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42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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from opencompass.multimodal.models.qwen import QwenVLMMBenchPromptConstructor
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# dataloader settings
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val_pipeline = [
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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', 'options', 'category', 'l2-category', 'context',
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'index', 'options_dict'
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])
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]
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dataset = dict(type='opencompass.MMBenchDataset',
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data_file='/mnt/petrelfs/share_data/yuanyike/cnbench_v010_rolling.tsv',
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pipeline=val_pipeline,
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sys_prompt='请从以下选项中选择一个正确选项。')
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qwen_mmbench_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_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=QwenVLMMBenchPromptConstructor)
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)
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# evaluation settings
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qwen_mmbench_evaluator = [
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dict(type='opencompass.DumpResults',
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save_path='work_dirs/qwenvl-chat-7b-cnbench-v010.xlsx')
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]
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