删除无用样例
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import argparse
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import torch
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import os
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from torch import optim
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from torch.optim import lr_scheduler
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from fed_example.utils.data_utils import get_data
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from fed_example.utils.model_utils import get_model
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from fed_example.utils.train_utils import train_model, validate_model, v3_update_model_weights
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def main(args):
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device = torch.device(args.device)
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# 数据加载器
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loader1, loader2, loader3, subset_len, val_loader = get_data(
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args.train_path, args.val_path, args.batch_size, args.number_workers
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)
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# 模型 get_model(name='ResNet', number_class=2, device=device, resnet_type='resnet18')
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model_a = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
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model_b = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
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model_c = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
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# 添加全局模型
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global_model = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
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if args.resume_training:
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model_a.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_a.pth')))
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model_b.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_b.pth')))
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model_c.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_c.pth')))
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print("已加载之前保存的模型参数继续训练")
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# 优化器和损失函数
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criterion = torch.nn.BCEWithLogitsLoss().to(device)
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optimizer_a = optim.Adam(model_a.parameters(), lr=args.lr, weight_decay=5e-4)
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optimizer_b = optim.Adam(model_b.parameters(), lr=args.lr, weight_decay=5e-4)
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optimizer_c = optim.Adam(model_c.parameters(), lr=args.lr, weight_decay=5e-4)
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scheduler_a = lr_scheduler.ReduceLROnPlateau(optimizer_a, mode='min', factor=0.5, patience=2, verbose=True)
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scheduler_b = lr_scheduler.ReduceLROnPlateau(optimizer_b, mode='min', factor=0.5, patience=2, verbose=True)
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scheduler_c = lr_scheduler.ReduceLROnPlateau(optimizer_c, mode='min', factor=0.5, patience=2, verbose=True)
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# 初始化最优验证损失和模型路径
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best_val_loss_a = float('inf')
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best_val_loss_b = float('inf')
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best_val_loss_c = float('inf')
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save_dir = args.save_dir
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os.makedirs(save_dir, exist_ok=True)
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# 训练与验证
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for epoch in range(args.epochs):
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print(f'Epoch {epoch + 1}/{args.epochs}')
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# 训练模型
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loss_a = train_model(device, model_a, loader1, optimizer_a, criterion, epoch, 'model_a')
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loss_b = train_model(device, model_b, loader2, optimizer_b, criterion, epoch, 'model_b')
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loss_c = train_model(device, model_c, loader3, optimizer_c, criterion, epoch, 'model_c')
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# 验证模型
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val_loss_a, val_acc_a, val_auc_a = validate_model(device, model_a, val_loader, criterion, epoch, 'model_a')
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val_loss_b, val_acc_b, val_auc_b = validate_model(device, model_b, val_loader, criterion, epoch, 'model_b')
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val_loss_c, val_acc_c, val_auc_c = validate_model(device, model_c, val_loader, criterion, epoch, 'model_c')
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if args.save_model and val_loss_a < best_val_loss_a:
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best_val_loss_a = val_loss_a
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torch.save(model_a.state_dict(), os.path.join(save_dir, 'best_model_a.pth'))
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print(f"Best model_a saved with val_loss: {best_val_loss_a:.4f}")
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if args.save_model and val_loss_b < best_val_loss_b:
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best_val_loss_b = val_loss_b
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torch.save(model_b.state_dict(), os.path.join(save_dir, 'best_model_b.pth'))
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print(f"Best model_b saved with val_loss: {best_val_loss_b:.4f}")
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if args.save_model and val_loss_c < best_val_loss_c:
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best_val_loss_c = val_loss_c
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torch.save(model_c.state_dict(), os.path.join(save_dir, 'best_model_c.pth'))
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print(f"Best model_c saved with val_loss: {best_val_loss_c:.4f}")
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print(
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f'Model A - Loss: {loss_a:.4f}, Val Loss: {val_loss_a:.4f}, Val Acc: {val_acc_a:.4f}, AUC: {val_auc_a:.4f}')
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print(
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f'Model B - Loss: {loss_b:.4f}, Val Loss: {val_loss_b:.4f}, Val Acc: {val_acc_b:.4f}, AUC: {val_auc_b:.4f}')
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print(
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f'Model C - Loss: {loss_c:.4f}, Val Loss: {val_loss_c:.4f}, Val Acc: {val_acc_c:.4f}, AUC: {val_auc_c:.4f}')
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# 更新模型 A 的权重，每 3 轮 1
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val_acc_a, val_auc_a, val_acc_a_threshold = v3_update_model_weights(
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epoch=epoch,
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model_to_update=model_a,
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other_models=[model_a, model_b, model_c],
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global_model=global_model,
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losses=[loss_a, loss_b, loss_c],
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val_loader=val_loader,
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device=device,
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val_auc_threshold=val_auc_a,
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validate_model=validate_model,
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criterion=criterion,
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update_frequency=1
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)
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# 更新模型 B 的权重，每 5 轮1
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val_acc_b, val_auc_b, val_acc_b_threshold = v3_update_model_weights(
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epoch=epoch,
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model_to_update=model_b,
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other_models=[model_a, model_b, model_c],
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global_model=global_model,
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losses=[loss_a, loss_b, loss_c],
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val_loader=val_loader,
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device=device,
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val_auc_threshold=val_auc_b,
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validate_model=validate_model,
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criterion=criterion,
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update_frequency=1
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)
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# 更新模型 C 的权重，每 2 轮 1
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val_acc_c, val_auc_c, val_acc_c_threshold = v3_update_model_weights(
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epoch=epoch,
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model_to_update=model_c,
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other_models=[model_a, model_b, model_c],
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global_model=global_model,
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losses=[loss_a, loss_b, loss_c],
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val_loader=val_loader,
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device=device,
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val_auc_threshold=val_auc_c,
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validate_model=validate_model,
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criterion=criterion,
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update_frequency=1
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)
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print("Training complete! Best models saved.")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_name', type=str, default='resnet18_psa', help='Model name')
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parser.add_argument('--deep_backbone', type=str, default='*', help='deeplab backbone')
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parser.add_argument('--train_path', type=str, default='/media/terminator/实验&代码/yhs/FF++/c40/total/train')
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parser.add_argument('--val_path', type=str, default='/media/terminator/实验&代码/yhs/FF++/c40/total/val')
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# parser.add_argument('--train_path', type=str, default='/media/terminator/实验&代码/yhs/FF++_mask_sample/c23/df/train')
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# parser.add_argument('--val_path', type=str, default='/media/terminator/实验&代码/yhs/FF++_mask_sample/c23/df/val')
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parser.add_argument('--epochs', type=int, default=10)
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parser.add_argument('--batch_size', type=int, default=16)
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parser.add_argument('--number_workers', type=int, default=8)
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parser.add_argument('--number_class', type=int, default=1)
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parser.add_argument('--device', type=str, default='cuda:0')
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parser.add_argument('--lr', type=float, default=0.00005)
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parser.add_argument('--save_dir', type=str,
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default='/media/terminator/实验&代码/yhs/output/work2/resnet18_psa/c40/total/e10',
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help='Directory to save best models')
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parser.add_argument('--save_model', type=bool, default=True, help='是否保存最优模型')
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parser.add_argument('--resume_training', type=bool, default=False, help='是否从保存的模型参数继续训练')
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args = parser.parse_args()
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main(args)
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import os
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from collections import Counter
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import torch
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from PIL import Image
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from sklearn.model_selection import train_test_split
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from torch.utils.data import DataLoader
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from torch.utils.data import Dataset, random_split
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from torchvision import transforms, datasets
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class CustomImageDataset(Dataset):
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def __init__(self, root_dir, transform=None):
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self.root_dir = root_dir
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self.transform = transform
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self.image_paths = []
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self.labels = []
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# 遍历 root_dir 下的子文件夹 0 和 1
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for label in [0, 1]:
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folder_path = os.path.join(root_dir, str(label))
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if os.path.isdir(folder_path):
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for img_name in os.listdir(folder_path):
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img_path = os.path.join(folder_path, img_name)
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self.image_paths.append(img_path)
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self.labels.append(label)
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# 打印用于调试的图像路径和标签
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# print("Loaded image paths and labels:")
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# for path, label in zip(self.image_paths[:5], self.labels[:5]):
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# print(f"Path: {path}, Label: {label}")
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# print(f"Total samples: {len(self.image_paths)}\n")
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def __len__(self):
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return len(self.image_paths)
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def __getitem__(self, idx):
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img_path = self.image_paths[idx]
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label = self.labels[idx]
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image = Image.open(img_path).convert("RGB")
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if self.transform:
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image = self.transform(image)
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return image, label
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def get_test_data(test_image_path, batch_size, nw):
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data_transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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# test_dataset = datasets.ImageFolder(root=test_image_path, transform=data_transform)
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test_dataset = CustomImageDataset(root_dir=test_image_path, transform=data_transform)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=nw)
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return test_loader
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def get_Onedata(train_image_path, val_image_path, batch_size, num_workers):
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"""
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加载完整的训练数据集和验证数据集。
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"""
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data_transform = {
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"train": transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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"val": transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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# 创建训练和验证数据集
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train_dataset = CustomImageDataset(root_dir=train_image_path, transform=data_transform["train"])
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val_dataset = CustomImageDataset(root_dir=val_image_path, transform=data_transform["val"])
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# 创建数据加载器
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
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return train_loader, val_loader
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def get_data(train_image_path, val_image_path, batch_size, num_workers):
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data_transform = {
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"train": transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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"val": transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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"test": transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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train_dataset = CustomImageDataset(root_dir=train_image_path, transform=data_transform["train"])
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val_dataset = CustomImageDataset(root_dir=val_image_path, transform=data_transform["val"])
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# 切分数据集为三个等分
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train_len = (len(train_dataset) // 3) * 3
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train_dataset_truncated = torch.utils.data.Subset(train_dataset, range(train_len))
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subset_len = train_len // 3
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dataset1, dataset2, dataset3 = random_split(train_dataset_truncated, [subset_len] * 3)
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loader1 = DataLoader(dataset1, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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loader2 = DataLoader(dataset2, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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loader3 = DataLoader(dataset3, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
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return loader1, loader2, loader3, subset_len, val_loader
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def get_Fourdata(train_path, val_path, batch_size, num_workers):
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"""
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加载训练集和验证集。
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包括 4 个客户端验证集（df、f2f、fs、nt）和 1 个全局验证集。
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Args:
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train_path (str): 训练数据路径
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val_path (str): 验证数据路径
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batch_size (int): 批量大小
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num_workers (int): DataLoader 的工作线程数
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Returns:
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tuple: 包含 4 个客户端训练和验证加载器，以及全局验证加载器
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"""
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# 数据预处理
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train_transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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val_transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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# 定义 4 个客户端数据集路径
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client_names = ['df', 'f2f', 'fs', 'nt']
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client_train_loaders = []
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client_val_loaders = []
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for client in client_names:
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client_train_path = os.path.join(train_path, client)
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client_val_path = os.path.join(val_path, client)
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# 加载客户端训练数据
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train_dataset = datasets.ImageFolder(root=client_train_path, transform=train_transform)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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# 加载客户端验证数据
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val_dataset = datasets.ImageFolder(root=client_val_path, transform=val_transform)
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val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
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client_train_loaders.append(train_loader)
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client_val_loaders.append(val_loader)
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# 全局验证集
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global_val_dataset = datasets.ImageFolder(root=val_path, transform=val_transform)
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global_val_loader = DataLoader(global_val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
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return (*client_train_loaders, *client_val_loaders, global_val_loader)
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def get_federated_data(train_path, val_path, num_clients=3, batch_size=16, num_workers=8):
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"""
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将数据集划分为多个客户端，每个客户端拥有独立的训练和验证数据。
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"""
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# 加载完整数据集
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full_train_dataset = CustomImageDataset(root_dir=train_path, transform=get_transform("train"))
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full_val_dataset = CustomImageDataset(root_dir=val_path, transform=get_transform("val"))
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# 划分客户端训练集
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client_train_datasets = random_split(full_train_dataset, [len(full_train_dataset) // num_clients] * num_clients)
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# 创建客户端数据加载器
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client_train_loaders = [
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DataLoader(ds, batch_size=batch_size, shuffle=True, num_workers=num_workers)
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for ds in client_train_datasets
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]
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# 全局验证集
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global_val_loader = DataLoader(full_val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
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return client_train_loaders, global_val_loader
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def main():
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# 设置参数
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train_image_path = "/media/terminator/实验&代码/yhs/FF++_mask/c23/f2f/train"
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val_image_path = "/media/terminator/实验&代码/yhs/FF++_mask/c23/f2f/val"
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batch_size = 4
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num_workers = 2
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# 获取数据加载器
|
||||
loader1, loader2, loader3, subset_len, val_loader = get_data(train_image_path, val_image_path, batch_size,
|
||||
num_workers)
|
||||
|
||||
# 统计标签数量和类型
|
||||
train_labels = []
|
||||
for dataset in [loader1, loader2, loader3]:
|
||||
for _, labels in dataset:
|
||||
train_labels.extend(labels.tolist())
|
||||
|
||||
val_labels = []
|
||||
for _, labels in val_loader:
|
||||
val_labels.extend(labels.tolist())
|
||||
|
||||
# 使用 Counter 统计标签数量
|
||||
train_label_counts = Counter(train_labels)
|
||||
val_label_counts = Counter(val_labels)
|
||||
|
||||
# 打印统计结果
|
||||
print("Training Dataset - Label Counts:", train_label_counts)
|
||||
print("Validation Dataset - Label Counts:", val_label_counts)
|
||||
print("Label Types in Training:", set(train_labels))
|
||||
print("Label Types in Validation:", set(val_labels))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,65 +0,0 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
from torchvision import models
|
||||
|
||||
from Deeplab.deeplab import DeepLab_F
|
||||
from Deeplab.resnet_psa import BasicBlockWithPSA
|
||||
from Deeplab.resnet_psa_v2 import ResNet
|
||||
from model_base.efNet_base_model import DeepLab
|
||||
from model_base.efficientnet import EfficientNet
|
||||
from model_base.resnet_more import CustomResNet
|
||||
from model_base.xcption import Xception
|
||||
|
||||
|
||||
def get_model(name, number_class, device, backbone):
|
||||
"""
|
||||
根据指定的模型名称加载模型，并根据任务类别数调整最后的分类层。
|
||||
|
||||
Args:
|
||||
name (str): 模型名称 ('Vgg', 'ResNet', 'EfficientNet', 'Xception')。
|
||||
number_class (int): 分类类别数。
|
||||
device (torch.device): 设备 ('cuda' or 'cpu')。
|
||||
resnet_type (str): ResNet类型 ('resnet18', 'resnet34', 'resnet50', 'resnet101', etc.)。
|
||||
|
||||
Returns:
|
||||
nn.Module: 经过修改的模型。
|
||||
"""
|
||||
if name == 'Vgg':
|
||||
model = models.vgg16_bn(pretrained=True).to(device)
|
||||
model.classifier[6] = nn.Linear(model.classifier[6].in_features, number_class)
|
||||
elif name == 'ResNet18':
|
||||
model = CustomResNet(resnet_type='resnet18', num_classes=number_class, pretrained=True).to(device)
|
||||
elif name == 'ResNet34':
|
||||
model = CustomResNet(resnet_type='resnet34', num_classes=number_class, pretrained=True).to(device)
|
||||
elif name == 'ResNet50':
|
||||
model = CustomResNet(resnet_type='resnet50', num_classes=number_class, pretrained=True).to(device)
|
||||
elif name == 'ResNet101':
|
||||
model = CustomResNet(resnet_type='resnet101', num_classes=number_class, pretrained=True).to(device)
|
||||
elif name == 'ResNet152':
|
||||
model = CustomResNet(resnet_type='resnet152', num_classes=number_class, pretrained=True).to(device)
|
||||
elif name == 'EfficientNet':
|
||||
# 使用自定义的 DeepLab 类加载 EfficientNet
|
||||
model = DeepLab(backbone='efficientnet', num_classes=number_class).to(device)
|
||||
elif name == 'Xception':
|
||||
model = Xception(
|
||||
in_planes=3,
|
||||
num_classes=number_class,
|
||||
pretrained=True,
|
||||
pretrained_path="/home/terminator/1325/yhs/fedLeaning/pre_model/xception-43020ad28.pth"
|
||||
).to(device)
|
||||
elif name == 'DeepLab':
|
||||
# 使用自定义的 DeepLab 类加载 EfficientNet
|
||||
model = DeepLab_F(num_classes=1, backbone=backbone).to(device)
|
||||
elif name == 'resnet18_psa':
|
||||
model = ResNet(BasicBlockWithPSA, [2, 2, 2, 2], number_class)
|
||||
else:
|
||||
raise ValueError(f"Model {name} is not supported.")
|
||||
return model
|
||||
|
||||
def get_federated_model(device):
|
||||
"""初始化客户端模型和全局模型"""
|
||||
client_models = [
|
||||
get_model("resnet18_psa", 1, device, "*") for _ in range(3)
|
||||
]
|
||||
global_model = get_model("resnet18_psa", 1, device, "*")
|
||||
return client_models, global_model
|
||||
@ -1,370 +0,0 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
from sklearn.metrics import roc_auc_score, accuracy_score
|
||||
import copy
|
||||
import torch.nn.functional as F
|
||||
import random
|
||||
|
||||
|
||||
def train_deepmodel(device, model, loader, optimizer, criterion, epoch, model_name):
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
corrects = 0.0
|
||||
alpha = 1
|
||||
beta = 0.1
|
||||
for inputs, labels in tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch'):
|
||||
inputs, labels = inputs.float().to(device), labels.to(device) # 确保数据在 device 上
|
||||
optimizer.zero_grad()
|
||||
|
||||
outputs, re_img = model(inputs)
|
||||
loss = criterion(outputs.squeeze(), labels.float())
|
||||
loss_F1 = F.l1_loss(re_img, inputs)
|
||||
loss = alpha * loss + beta * loss_F1
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
running_loss += loss.item()
|
||||
|
||||
avg_loss = running_loss / len(loader)
|
||||
print(f'{model_name} Training Loss: {avg_loss:.4f}')
|
||||
return avg_loss
|
||||
|
||||
|
||||
def validate_deepmodel(device, model, loader, criterion, epoch, model_name):
|
||||
model.eval()
|
||||
running_loss = 0.0
|
||||
correct, total = 0, 0
|
||||
all_labels, all_preds = [], []
|
||||
val_corrects = 0.0
|
||||
alpha = 1
|
||||
beta = 0.1
|
||||
|
||||
with torch.no_grad():
|
||||
for inputs, labels in tqdm(loader, desc=f'Validating {model_name} Epoch {epoch + 1}', unit='batch'):
|
||||
inputs, labels = inputs.float().to(device), labels.to(device) # 确保数据在 device 上
|
||||
|
||||
outputs, re_img = model(inputs)
|
||||
|
||||
# 将 logits 转换为预测
|
||||
predicted = torch.sigmoid(outputs).data
|
||||
all_preds.extend(predicted.cpu().numpy())
|
||||
all_labels.extend(labels.cpu().numpy())
|
||||
|
||||
# loss = criterion(outputs.squeeze(), labels.float())
|
||||
loss = criterion(outputs.squeeze(), labels.float())
|
||||
loss_F1 = F.l1_loss(re_img, inputs)
|
||||
loss = alpha * loss + beta * loss_F1
|
||||
running_loss += loss.item()
|
||||
|
||||
auc = roc_auc_score(all_labels, all_preds)
|
||||
predicted_labels = (np.array(all_preds) >= 0.5).astype(int) # 确保转换为 NumPy 数组
|
||||
acc = accuracy_score(all_labels, predicted_labels)
|
||||
avg_loss = running_loss / len(loader)
|
||||
print(f'{model_name} Validation Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}, AUC: {auc:.4f}')
|
||||
return avg_loss, acc, auc
|
||||
|
||||
|
||||
def test_deepmodel(device, model, loader):
|
||||
model.eval()
|
||||
all_labels, all_preds = [], []
|
||||
|
||||
with torch.no_grad():
|
||||
for inputs, labels in tqdm(loader, desc=f'Testing', unit='batch'):
|
||||
inputs, labels = inputs.float().to(device), labels.to(device) # 确保数据在 device 上
|
||||
outputs, re_img = model(inputs)
|
||||
predicted = torch.sigmoid(outputs).data # 将 logits 转换为预测
|
||||
|
||||
# 收集预测值和真实标签
|
||||
all_preds.extend(predicted.cpu().numpy())
|
||||
all_labels.extend(labels.cpu().numpy())
|
||||
|
||||
# 将预测值转换为二值标签
|
||||
predicted_labels = (np.array(all_preds) >= 0.5).astype(int)
|
||||
|
||||
# 计算准确率和AUC
|
||||
acc = accuracy_score(all_labels, predicted_labels)
|
||||
auc = roc_auc_score(all_labels, all_preds)
|
||||
|
||||
print(f'Test Accuracy: {acc:.4f}, Test AUC: {auc:.4f}')
|
||||
return acc, auc
|
||||
|
||||
|
||||
# def train_model(device, model, loader, optimizer, criterion, epoch, model_name):
|
||||
# model.train()
|
||||
# running_loss = 0.0
|
||||
# for i, (inputs, labels) in enumerate(tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch')):
|
||||
# inputs, labels = inputs.float().to(device), labels.float().to(device) # 确保数据格式正确
|
||||
# optimizer.zero_grad()
|
||||
#
|
||||
# outputs = model(inputs)
|
||||
# loss = criterion(outputs.squeeze(), labels)
|
||||
#
|
||||
# # 随机打印部分输出和标签，检查格式
|
||||
# if i % 10 == 0: # 每100个批次打印一次
|
||||
# print(f"Batch {i} - Sample Output: {outputs[0].item():.4f}, Sample Label: {labels[0].item()}")
|
||||
#
|
||||
# # 检查损失值是否异常
|
||||
# if loss.item() < 0:
|
||||
# print(f"Warning: Negative loss detected at batch {i}. Loss: {loss.item()}")
|
||||
#
|
||||
# loss.backward()
|
||||
# optimizer.step()
|
||||
#
|
||||
# running_loss += loss.item()
|
||||
#
|
||||
# avg_loss = running_loss / len(loader)
|
||||
# print(f'{model_name} Training Loss: {avg_loss:.4f}')
|
||||
# return avg_loss
|
||||
|
||||
def train_model(device, model, loader, optimizer, criterion, epoch, model_name):
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
for inputs, labels in tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch'):
|
||||
inputs, labels = inputs.float().to(device), labels.to(device) # 确保数据在 device 上
|
||||
optimizer.zero_grad()
|
||||
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs.squeeze(), labels.float())
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
running_loss += loss.item()
|
||||
|
||||
avg_loss = running_loss / len(loader)
|
||||
print(f'{model_name} Training Loss: {avg_loss:.4f}')
|
||||
return avg_loss
|
||||
|
||||
|
||||
def validate_model(device, model, loader, criterion, epoch, model_name):
|
||||
model.eval()
|
||||
running_loss = 0.0
|
||||
correct, total = 0, 0
|
||||
all_labels, all_preds = [], []
|
||||
|
||||
with torch.no_grad():
|
||||
for inputs, labels in tqdm(loader, desc=f'Validating {model_name} Epoch {epoch + 1}', unit='batch'):
|
||||
inputs, labels = inputs.float().to(device), labels.to(device) # 确保数据在 device 上
|
||||
|
||||
outputs = model(inputs)
|
||||
|
||||
# 将 logits 转换为预测
|
||||
predicted = torch.sigmoid(outputs).data
|
||||
all_preds.extend(predicted.cpu().numpy())
|
||||
all_labels.extend(labels.cpu().numpy())
|
||||
|
||||
# loss = criterion(outputs.squeeze(), labels.float())
|
||||
loss = criterion(outputs.squeeze(), labels.float())
|
||||
running_loss += loss.item()
|
||||
auc = roc_auc_score(all_labels, all_preds)
|
||||
predicted_labels = (np.array(all_preds) >= 0.5).astype(int) # 确保转换为 NumPy 数组
|
||||
acc = accuracy_score(all_labels, predicted_labels)
|
||||
avg_loss = running_loss / len(loader)
|
||||
print(f'{model_name} Validation Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}, AUC: {auc:.4f}')
|
||||
return avg_loss, acc, auc
|
||||
|
||||
|
||||
# 权重聚合函数
|
||||
def aggregate_weights(weights_list, alpha=1 / 3, beta=1 / 3, gamma=1 / 3):
|
||||
new_state_dict = copy.deepcopy(weights_list[0]) # 从模型a复制权重结构
|
||||
for key in new_state_dict.keys():
|
||||
new_state_dict[key] = (alpha * weights_list[0][key] +
|
||||
beta * weights_list[1][key] +
|
||||
gamma * weights_list[2][key])
|
||||
return new_state_dict
|
||||
|
||||
|
||||
def v3_update_model_weights(
|
||||
epoch,
|
||||
model_to_update,
|
||||
other_models,
|
||||
global_model,
|
||||
losses,
|
||||
val_loader,
|
||||
device,
|
||||
val_auc_threshold, # 当前需要更新模型的验证 AUC 阈值
|
||||
validate_model,
|
||||
criterion,
|
||||
update_frequency
|
||||
):
|
||||
"""
|
||||
根据给定的条件更新模型的权重。
|
||||
|
||||
参数:
|
||||
epoch (int): 当前训练轮次。
|
||||
model_to_update: 需要更新的模型。
|
||||
other_models (list): 其他模型列表，用于计算全局模型权重。
|
||||
global_model: 全局模型。
|
||||
losses (list): 各模型的损失值列表。
|
||||
val_loader: 验证数据的 DataLoader。
|
||||
device: 设备 ('cuda' 或 'cpu')。
|
||||
val_auc_threshold (float): 当前需要更新模型的验证 AUC。
|
||||
aggregate_weights (function): 权重聚合函数。
|
||||
validate_model (function): 验证模型的函数。
|
||||
update_frequency (int): 权重更新的频率。
|
||||
|
||||
返回:
|
||||
val_acc (float): 全局模型的验证精度。
|
||||
val_auc (float): 全局模型的验证 AUC。
|
||||
updated_val_auc_threshold (float): 更新后的验证 AUC。
|
||||
"""
|
||||
if (epoch + 1) % update_frequency == 0:
|
||||
# 获取所有模型的权重
|
||||
all_weights = [model.state_dict() for model in other_models]
|
||||
avg_weights = aggregate_weights(all_weights) # 聚合权重
|
||||
|
||||
# 更新全局模型权重
|
||||
global_model.load_state_dict(avg_weights)
|
||||
|
||||
# 计算加权平均损失
|
||||
weighted_loss = sum(loss * 0.33 for loss in losses)
|
||||
print(f"Weighted Average Loss: {weighted_loss:.4f}")
|
||||
|
||||
# 验证全局模型
|
||||
val_loss, val_acc, val_auc = validate_model(device, global_model, val_loader, criterion, epoch, 'global_model')
|
||||
print(f'global_model Validation Accuracy: {val_acc:.4f}, global_model Validation AUC: {val_auc:.4f}')
|
||||
|
||||
# 如果全局模型的 AUC 更高，则更新目标模型
|
||||
if val_auc > val_auc_threshold:
|
||||
print(f'Updating model at epoch {epoch + 1}')
|
||||
model_to_update.load_state_dict(global_model.state_dict())
|
||||
val_auc_threshold = val_auc # 更新 AUC 阈值
|
||||
|
||||
return val_acc, val_auc, val_auc_threshold
|
||||
return None, None, val_auc_threshold
|
||||
|
||||
|
||||
def update_model_weights(
|
||||
epoch,
|
||||
model_to_update,
|
||||
other_models,
|
||||
global_model,
|
||||
losses,
|
||||
val_loader,
|
||||
device,
|
||||
val_auc_threshold, # 当前需要更新模型的验证 AUC 阈值
|
||||
validate_model,
|
||||
criterion,
|
||||
update_frequency
|
||||
):
|
||||
"""
|
||||
根据给定的条件更新模型的权重。
|
||||
|
||||
参数:
|
||||
epoch (int): 当前训练轮次。
|
||||
model_to_update: 需要更新的模型。
|
||||
other_models (list): 其他模型列表，用于计算全局模型权重。
|
||||
global_model: 全局模型。
|
||||
losses (list): 各模型的损失值列表。
|
||||
val_loader: 验证数据的 DataLoader。
|
||||
device: 设备 ('cuda' 或 'cpu')。
|
||||
val_auc_threshold (float): 当前需要更新模型的验证 AUC。
|
||||
aggregate_weights (function): 权重聚合函数。
|
||||
validate_model (function): 验证模型的函数。
|
||||
update_frequency (int): 权重更新的频率。
|
||||
|
||||
返回:
|
||||
val_acc (float): 全局模型的验证精度。
|
||||
val_auc (float): 全局模型的验证 AUC。
|
||||
updated_val_auc_threshold (float): 更新后的验证 AUC。
|
||||
"""
|
||||
if (epoch + 1) % update_frequency == 0:
|
||||
# 获取所有模型的权重
|
||||
all_weights = [model.state_dict() for model in other_models]
|
||||
avg_weights = aggregate_weights(all_weights) # 聚合权重
|
||||
|
||||
# 更新全局模型权重
|
||||
global_model.load_state_dict(avg_weights)
|
||||
|
||||
# 计算加权平均损失
|
||||
weighted_loss = sum(loss * 0.33 for loss in losses)
|
||||
print(f"Weighted Average Loss: {weighted_loss:.4f}")
|
||||
|
||||
# 验证全局模型
|
||||
val_loss, val_acc, val_auc = validate_deepmodel(device, global_model, val_loader, criterion, epoch,
|
||||
'global_model')
|
||||
print(f'global_model Validation Accuracy: {val_acc:.4f}, global_model Validation AUC: {val_auc:.4f}')
|
||||
|
||||
# 如果全局模型的 AUC 更高，则更新目标模型
|
||||
if val_auc > val_auc_threshold:
|
||||
print(f'Updating model at epoch {epoch + 1}')
|
||||
model_to_update.load_state_dict(global_model.state_dict())
|
||||
val_auc_threshold = val_auc # 更新 AUC 阈值
|
||||
|
||||
return val_acc, val_auc, val_auc_threshold
|
||||
return None, None, val_auc_threshold
|
||||
|
||||
|
||||
def f_update_model_weights(
|
||||
epoch,
|
||||
model_to_update,
|
||||
other_models,
|
||||
global_model,
|
||||
losses,
|
||||
val_loader,
|
||||
device,
|
||||
val_auc_threshold, # 当前需要更新模型的验证 AUC 阈值
|
||||
aggregate_weights, # 权重聚合函数
|
||||
validate_model,
|
||||
criterion,
|
||||
update_frequency
|
||||
):
|
||||
"""
|
||||
根据给定的条件更新模型的权重。
|
||||
|
||||
参数:
|
||||
epoch (int): 当前训练轮次。
|
||||
model_to_update: 需要更新的模型。
|
||||
other_models (list): 其他模型列表，用于计算全局模型权重。
|
||||
global_model: 全局模型。
|
||||
losses (list): 各模型的损失值列表。
|
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val_loader: 验证数据的 DataLoader。
|
||||
device: 设备 ('cuda' 或 'cpu')。
|
||||
val_auc_threshold (float): 当前需要更新模型的验证 AUC 阈值。
|
||||
aggregate_weights (function): 权重聚合函数。
|
||||
validate_model (function): 验证模型的函数。
|
||||
criterion: 损失函数。
|
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update_frequency (int): 权重更新的频率。
|
||||
|
||||
返回:
|
||||
val_acc (float): 全局模型的验证精度。
|
||||
val_auc (float): 全局模型的验证 AUC。
|
||||
updated_val_auc_threshold (float): 更新后的验证 AUC 阈值。
|
||||
"""
|
||||
# 每隔指定的 epoch 更新一次模型权重
|
||||
|
||||
if (epoch + 1) % update_frequency == 0:
|
||||
print(f"\n[Epoch {epoch + 1}] Updating global model weights...")
|
||||
|
||||
# 获取其他模型的权重
|
||||
all_weights = [model.state_dict() for model in other_models]
|
||||
|
||||
# 使用聚合函数计算全局权重
|
||||
avg_weights = aggregate_weights(all_weights)
|
||||
print("Global model weights aggregated.")
|
||||
|
||||
# 更新全局模型权重
|
||||
global_model.load_state_dict(avg_weights)
|
||||
|
||||
# 计算加权平均损失
|
||||
weighted_loss = sum(loss * (1 / len(losses)) for loss in losses) # 平均加权
|
||||
print(f"Weighted Average Loss: {weighted_loss:.4f}")
|
||||
|
||||
# 验证全局模型性能
|
||||
val_loss, val_acc, val_auc = validate_deepmodel(device, global_model, val_loader, criterion, epoch,
|
||||
'global_model')
|
||||
print(f"[Global Model] Validation Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}, AUC: {val_auc:.4f}")
|
||||
|
||||
# 如果全局模型 AUC 高于阈值，则更新目标模型权重
|
||||
if val_auc > val_auc_threshold:
|
||||
print(f"Global model AUC improved ({val_auc:.4f} > {val_auc_threshold:.4f}). Updating target model.")
|
||||
model_to_update.load_state_dict(global_model.state_dict())
|
||||
val_auc_threshold = val_auc # 更新 AUC 阈值
|
||||
else:
|
||||
print(
|
||||
f"Global model AUC did not improve ({val_auc:.4f} <= {val_auc_threshold:.4f}). No update to target model.")
|
||||
|
||||
return val_acc, val_auc, val_auc_threshold
|
||||
|
||||
# 如果未到达更新频率，返回当前的 AUC 阈值
|
||||
return None, None, val_auc_threshold
|
||||
Loading…
Reference in New Issue
Block a user