opencompass-my-api/configs/multimodal/qwen/qwenvl_chat_7b_coco_caption.py

from opencompass.multimodal.models.qwen import QwenVLChatPromptConstructor

# dataloader settings
val_pipeline = [
    dict(type='mmpretrain.LoadImageFromFile'),
    dict(type='mmpretrain.ToPIL', to_rgb=True),
    dict(type='mmpretrain.torchvision/Resize',
         size=(448, 448),
         interpolation=3),
    dict(type='mmpretrain.torchvision/ToTensor'),
    dict(type='mmpretrain.torchvision/Normalize',
         mean=(0.48145466, 0.4578275, 0.40821073),
         std=(0.26862954, 0.26130258, 0.27577711)),
    dict(type='mmpretrain.PackInputs',
         algorithm_keys=['image_id'])
]

dataset = dict(type='mmpretrain.COCOCaption',
               data_root='data/coco',
               data_prefix=dict(img_path='images'),
               ann_file='annotations/coco_karpathy_val.json',
               pipeline=val_pipeline)

qwen_coco_caption_dataloader = dict(batch_size=1,
                  num_workers=4,
                  dataset=dataset,
                  collate_fn=dict(type='pseudo_collate'),
                  sampler=dict(type='DefaultSampler', shuffle=False))

# model settings
qwen_coco_caption_model = dict(
    type='qwen-vl-chat',
    pretrained_path='Qwen/Qwen-VL-Chat',  # or Huggingface repo id
    prompt_constructor=dict(type=QwenVLChatPromptConstructor, prompt='Describe the image.'),
    is_caption_task=True,
)

# evaluation settings
qwen_coco_caption_evaluator = [
    dict(
        type='mmpretrain.COCOCaption',
        ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
    )  # noqa
]