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from opencompass.multimodal.models.instructblip import ( |
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InstructBlipScienceQAPromptConstructor, |
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InstructBlipScienceQAPostProcessor, |
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) |
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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=(224, 224), |
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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', 'has_image' |
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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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instruct_blip_scienceqa_dataloader = dict( |
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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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instruct_blip_scienceqa_model = dict( |
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type='blip2-vicuna-instruct', |
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prompt_constructor=dict(type=InstructBlipScienceQAPromptConstructor), |
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post_processor=dict(type=InstructBlipScienceQAPostProcessor), |
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freeze_vit=True, |
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low_resource=False, |
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llm_model='/path/to/vicuna-7b/', |
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max_output_txt_len=10, |
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) |
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instruct_blip_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')] |
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instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth' |
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