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44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
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from opencompass.multimodal.models.qwen import QwenVLChatScienceQAPromptConstructor
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# dataloader settings
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val_pipeline = [
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dict(type='mmpretrain.LoadImageFromFile'),
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dict(type='mmpretrain.ToPIL', to_rgb=True),
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dict(type='mmpretrain.torchvision/Resize',
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size=(448, 448),
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interpolation=3),
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dict(type='mmpretrain.torchvision/ToTensor'),
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dict(type='mmpretrain.torchvision/Normalize',
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mean=(0.48145466, 0.4578275, 0.40821073),
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std=(0.26862954, 0.26130258, 0.27577711)),
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dict(type='mmpretrain.PackInputs',
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algorithm_keys=[
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'question', 'gt_answer', 'choices', 'hint', 'lecture', 'solution'
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])
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]
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dataset = dict(type='mmpretrain.ScienceQA',
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data_root='./data/scienceqa',
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split='val',
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split_file='pid_splits.json',
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ann_file='problems.json',
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image_only=True,
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data_prefix=dict(img_path='val'),
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pipeline=val_pipeline)
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qwen_scienceqa_dataloader = dict(batch_size=1,
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num_workers=4,
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dataset=dataset,
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collate_fn=dict(type='pseudo_collate'),
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sampler=dict(type='DefaultSampler', shuffle=False))
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# model settings
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qwen_scienceqa_model = dict(
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type='qwen-vl-chat',
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pretrained_path='Qwen/Qwen-VL-Chat', # or Huggingface repo id
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prompt_constructor=dict(type=QwenVLChatScienceQAPromptConstructor)
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)
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# evaluation settings
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qwen_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]
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