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from opencompass.multimodal.models.instructblip import ( |
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InstructBlipVQAPromptConstructor, |
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InstructBlipVQAPostProcessor, |
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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( |
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type='mmpretrain.PackInputs', |
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algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'], |
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meta_keys=['question_id', 'image_id'], |
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) |
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] |
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dataset = dict(type='mmpretrain.GQA', |
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data_root='data/gqa', |
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data_prefix='images', |
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ann_file='annotations/testdev_balanced_questions.json', |
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pipeline=val_pipeline) |
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instruct_blip_gqa_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', |
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shuffle=False)) |
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instruct_blip_gqa_model = dict( |
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type='blip2-vicuna-instruct', |
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prompt_constructor=dict(type=InstructBlipVQAPromptConstructor), |
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post_processor=dict(type=InstructBlipVQAPostProcessor), |
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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_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')] |
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instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth' |
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