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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
]
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