完整的pytorch 实现PATnet模型代码及训练代码

以下是PATNet模型的PyTorch实现代码及训练代码，其中使用了ImageNet数据集进行训练：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets, models

# 定义PATNet模型
class PATNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(PATNet, self).__init__()
        self.resnet = models.resnet50(pretrained=True)
        self.resnet.fc = nn.Linear(2048, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        return x

# 数据预处理
train_transforms = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

val_transforms = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载ImageNet数据集
train_data = datasets.ImageNet('path/to/train', split='train', transform=train_transforms)
val_data = datasets.ImageNet('path/to/val', split='val', transform=val_transforms)

# 定义训练参数
batch_size = 32
num_epochs = 10
learning_rate = 0.001

train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=True)

# 初始化模型和优化器
model = PATNet(num_classes=1000)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(num_epochs):
    model.train()
    train_loss = 0.0
    train_acc = 0.0

    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc.double() / len(train_loader.dataset)

    model.eval()
    val_loss = 0.0
    val_acc = 0.0

    for inputs, labels in val_loader:
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        val_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        val_acc += torch.sum(preds == labels.data)

    val_loss = val_loss / len(val_loader.dataset)
    val_acc = val_acc.double() / len(val_loader.dataset)

    print('Epoch: {} || Train Loss: {:.4f} || Train Acc: {:.4f} || Val Loss: {:.4f} || Val Acc: {:.4f}'.format(
        epoch+1, train_loss, train_acc, val_loss, val_acc))

在模型训练完成后，您可以使用保存的模型进行预测和推理。