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022/PyTorch-Image-Classification

/README.md
import torch
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import argparse

class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer3 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer4 = nn.Sequential(
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.fc1 = nn.Linear(in_features=512*7*7, out_features=4096)
self.fc2 = nn.Linear(in_features=4096, out_features=1024)
self.fc3 = nn.Linear(in_features=1024, out_features=10)
self.dropout = nn.Dropout(0.5)

def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = out.reshape(out.size(0), -1)
out = self.fc1(out)
out = self.dropout(out)
out = self.fc2(out)
out = self.dropout(out)
out = self.fc3(out)
return out

# 损失函数
def loss_fn(outputs, targets):
return nn.CrossEntropyLoss()(outputs, targets)

# 数据预处理
transform = transforms.Compose(
[
transforms.Resize((32, 32)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation(degrees=15),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
]
)

# 加载训练集和测试集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False)

# 定义训练过程
def train(model, criterion, optimizer, trainloader, epochs):
model.train()
for epoch in range(epochs):
running_loss = 0.0
for images, labels in trainloader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader)}')

# 定义测试过程
def test(model, criterion, testloader):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in testloader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Test Accuracy: {100 * correct / total:.2f} %')

# 优化器和损失函数
optimizer = optim.Adam(CNN().parameters(), lr=0.001)
criterion = loss_fn

# 训练模型
epochs = 10
train(CNN(), criterion, optimizer, trainloader, epochs)

# 测试模型
test(CNN(), criterion, testloader)

/README_CN.md
import torch
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import argparse

def load_data():
# 数据预处理
transform = transforms.Compose(
[
transforms.Resize((32, 32)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation(degrees=15),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
]
)

# 加载训练集和测试集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)