pytorch人脸识别消融代码

以下是使用PyTorch实现人脸识别消融实验的示例代码：

1. 导入必要的库

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.autograd import Variable
import numpy as np
import os
import time
import argparse
from PIL import Image

2. 定义模型

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.conv4 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc1 = nn.Sequential(
            nn.Linear(256 * 7 * 7, 1024),
            nn.Dropout(0.5),
            nn.ReLU(inplace=True))
        self.fc2 = nn.Sequential(
            nn.Linear(1024, 512),
            nn.Dropout(0.5),
            nn.ReLU(inplace=True))
        self.fc3 = nn.Linear(512, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

3. 数据预处理

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])])

4. 加载数据集

data_dir = 'data'
train_dir = os.path.join(data_dir, 'train')
test_dir = os.path.join(data_dir, 'test')

train_data = torchvision.datasets.ImageFolder(train_dir, transform=transform)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4)

test_data = torchvision.datasets.ImageFolder(test_dir, transform=transform)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=True, num_workers=4)

5. 定义训练函数

def train(model, train_loader, criterion, optimizer, epoch):
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images)
        labels = Variable(labels)
        if torch.cuda.is_available():
            images = images.cuda()
            labels = labels.cuda()
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i+1) % 50 == 0:
            print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
                   %(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

6. 定义测试函数

def test(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = Variable(images)
        if torch.cuda.is_available():
            images = images.cuda()
            labels = labels.cuda()
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
    print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))

7. 训练模型

num_epochs = 10
model = CNN()
if torch.cuda.is_available():
    model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    train(model, train_loader, criterion, optimizer, epoch)
    test(model, test_loader)

在这个示例中，我们使用了一个具有4个卷积层和3个全连接层的卷积神经网络，使用PyTorch中的CrossEntropyLoss作为损失函数，使用Adam优化器进行训练。我们还定义了一个数据预处理函数，将图像调整为指定的大小，将每个像素的值标准化到0到1之间，并减去每种颜色通道的均值和标准差。最后，我们加载包含训练和测试图像的文件夹，并使用DataLoader对象进行批处理和数据增强。