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[Feature]: Add other public datasets config (#214)
* [Feature]: Add flickr30k * [Feature]: Add GQA * [Feature]: Add OCR VQA * [Feature]: Add OK VQA * [Feature]: Add text vqa * [Feature]: Add other vqa
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Yuan Liu
GitHub
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53
configs/multimodal/minigpt_4/minigpt_4_7b_flickr30k.py
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53
configs/multimodal/minigpt_4/minigpt_4_7b_flickr30k.py
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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.Flickr30kCaption',
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data_root='data/flickr30k',
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ann_file='annotations/dataset_flickr30k.json',
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data_prefix='images',
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split='val',
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pipeline=val_pipeline)
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minigpt_4_flickr30k_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_flickr30k_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_flickr30k_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_flickr30k_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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52
configs/multimodal/minigpt_4/minigpt_4_7b_gqa.py
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configs/multimodal/minigpt_4/minigpt_4_7b_gqa.py
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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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# 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(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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minigpt_4_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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# model settings
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minigpt_4_gqa_model = dict(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-7b/',
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prompt_constructor=dict(
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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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# evaluation settings
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minigpt_4_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]
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minigpt_4_gqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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53
configs/multimodal/minigpt_4/minigpt_4_7b_ocr-vqa.py
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configs/multimodal/minigpt_4/minigpt_4_7b_ocr-vqa.py
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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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# 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(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.OCRVQA',
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data_root='data/ocrvqa',
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ann_file='annotations/dataset.json',
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split='test',
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data_prefix='images',
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pipeline=val_pipeline)
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minigpt_4_ocr_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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# model settings
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minigpt_4_ocr_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-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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# evaluation settings
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minigpt_4_ocr_vqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
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minigpt_4_ocr_vqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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55
configs/multimodal/minigpt_4/minigpt_4_7b_ok-vqa.py
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configs/multimodal/minigpt_4/minigpt_4_7b_ok-vqa.py
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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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# 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(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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# model settings
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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-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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# evaluation settings
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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' # noqa
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55
configs/multimodal/minigpt_4/minigpt_4_7b_textvqa.py
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55
configs/multimodal/minigpt_4/minigpt_4_7b_textvqa.py
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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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# 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(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.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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minigpt_4_textvqa_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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# model settings
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minigpt_4_textvqa_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-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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# evaluation settings
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minigpt_4_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
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minigpt_4_textvqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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52
configs/multimodal/minigpt_4/minigpt_4_7b_vizwiz.py
Normal file
52
configs/multimodal/minigpt_4/minigpt_4_7b_vizwiz.py
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@ -0,0 +1,52 @@
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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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# 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(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.VizWiz',
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data_root='data/vizwiz/',
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data_prefix='Images/val',
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ann_file='Annotations/val.json',
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pipeline=val_pipeline)
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minigpt_4_vizwiz_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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# model settings
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minigpt_4_vizwiz_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-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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# evaluation settings
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minigpt_4_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]
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minigpt_4_vizwiz_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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