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Unverified Commit ae3c1869 authored by Yuan Liu's avatar Yuan Liu Committed by GitHub
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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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configs/multimodal/minigpt_4/minigpt_4_7b_flickr30k.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4COCOCaotionPromptConstructor,
MiniGPT4COCOCaptionPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(384, 384),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(type='mmpretrain.PackInputs', algorithm_keys=['image_id'])
]
dataset = dict(type='mmpretrain.Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='val',
pipeline=val_pipeline)
minigpt_4_flickr30k_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False))
# model settings
minigpt_4_flickr30k_model = dict(
type='minigpt-4',
low_resource=False,
img_size=384,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4COCOCaotionPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4COCOCaptionPostProcessor))
# evaluation settings
minigpt_4_flickr30k_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
) # noqa
]
minigpt_4_flickr30k_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
configs/multimodal/minigpt_4/minigpt_4_7b_gqa.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4VQAPromptConstructor,
MiniGPT4VQAPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(224, 224),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(
type='mmpretrain.PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(type='mmpretrain.GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/testdev_balanced_questions.json',
pipeline=val_pipeline)
minigpt_4_gqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler',
shuffle=False))
# model settings
minigpt_4_gqa_model = dict(type='minigpt-4',
low_resource=False,
img_size=224,
max_length=10,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(
type=MiniGPT4VQAPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4VQAPostProcessor))
# evaluation settings
minigpt_4_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]
minigpt_4_gqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
configs/multimodal/minigpt_4/minigpt_4_7b_ocr-vqa.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4VQAPromptConstructor,
MiniGPT4VQAPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(224, 224),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(
type='mmpretrain.PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(type='mmpretrain.OCRVQA',
data_root='data/ocrvqa',
ann_file='annotations/dataset.json',
split='test',
data_prefix='images',
pipeline=val_pipeline)
minigpt_4_ocr_vqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler',
shuffle=False))
# model settings
minigpt_4_ocr_vqa_model = dict(
type='minigpt-4',
low_resource=False,
img_size=224,
max_length=10,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4VQAPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4VQAPostProcessor))
# evaluation settings
minigpt_4_ocr_vqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
minigpt_4_ocr_vqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
configs/multimodal/minigpt_4/minigpt_4_7b_ok-vqa.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4VQAPromptConstructor,
MiniGPT4VQAPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(224, 224),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(
type='mmpretrain.PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(
type='mmpretrain.COCOVQA',
data_root='data/okvqa',
question_file='annotations/OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/mscoco_val2014_annotations.json',
pipeline=val_pipeline,
data_prefix='images/val2014',
)
minigpt_4_ok_vqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler',
shuffle=False))
# model settings
minigpt_4_ok_vqa_model = dict(
type='minigpt-4',
low_resource=False,
img_size=224,
max_length=10,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4VQAPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4VQAPostProcessor))
# evaluation settings
minigpt_4_ok_vqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
minigpt_4_ok_vqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
configs/multimodal/minigpt_4/minigpt_4_7b_textvqa.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4VQAPromptConstructor,
MiniGPT4VQAPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(224, 224),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(
type='mmpretrain.PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(
type='mmpretrain.TextVQA',
data_root='data/textvqa',
ann_file='annotations/TextVQA_0.5.1_val.json',
pipeline=val_pipeline,
data_prefix='images/train_images',
)
minigpt_4_textvqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler',
shuffle=False))
# model settings
minigpt_4_textvqa_model = dict(
type='minigpt-4',
low_resource=False,
img_size=224,
max_length=10,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4VQAPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4VQAPostProcessor))
# evaluation settings
minigpt_4_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
minigpt_4_textvqa_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
configs/multimodal/minigpt_4/minigpt_4_7b_vizwiz.py 0 → 100644
View file @ ae3c1869
from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4VQAPromptConstructor,
MiniGPT4VQAPostProcessor,
)
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.LoadImageFromFile'),
dict(type='mmpretrain.ToPIL', to_rgb=True),
dict(type='mmpretrain.torchvision/Resize',
size=(224, 224),
interpolation=3),
dict(type='mmpretrain.torchvision/ToTensor'),
dict(type='mmpretrain.torchvision/Normalize',
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711)),
dict(
type='mmpretrain.PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
)
]
dataset = dict(type='mmpretrain.VizWiz',
data_root='data/vizwiz/',
data_prefix='Images/val',
ann_file='Annotations/val.json',
pipeline=val_pipeline)
minigpt_4_vizwiz_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler',
shuffle=False))
# model settings
minigpt_4_vizwiz_model = dict(
type='minigpt-4',
low_resource=False,
img_size=224,
max_length=10,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4VQAPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4VQAPostProcessor))
# evaluation settings
minigpt_4_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]
minigpt_4_vizwiz_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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