Pytorch 数据集类在测试时返回两次 hdf5 数据

Question

我一直在尝试使用未标记的数据测试我的级联模型。该过程涉及遍历特定目录中的每个数据实例，模型应为它们分配标签，然后继续下一个。但是，我遇到了数据似乎重复的问题，导致张量形状不正确。此外，对于第二个数据实例，返回一个空数组。这是我目前用于参考的代码。

class Hfdata(Dataset):
    def __init__(self, data_dir, file_name):
        self.data_dir = data_dir
        self.file_name = file_name
        with h5py.File(f"{data_dir}/{file_name}", "r") as f:
            self.data = f["data"][:]

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        x = self.data[idx]
        print(x.shape)
        return x
folder_path_sp = 'test_data'
file_list_sp = glob.glob(os.path.join(folder_path_sp, '*.h5'))
folder_path_tm = 'test_data_tm'
file_list_tm = glob.glob(os.path.join(folder_path_tm, '*.h5'))
model = Ensemble(ModelA(), ModelB())
model.load_state_dict(torch.load('Ensamble_model.pt'))

# predict on the new data
model.eval()
predictions = []
for file_path_sp, file_path_tm in zip(file_list_sp, file_list_tm):
    file_name_sp = os.path.basename(file_path_sp)
    file_name_tm = os.path.basename(file_path_tm)
    print(f"Prediction on {file_name_sp} and {file_name_tm}")
    dataset_sp_p = Hfdata(folder_path_sp, file_name_sp)
    dataloader_sp_p = DataLoader(dataset_sp_p, batch_size=batch_size, shuffle=False)
    dataset_tm_p = Hfdata(folder_path_tm, file_name_tm)
    dataloader_tm_p = DataLoader(dataset_tm_p, batch_size=batch_size, shuffle=False)
    for i, ((data_sp), (data_tm)) in enumerate(zip(dataloader_sp_p, dataloader_tm_p)):
        # prepare the data
        print(data_sp.shape)
        data_sp = data_sp.permute(0, 4, 1,2, 3)
        data_sp = data_sp.float()
        print(data_tm.shape)
        data_tm = data_tm.view(-1, 1, 609).float()

        # pass the data through the model
        with torch.no_grad():
            outputs = model(data_sp, data_tm)

        # obtain the predicted labels from the predicted outputs
        _, predicted_labels = torch.max(outputs.data, 1)

        # append the predicted labels to the list of predictions
        predictions.append(predicted_labels)
        print(predicted_labels)

# print the predictions
print(predictions)

这只是打印以下内容：

(79, 95, 79, 1)
(79, 95, 79, 1)
()
()
torch.Size([2, 79, 95, 79, 1])
torch. Size([2])

所以，我的你能帮我解决这个问题吗？ 非常感谢。

i 期望第一个数据的 torch 大小为 ([batch size, channel, depth, height width)]，而第二个数据为 ([1, 609]) 但这里不是这种情况。