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from opencompass.multimodal.models.minigpt_4 import ( |
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MiniGPT4VQAPromptConstructor, |
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MiniGPT4VQAPostProcessor, |
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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( |
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type='mmpretrain.COCOVQA', |
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data_root='data/okvqa', |
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question_file='annotations/OpenEnded_mscoco_val2014_questions.json', |
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ann_file='annotations/mscoco_val2014_annotations.json', |
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pipeline=val_pipeline, |
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data_prefix='images/val2014', |
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) |
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minigpt_4_ok_vqa_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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minigpt_4_ok_vqa_model = dict( |
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type='minigpt-4', |
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low_resource=False, |
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img_size=224, |
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max_length=10, |
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llama_model='/path/to/vicuna_weights_7b/', |
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prompt_constructor=dict(type=MiniGPT4VQAPromptConstructor, |
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image_prompt='###Human: <Img><ImageHere></Img>', |
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reply_prompt='###Assistant:'), |
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post_processor=dict(type=MiniGPT4VQAPostProcessor)) |
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minigpt_4_ok_vqa_evaluator = [dict(type='mmpretrain.VQAAcc')] |
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minigpt_4_ok_vqa_load_from = '/path/to/prerained_minigpt4_7b.pth' |
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