fix test files

Signed-off-by: weijingchen <talkingwallace@sohu.com>

Signed-off-by: cwj <talkingwallace@sohu.com>

python/fate/ml/nn/model_zoo/agg_layer/fedpass/_passport_block.py

@@ -16,7 +16,6 @@ def _get_activation(activation):
         raise ValueError(f"Unsupported activation: {activation}")
 
 
-
 class PassportBlock(nn.Module):
 
     def __init__(self, passport_distribute: Literal['gaussian', 'uniform'], passport_mode: Literal['single', 'multi']):

python/fate/ml/nn/test/test_fedpass_alexnet.py

@@ -172,29 +172,24 @@ def set_seed(seed):
     set_seed(42)
 
 
-    # 1. 加载数据
     train_data = torchvision.datasets.CIFAR10(root='./cifar10',
                                             train=True,
                                             download=True,
                                             transform=torchvision.transforms.ToTensor())
 
-    # 2. 为每个数字保存其索引
     digit_indices = [[] for _ in range(10)]
     for idx, (_, label) in enumerate(train_data):
         digit_indices[label].append(idx)
 
-    # 3. 从每个数字的索引中随机选择300个样本作为训练集
     selected_train_indices = []
     for indices in digit_indices:
         selected_train_indices.extend(torch.randperm(len(indices))[:500].tolist())
 
-    # 4. 从剩下的索引中随机选择100个样本作为验证集
     selected_val_indices = []
     for indices in digit_indices:
         remaining_indices = [idx for idx in indices if idx not in selected_train_indices]
         selected_val_indices.extend(torch.randperm(len(remaining_indices))[:100].tolist())
 
-    # 5. 使用Subset获取训练集和验证集
     subset_train_data = torch.utils.data.Subset(train_data, selected_train_indices)
     subset_val_data = torch.utils.data.Subset(train_data, selected_val_indices)
 

python/fate/ml/nn/test/test_hetero_nn_algo.py

@@ -95,7 +95,6 @@ def set_seed(seed):
             training_args=args
         )
         trainer.train()
-        print('cwj done')
         # pred = trainer.predict(dataset)
         # # compute auc
         # from sklearn.metrics import roc_auc_score