the network layer reached 152, then further added to a depth of 1000.

class ResidualBlock(nn.Module):   # 我们定义网络时一般是继承的torch.nn.Module创建新的子类

    def __init__(self, inchannel, outchannel, stride=1):
        super(ResidualBlock, self).__init__()
        #torch.nn.Sequential是一个Sequential容器，模块将按照构造函数中传递的顺序添加到模块中。
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False), 
            # 添加第一个卷积层,调用了nn里面的Conv2d（）
            nn.BatchNorm2d(outchannel), # 进行数据的归一化处理
            nn.ReLU(inplace=True), # 修正线性单元，是一种人工神经网络中常用的激活函数
            nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.shortcut = nn.Sequential() 
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )
        #  便于之后的联合,要判断Y = self.left(X)的形状是否与X相同

    def forward(self, x): # 将两个模块的特征进行结合，并使用ReLU激活函数得到最终的特征。
        out = self.left(x)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, ResidualBlock, num_classes=10):
        super(ResNet, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential( # 用3个3x3的卷积核代替7x7的卷积核，减少模型参数
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        ) 
        self.layer1 = self.make_layer(ResidualBlock, 64,  2, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)   #第一个ResidualBlock的步幅由make_layer的函数参数stride指定
        # ，后续的num_blocks-1个ResidualBlock步幅是1
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out


def ResNet18():
    return ResNet(ResidualBlock)

Training and Testing

# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 超参数设置
EPOCH = 20   #遍历数据集次数
pre_epoch = 0  # 定义已经遍历数据集的次数
LR = 0.1        #学习率

# 模型定义-ResNet
net = ResNet18().to(device)

# 定义损失函数和优化方式
criterion = nn.CrossEntropyLoss()  #损失函数为交叉熵，多用于多分类问题
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4) 
#优化方式为mini-batch momentum-SGD，并采用L2正则化（权重衰减）

# 训练
if __name__ == "__main__":
    print("Start Training, Resnet-18!")
    num_iters = 0
    for epoch in range(pre_epoch, EPOCH):
        print('\nEpoch: %d' % (epoch + 1))
        net.train()
        sum_loss = 0.0
        correct = 0.0
        total = 0
        for i, data in enumerate(trainloader, 0): 
            #用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标，
            #下标起始位置为0，返回 enumerate(枚举) 对象。
            
            num_iters += 1
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
            optimizer.zero_grad()  # 清空梯度

            # forward + backward
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            sum_loss += loss.item() * labels.size(0)
            _, predicted = torch.max(outputs, 1) #选出每一列中最大的值作为预测结果
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
            # 每20个batch打印一次loss和准确率
            if (i + 1) % 20 == 0:
                print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% '
                        % (epoch + 1, num_iters, sum_loss / (i + 1), 100. * correct / total))

    print("Training Finished, TotalEPOCH=%d" % EPOCH)