更新联邦学习代码
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yoiannis
parent
4cb9790dee
commit
a5ca9d04d7
139
FED.py
139
FED.py
@ -8,6 +8,7 @@ import copy
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from tqdm import tqdm
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from data_loader import get_data_loader
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from model.repvit import repvit_m1_1
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from model.mobilenetv3 import MobileNetV3
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@ -17,7 +18,7 @@ NUM_ROUNDS = 10
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CLIENT_EPOCHS = 2
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BATCH_SIZE = 32
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TEMP = 2.0 # 蒸馏温度
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CLASS_NUM = [3, 3, 3]
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CLASS_NUM = [9, 9, 9]
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# 设备配置
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@ -32,31 +33,39 @@ def prepare_data():
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transforms.ToTensor()
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])
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# Load datasets
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dataset_A = ImageFolder(root='G:/testdata/JY_A/train', transform=transform)
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dataset_B = ImageFolder(root='G:/testdata/ZY_A/train', transform=transform)
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dataset_C = ImageFolder(root='G:/testdata/ZY_B/train', transform=transform)
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# 加载所有数据集（训练、验证、测试）
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dataset_A_train,dataset_A_val,dataset_A_test = get_data_loader(root_dir='/home/yoiannis/deep_learning/dataset/03.TA_EC_FD3/JY_A',Cache='RAM')
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dataset_B_train,dataset_B_val,dataset_B_test = get_data_loader(root_dir='/home/yoiannis/deep_learning/dataset/03.TA_EC_FD3/ZY_A',Cache='RAM')
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dataset_C_train,dataset_C_val,dataset_C_test = get_data_loader(root_dir='/home/yoiannis/deep_learning/dataset/03.TA_EC_FD3/ZY_B',Cache='RAM')
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# Assign datasets to clients
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client_datasets = [dataset_B, dataset_C]
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# 组织客户端数据集
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client_datasets = [
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{ # Client 0
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'train': dataset_B_train,
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'val': dataset_B_val,
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'test': dataset_B_test
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},
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{ # Client 1
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'train': dataset_C_train,
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'val': dataset_C_val,
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'test': dataset_C_test
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}
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]
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# Server dataset (A) for public updates
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public_loader = DataLoader(dataset_A, batch_size=BATCH_SIZE, shuffle=True)
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# 公共数据集（使用A的训练集）
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public_loader = dataset_A_train
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return client_datasets, public_loader
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# 服务器测试集（使用A的测试集）
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server_test_loader = dataset_A_test
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return client_datasets, public_loader, server_test_loader
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# 客户端训练函数
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def client_train(client_model, server_model, dataset):
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def client_train(client_model, server_model, loader):
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client_model.train()
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server_model.eval()
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optimizer = torch.optim.SGD(client_model.parameters(), lr=0.1)
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loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
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# 训练进度条
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progress_bar = tqdm(total=CLIENT_EPOCHS*len(loader),
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desc="Client Training",
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unit="batch")
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for epoch in range(CLIENT_EPOCHS):
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epoch_loss = 0.0
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@ -65,16 +74,19 @@ def client_train(client_model, server_model, dataset):
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correct = 0
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total = 0
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for batch_idx, (data, target) in enumerate(loader):
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# 训练进度条
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progress_bar = tqdm(loader, desc=f"Epoch {epoch+1}/{CLIENT_EPOCHS}")
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for batch_idx, (data, target) in enumerate(progress_bar):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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# 前向传播
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client_output = client_model(data)
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client_output = client_model(data).to(device)
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# 获取教师模型输出
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with torch.no_grad():
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server_output = server_model(data)
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server_output = server_model(data).to(device)
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# 计算损失
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loss_task = F.cross_entropy(client_output, target)
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@ -166,9 +178,10 @@ def server_update(server_model, client_models, public_loader):
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optimizer = torch.optim.Adam(server_model.parameters(), lr=0.001)
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total_loss = 0.0
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progress_bar = tqdm(public_loader, desc="Server Updating", unit="batch")
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for batch_idx, (data, _) in enumerate(progress_bar):
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for batch_idx, (data, target) in enumerate(progress_bar):
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data = data.to(device)
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optimizer.zero_grad()
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@ -199,97 +212,97 @@ def server_update(server_model, client_models, public_loader):
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print(f"\nServer Update Complete | Average Loss: {total_loss/len(public_loader):.4f}\n")
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def test_model(model, test_loader):
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def test_model(model, test_loader): # 添加对DataLoader的支持
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model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for data, target in test_loader:
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progress_bar = tqdm(test_loader, desc="Server Updating", unit="batch")
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for batch_idx, (data, target) in enumerate(progress_bar):
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data, target = data.to(device), target.to(device)
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output = model(data)
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_, predicted = torch.max(output.data, 1)
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total += target.size(0)
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correct += (predicted == target).sum().item()
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accuracy = 100 * correct / total
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return accuracy
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return 100 * correct / total
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# 主训练流程
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def main():
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor()
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])
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# Initialize models
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# 初始化模型（保持不变）
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global_server_model = repvit_m1_1(num_classes=CLASS_NUM[0]).to(device)
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client_models = [MobileNetV3(n_class=CLASS_NUM[i+1]).to(device) for i in range(NUM_CLIENTS)]
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# Prepare data
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client_datasets, public_loader = prepare_data()
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# Test dataset (using dataset A's test set for simplicity)
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test_dataset = ImageFolder(root='G:/testdata/JY_A/test', transform=transform)
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test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)
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# 加载数据集
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client_datasets, public_loader, server_test_loader = prepare_data()
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round_progress = tqdm(total=NUM_ROUNDS, desc="Federated Rounds", unit="round")
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for round in range(NUM_ROUNDS):
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print(f"\n{'#'*50}")
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print(f"Federated Round {round+1}/{NUM_ROUNDS}")
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print(f"Round {round+1}/{NUM_ROUNDS}")
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print(f"{'#'*50}")
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# Client selection (only 2 clients)
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# 客户端选择
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selected_clients = np.random.choice(NUM_CLIENTS, 2, replace=False)
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print(f"Selected Clients: {selected_clients}")
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print(f"Selected clients: {selected_clients}")
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# Client local training
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# 客户端训练
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client_params = []
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for cid in selected_clients:
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print(f"\nTraining Client {cid}")
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local_model = copy.deepcopy(client_models[cid])
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local_model.load_state_dict(client_models[cid].state_dict())
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updated_params = client_train(local_model, global_server_model, client_datasets[cid])
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# 传入客户端的训练集
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updated_params = client_train(
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local_model,
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global_server_model,
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client_datasets[cid]['train'] # 使用训练集
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)
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client_params.append(updated_params)
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# Model aggregation
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# 模型聚合
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global_client_params = aggregate(client_params)
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for model in client_models:
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model.load_state_dict(global_client_params)
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# Server knowledge update
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print("\nServer Updating...")
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# 服务器更新
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print("\nServer updating...")
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server_update(global_server_model, client_models, public_loader)
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# Test model performance
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server_acc = test_model(global_server_model, test_loader)
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client_acc = test_model(client_models[0], test_loader)
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print(f"\nRound {round+1} Performance:")
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print(f"Global Model Accuracy: {server_acc:.2f}%")
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print(f"Client Model Accuracy: {client_acc:.2f}%")
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# 测试性能
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server_acc = test_model(global_server_model, server_test_loader)
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client_accuracies = [
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test_model(client_models[i],
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client_datasets[i]['test']) # 动态创建测试loader
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for i in range(NUM_CLIENTS)
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]
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print(f"\nRound {round+1} Results:")
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print(f"Server Accuracy: {server_acc:.2f}%")
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for i, acc in enumerate(client_accuracies):
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print(f"Client {i} Accuracy: {acc:.2f}%")
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round_progress.update(1)
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print(f"Round {round+1} completed")
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print("Training completed!")
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# Save trained models
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# 保存模型
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torch.save(global_server_model.state_dict(), "server_model.pth")
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for i in range(NUM_CLIENTS):
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torch.save(client_models[i].state_dict(), "client"+str(i)+"_model.pth")
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print("Models saved successfully.")
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torch.save(client_models[i].state_dict(), f"client{i}_model.pth")
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# Test server model
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# 最终测试
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print("\nFinal Evaluation:")
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server_model = repvit_m1_1(num_classes=CLASS_NUM[0]).to(device)
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server_model.load_state_dict(torch.load("server_model.pth",weights_only=True))
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server_acc = test_model(server_model, test_loader)
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print(f"Server Model Test Accuracy: {server_acc:.2f}%")
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print(f"Server Accuracy: {test_model(server_model, server_test_loader):.2f}%")
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# Test client model
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for i in range(NUM_CLIENTS):
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client_model = MobileNetV3(n_class=CLASS_NUM[i+1]).to(device)
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client_model.load_state_dict(torch.load("client"+str(i)+"_model.pth",weights_only=True))
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client_acc = test_model(client_model, test_loader)
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print(f"Client->{i} Model Test Accuracy: {client_acc:.2f}%")
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client_model.load_state_dict(torch.load(f"client{i}_model.pth",weights_only=True))
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test_loader = client_datasets[i]['test']
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print(f"Client {i} Accuracy: {test_model(client_model, test_loader):.2f}%")
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if __name__ == "__main__":
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main()
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@ -5,8 +5,11 @@ import torch
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms
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from tqdm import tqdm # 导入 tqdm
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import logging
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class ImageClassificationDataset(Dataset):
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def __init__(self, root_dir, transform=None,Cache=False):
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def __init__(self, root_dir, transform=None, Cache=False):
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self.root_dir = root_dir
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self.transform = transform
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self.classes = sorted(os.listdir(root_dir))
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@ -20,7 +23,8 @@ class ImageClassificationDataset(Dataset):
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"init the dataloader"
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)
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for cls_name in self.classes:
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# 使用 tqdm 显示进度条
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for cls_name in tqdm(self.classes, desc="Loading images"):
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cls_dir = os.path.join(root_dir, cls_name)
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for img_name in os.listdir(cls_dir):
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try:
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@ -33,11 +37,8 @@ class ImageClassificationDataset(Dataset):
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else:
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self.image_paths.append(img_path)
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self.labels.append(self.class_to_idx[cls_name])
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except:
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logger.log("info",
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"read image error " +
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img_path
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)
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except Exception as e:
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logger.log("info", f"Read image error: {img_path} - {e}")
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def __len__(self):
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return len(self.labels)
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@ -51,7 +52,6 @@ class ImageClassificationDataset(Dataset):
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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_data_loader(root_dir, batch_size=64, num_workers=4, pin_memory=True,Cache=False):
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@ -2,9 +2,8 @@ import os
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import shutil
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import random
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def create_dataset_splits(base_dir, output_dir, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15):
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def create_dataset_splits(base_dir, output_dir, train_ratio=0.7, val_ratio=0.2, test_ratio=0.1):
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# 确保比例总和为1
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assert train_ratio + val_ratio + test_ratio == 1.0, "Ratios must sum to 1"
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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@ -55,6 +54,6 @@ def create_dataset_splits(base_dir, output_dir, train_ratio=0.7, val_ratio=0.15,
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print("Dataset successfully split into train, validation, and test sets.")
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# 使用示例
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base_directory = 'F:/dataset/02.TA_EC/EC27/JY_A'
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output_directory = 'F:/dataset/02.TA_EC/datasets/EC27'
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base_directory = 'L:/Grade_datasets/SPLIT/JY_A'
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output_directory = 'L:/Grade_datasets/train/JY_A'
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create_dataset_splits(base_directory, output_directory)
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