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from opencompass.multimodal.models.qwen import QwenVLChatVQAPromptConstructor
# 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=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(
type='mmpretrain.COCOVQA',
data_root='data/coco',
data_prefix='images/val2014',
question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=val_pipeline)
qwen_vqav2_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_vqav2_model = dict(
type='qwen-vl-chat',
pretrained_path='Qwen/Qwen-VL-Chat', # or Huggingface repo id
prompt_constructor=dict(type=QwenVLChatVQAPromptConstructor)
)
# evaluation settings
qwen_vqav2_evaluator = [dict(type='mmpretrain.VQAAcc')]
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