问题描述：

1. 在训练模型的过程中，出现 clip_image_processor 无法处理数据的问题，说明数据集中很可能出现了脏数据。
2. 本文使用的数据为 LAION-Aesthetics-V2-6.5plus，从 https://dagshub.com/DagsHub-Datasets/LAION-Aesthetics-V2-6.5plus 上下载的。

Traceback (most recent call last):
...
  File "/xxx/check_train_data.py", line 69, in __getitem__
    raise e  # Re-raise the exception to halt the training process
    ^^^^^^^
  File "/xxx/check_train_data.py", line 64, in __getitem__
    clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/xxx/lib/python3.12/site-packages/transformers/image_processing_utils.py", line 41, in __call__
    return self.preprocess(images, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/xxx/lib/python3.12/site-packages/transformers/models/clip/image_processing_clip.py", line 341, in preprocess
    self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
  File "/xxx/lib/python3.12/site-packages/transformers/image_processing_utils.py", line 111, in normalize
    return normalize(
           ^^^^^^^^^^
  File "/xxx/lib/python3.12/site-packages/transformers/image_transforms.py", line 392, in normalize
    raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}")
ValueError: mean must have 1 elements if it is an iterable, got 3

解决方案：

1. 将原代码的 clip_image = self.clip_image_processor 修改为 try、except 来找到导致报错的图片。
2. 将加载数据的代码部分拎出，并遍历一遍。

 # read image
 raw_image = Image.open(os.path.join(self.image_root_path, image_file))
 image = self.transform(raw_image.convert("RGB"))
 # clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
 try:
     clip_image = self.clip_image_processor(images=raw_image, return_tensors="pt").pixel_values
     print(f'image_file_{idx} processed with clip_image_processor: {image_file}')
 except Exception as e:
     print(f'Error processing image_file_{idx}: {image_file}')
     print(e)
     raise e  # Re-raise the exception to halt the training process

3. 最终卡在 4235 附近的图片，通过肉眼观察，发现 4236 是图片空的😂
4. 手动删除 4236 图片以及对应的 json 文本后便可正常训练！🏋️