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* [Feature]: Refactor class name * [Feature]: Add minigpt-4 coco caption * [Feature]: Update minigpt-4 coco caption * [Feature]: Add MiniGPT-4 ScienceQA * [Feature]: Add minigpt-4 vqav2 * [Feature]: Add VSR * [Feature]: Revert task to previous version
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
from opencompass.multimodal.models.minigpt_4 import (
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MiniGPT4COCOCaotionPromptConstructor,
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MiniGPT4COCOCaptionPostProcessor,
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)
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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=(384, 384),
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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', algorithm_keys=['image_id'])
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]
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dataset = dict(type='mmpretrain.COCOCaption',
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data_root='data/coco',
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data_prefix=dict(img_path='images'),
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ann_file='annotations/coco_karpathy_val.json',
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pipeline=val_pipeline)
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minigpt_4_coco_caption_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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# model settings
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minigpt_4_coco_caption_model = dict(
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type='minigpt-4',
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low_resource=False,
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img_size=384,
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llama_model='/path/to/vicuna-7b/',
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prompt_constructor=dict(type=MiniGPT4COCOCaotionPromptConstructor,
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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=MiniGPT4COCOCaptionPostProcessor))
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# evaluation settings
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minigpt_4_coco_caption_evaluator = [
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dict(
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type='mmpretrain.COCOCaption',
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ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
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) # noqa
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]
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minigpt_4_coco_caption_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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