掌握如何用Pytorch实现一个分类器

Posted 2022-11-11 黑马程序员官方

学习目标

• 了解分类器的任务和数据样式
• 掌握如何用Pytorch实现一个分类器

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分类器任务和数据介绍

• 构造一个将不同图像进行分类的神经网络分类器, 对输入的图片进行判别并完成分类.
• 本案例采用CIFAR10数据集作为原始图片数据.

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• CIFAR10数据集介绍: 数据集中每张图片的尺寸是3 * 32 * 32, 代表彩色3通道

• CIFAR10数据集总共有10种不同的分类, 分别是"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck".

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• CIFAR10数据集的样例如下图所示:

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训练分类器的步骤

• 1: 使用torchvision下载CIFAR10数据集
• 2: 定义卷积神经网络
• 3: 定义损失函数
• 4: 在训练集上训练模型
• 5: 在测试集上测试模型

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• 1: 使用torchvision下载CIFAR10数据集

• 导入torchvision包来辅助下载数据集

import torch
import torchvision
import torchvision.transforms as transforms

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• 下载数据集并对图片进行调整, 因为torchvision数据集的输出是PILImage格式, 数据域在[0, 1]. 我们将其转换为标准数据域[-1, 1]的张量格式.

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

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• 输出结果:

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified

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• 注意:
  • 如果你是在Windows系统下运行上述代码, 并且出现报错信息 "BrokenPipeError", 可以尝试将torch.utils.data.DataLoader()中的num_workers设置为0.

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• 展示若干训练集的图片

# 导入画图包和numpy
import matplotlib.pyplot as plt
import numpy as np

# 构建展示图片的函数
def imshow(img):
    img = img / 2 + 0.5
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# 从数据迭代器中读取一张图片
dataiter = iter(trainloader)
images, labels = dataiter.next()

# 展示图片
imshow(torchvision.utils.make_grid(images))
# 打印标签label
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

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• 输出图片结果:

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• 输出标签结果:

bird truck   cat   cat

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• 2: 定义卷积神经网络

• 仿照2.1节中的类来构造此处的类, 唯一的区别是此处采用3通道3-channel

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


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

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• 3: 定义损失函数

• 采用交叉熵损失函数和随机梯度下降优化器.

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

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• 4: 在训练集上训练模型

• 采用基于梯度下降的优化算法, 都需要很多个轮次的迭代训练.

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # data中包含输入图像张量inputs, 标签张量labels
        inputs, labels = data

        # 首先将优化器梯度归零
        optimizer.zero_grad()

        # 输入图像张量进网络, 得到输出张量outputs
        outputs = net(inputs)

        # 利用网络的输出outputs和标签labels计算损失值
        loss = criterion(outputs, labels)

        # 反向传播+参数更新, 是标准代码的标准流程
        loss.backward()
        optimizer.step()

        # 打印轮次和损失值
        running_loss += loss.item()
        if (i + 1) % 2000 == 0:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

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• 输出结果:

[1,  2000] loss: 2.227
[1,  4000] loss: 1.884
[1,  6000] loss: 1.672
[1,  8000] loss: 1.582
[1, 10000] loss: 1.526
[1, 12000] loss: 1.474
[2,  2000] loss: 1.407
[2,  4000] loss: 1.384
[2,  6000] loss: 1.362
[2,  8000] loss: 1.341
[2, 10000] loss: 1.331
[2, 12000] loss: 1.291
Finished Training

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• 保存模型:

# 首先设定模型的保存路径
PATH = './cifar_net.pth'
# 保存模型的状态字典
torch.save(net.state_dict(), PATH)

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• 5: 在测试集上测试模型

• 第一步, 展示测试集中的若干图片

dataiter = iter(testloader)
images, labels = dataiter.next()

# 打印原始图片
imshow(torchvision.utils.make_grid(images))
# 打印真实的标签
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

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• 输出图片结果:

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• 输出标签结果:

GroundTruth:    cat  ship  ship plane

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• 第二步, 加载模型并对测试图片进行预测

# 首先实例化模型的类对象
net = Net()
# 加载训练阶段保存好的模型的状态字典
net.load_state_dict(torch.load(PATH))

# 利用模型对图片进行预测
outputs = net(images)

# 共有10个类别, 采用模型计算出的概率最大的作为预测的类别
_, predicted = torch.max(outputs, 1)

# 打印预测标签的结果
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))

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• 输出结果:

Predicted:    cat  ship  ship plane

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• 接下来看一下在全部测试集上的表现

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

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• 输出结果:

Accuracy of the network on the 10000 test images: 53 %

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• 分析结果: 对于拥有10个类别的数据集, 随机猜测的准确率是10%, 模型达到了53%, 说明模型学到了真实的东西.

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• 为了更加细致的看一下模型在哪些类别上表现更好, 在哪些类别上表现更差, 我们分类别的进行准确率计算.

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

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• 输出结果:

Accuracy of plane : 62 %
Accuracy of   car : 62 %
Accuracy of  bird : 45 %
Accuracy of   cat : 36 %
Accuracy of  deer : 52 %
Accuracy of   dog : 25 %
Accuracy of  frog : 69 %
Accuracy of horse : 60 %
Accuracy of  ship : 70 %
Accuracy of truck : 48 %

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在GPU上训练模型

• 为了真正利用Pytorch中Tensor的优秀属性, 加速模型的训练, 我们可以将训练过程转移到GPU上进行.

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• 首先要定义设备, 如果CUDA是可用的则被定义成GPU, 否则被定义成CPU.

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

print(device)

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• 输出结果:

cuda:0

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• 当训练模型的时候, 只需要将模型转移到GPU上, 同时将输入的图片和标签页转移到GPU上即可.

# 将模型转移到GPU上
net.to(device)

# 将输入的图片张量和标签张量转移到GPU上
inputs, labels = data[0].to(device), data[1].to(device)