File "tools/train.py", line 31, in
trainer.train()
File "/home/aistudio/PaddleClas/ppcls/engine/trainer.py", line 218, in train
loss_dict = self.train_loss_func(out, batch[1:])
File "/home/aistudio/PaddleClas/ppcls/loss/__init__.py", line 45, in __call__
loss = loss_func(input, batch)
File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 902, in __call__
outputs = self.forward(*inputs, **kwargs)
File "/home/aistudio/PaddleClas/ppcls/loss/celoss.py", line 49, in forward
if label.shape[-1] == x.shape[-1]:
AttributeError: 'list' object has no attribute 'shape'

出现上面的内容，我的配置文件如下

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: ./output/
  device: gpu
  class_num: 12
  save_interval: 1
  eval_during_train: True
  eval_interval: 1
  epochs: 20
  print_batch_step: 10
  use_visualdl: True
  # used for static mode and model export
  image_shape: [3, 224, 224]
  save_inference_dir: ./inference

# model architecture
Arch:
  name: ResNet50_vd
 
# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
  Eval:
    - CELoss:
        weight: 1.0

Optimizer:
  name: Momentum
  momentum: 0.9
  lr:
    name: Cosine
    learning_rate: 0.0125
    warmup_epoch: 5
  regularizer:
    name: 'L2'
    coeff: 0.00001

# data loader for train and eval
DataLoader:
  Train:
    dataset:
      name: ImageNetDataset
      image_root: ./dataset/ImageNetDataset/
      cls_label_path: ./dataset/ImageNetDataset/train_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
      batch_transform_ops:
        - CutmixOperator:
            alpha: 0.2
    sampler:
      name: DistributedBatchSampler
      batch_size: 32
      drop_last: False
      shuffle: True
    loader:
      num_workers: 4
      use_shared_memory: True

  Eval:
    dataset: 
      name: ImageNetDataset
      image_root: ./dataset/ImageNetDataset/
      cls_label_path: ./dataset/ImageNetDataset/val_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            resize_short: 256
        - CropImage:
            size: 224
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
    sampler:
      name: DistributedBatchSampler
      batch_size: 64
      drop_last: False
      shuffle: False
    loader:
      num_workers: 4
      use_shared_memory: True

Metric:
  Train:
    - TopkAcc:
        topk: [1, 5]
  Eval:
    - TopkAcc:
        topk: [1, 5]