PULSE-ECG

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PULSE-7B

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PULSE-ECG 7

Could you provide the complete error message?

Here is the complete error message:

Some weights of the model checkpoint at PULSE-ECG/PULSE-7B were not used when initializing LlavaLlamaForCausalLM: ['model.image_newline']

• This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  Traceback (most recent call last):
  File "/home//PULSE/LLaVA/llava/eval/run_llava.py", line 148, in
  eval_model(args)
  File "/home//PULSE/LLaVA/llava/eval/run_llava.py", line 105, in eval_model
  images_tensor = process_images(
  File "/home/****/PULSE/LLaVA/llava/mm_utils.py", line 178, in process_images
  image = process_anyres_image(image, image_processor, model_cfg.image_grid_pinpoints)
  File "/home/***/PULSE/LLaVA/llava/mm_utils.py", line 140, in process_anyres_image
  patches = divide_to_patches(image_padded, processor.crop_size['height'])
  AttributeError: 'NoneType' object has no attribute 'crop_size

I think the issue comes from when in the builder.py in the load_pretrained_model function

image_processor = None


if 'llava' in model_name.lower():
    mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
    mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
    if mm_use_im_patch_token:
        tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
    if mm_use_im_start_end:
        tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
    model.resize_token_embeddings(len(tokenizer))

    vision_tower = model.get_vision_tower()
    if not vision_tower.is_loaded:
        vision_tower.load_model(device_map=device_map)
    if device_map != 'auto':
        vision_tower.to(device=device_map, dtype=torch.float16)
    image_processor = vision_tower.image_processor

Since running the command suggested in the github:
python llava/eval/run_llava.py --model-path "PULSE-ECG/PULSE-7B" --image-file "images/ecg_example.png" --query "What are the main features in this ECG image?" --conv-mode "llava_v1"
will result in model_name.lower() will be pulse-7b thus the returned image_processor = None

Yes, you’re right. Changing if 'llava' in model_name.lower(): to if 'llava' in model_name.lower() or 'pulse' in model_name.lower(): will work. This hasn’t yet been updated in the gitHub codebase. I’ll check and make sure to update it there.

Thank you for pointing this out. I have updated the code: https://github.com/AIMedLab/PULSE/commit/836f864e306012558c7d2611cc85f431be6785b5

No problem!!

Just wanted to ask, it seems that the demo page (https://huggingface.co./spaces/paralym/PULSE-7B) isn't working

Thanks for the reminder. It seems that the issue is due to the expiration of my Plus subscription. We’ll try applying for free ZeroGPU support.