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from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor |
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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( |
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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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), |
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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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llava_gqa_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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) |
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llava_gqa_model = dict( |
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type='llava', |
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model_path='/path/to/llava', |
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prompt_constructor=dict(type=LLaVAVQAPromptConstructor), |
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post_processor=dict(type=LLaVABasePostProcessor) |
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) |
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llava_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')] |
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