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* [Feat] Add public dataset support of VisualGLM. * [Feat] Refactor LLaVA. * [Feat] Add public dataset support of LlaVA. * [Fix] Add arg.
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
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# dataloader settings
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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(
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type='mmpretrain.TextVQA',
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data_root='data/textvqa',
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ann_file='annotations/TextVQA_0.5.1_val.json',
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pipeline=val_pipeline,
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data_prefix='images/train_images',
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)
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llava_textvqa_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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# model settings
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llava_textvqa_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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) # noqa
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# evaluation settings
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llava_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
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