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"composable_kernel/include/config.hpp" did not exist on "ac62d13ecdf1d959e62582a6bce017d6e4f90692"
Unverified Commit 3f601f42 authored by Yike Yuan's avatar Yike Yuan Committed by GitHub
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[Feat] Support public dataset of visualglm and llava. (#265) 

* [Feat] Add public dataset support of VisualGLM.

* [Feat] Refactor LLaVA.

* [Feat] Add public dataset support of LlaVA.

* [Fix] Add  arg.
parent dc6e54f6
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configs/multimodal/llava/llava_7b_coco_caption.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVABasePromptConstructor, LLaVABasePostProcessor
# 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=['image_id']),
]
dataset = dict(type='mmpretrain.COCOCaption',
data_root='data/coco',
data_prefix=dict(img_path='images'),
ann_file='annotations/coco_karpathy_val.json',
pipeline=val_pipeline)
llava_coco_caption_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_coco_caption_model = dict(
type='llava',
model_path='/path/to/llava',
is_caption_task=True,
prompt_constructor=dict(type=LLaVABasePromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_coco_caption_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
) # noqa
]
configs/multimodal/llava/llava_7b_flickr30k.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVABasePromptConstructor, LLaVABasePostProcessor
# 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=['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)
llava_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
llava_flickr30k_model = dict(
type='llava',
model_path='/path/to/llava',
is_caption_task=True,
prompt_constructor=dict(type=LLaVABasePromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_flickr30k_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
) # noqa
]
configs/multimodal/llava/llava_7b_gqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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)
llava_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
llava_gqa_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]
configs/multimodal/llava/llava_7b_mmbench.py
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAMMBenchPromptConstructor, LLaVABasePostProcessor
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.torchvision/Resize',
......@@ -34,6 +36,8 @@ mmbench_dataloader = dict(
llava_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAMMBenchPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
......
configs/multimodal/llava/llava_7b_ocr_vqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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)
llava_ocrvqa_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_ocrvqa_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_ocrvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/llava/llava_7b_ok_vqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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',
)
llava_okvqa_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_okvqa_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_okvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/llava/llava_7b_scienceqa.py 0 → 100644
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from opencompass.multimodal.models.llava import LLaVAScienceQAPromptConstructor, LLaVABasePostProcessor
# 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', 'choices', 'hint', 'lecture', 'solution'
])
]
dataset = dict(type='mmpretrain.ScienceQA',
data_root='./data/scienceqa',
split='val',
split_file='pid_splits.json',
ann_file='problems.json',
image_only=True,
data_prefix=dict(img_path='val'),
pipeline=val_pipeline)
llava_scienceqa_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_scienceqa_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAScienceQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]
configs/multimodal/llava/llava_7b_textvqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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',
)
llava_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
llava_textvqa_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/llava/llava_7b_vizwiz.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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)
llava_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
llava_vizwiz_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/llava/llava_7b_vqav2.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVABasePostProcessor
# 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/coco',
data_prefix='images/val2014',
question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=val_pipeline)
llava_vqav2_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_vqav2_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVABasePostProcessor)
) # noqa
# evaluation settings
llava_vqav2_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/llava/llava_7b_vsr.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.llava import LLaVAVQAPromptConstructor, LLaVAVSRPostProcessor
# 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.VSR',
data_root='data/vsr/',
data_prefix='images/',
ann_file='annotations/test.json',
pipeline=val_pipeline)
llava_vsr_dataloader = dict(
batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False),
)
# model settings
llava_vsr_model = dict(
type='llava',
model_path='/path/to/llava',
prompt_constructor=dict(type=LLaVAVQAPromptConstructor),
post_processor=dict(type=LLaVAVSRPostProcessor)
) # noqa
# evaluation settings
llava_vsr_evaluator = [dict(type='mmpretrain.GQAAcc')]
configs/multimodal/visualglm/visualglm_6b_coco_caption.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMBasePromptConstructor)
# 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=['image_id'])
]
dataset = dict(type='mmpretrain.COCOCaption',
data_root='data/coco',
data_prefix=dict(img_path='images'),
ann_file='annotations/coco_karpathy_val.json',
pipeline=val_pipeline)
visualglm_coco_caption_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False))
# model settings
visualglm_coco_caption_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
is_caption_task=True,
prompt_constructor=dict(type=VisualGLMBasePromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_coco_caption_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
) # noqa
]
configs/multimodal/visualglm/visualglm_6b_flickr30k.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMBasePromptConstructor)
# 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=['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)
visualglm_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
visualglm_flickr30k_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
is_caption_task=True,
prompt_constructor=dict(type=VisualGLMBasePromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_flickr30k_evaluator = [
dict(
type='mmpretrain.COCOCaption',
ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
) # noqa
]
configs/multimodal/visualglm/visualglm_6b_gqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
# 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)
visualglm_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
visualglm_gqa_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_gqa_evaluator = [dict(type='mmpretrain.GQAAcc')]
configs/multimodal/visualglm/visualglm_6b_mmbench.py
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMPostProcessor, VisualGLMPromptConstructor)
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMMMBenchPromptConstructor)
# dataloader settings
val_pipeline = [
......@@ -30,8 +30,8 @@ mmbench_dataloader = dict(batch_size=1,
visualglm_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMPromptConstructor),
post_processor=dict(type=VisualGLMPostProcessor)
prompt_constructor=dict(type=VisualGLMMMBenchPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
......
configs/multimodal/visualglm/visualglm_6b_ocr_vqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
# 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)
visualglm_ocrvqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False))
# model settings
visualglm_ocrvqa_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_ocrvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/visualglm/visualglm_6b_ok_vqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
# 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',
)
visualglm_okvqa_dataloader = dict(batch_size=1,
num_workers=4,
dataset=dataset,
collate_fn=dict(type='pseudo_collate'),
sampler=dict(type='DefaultSampler', shuffle=False))
# model settings
visualglm_okvqa_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_okvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/visualglm/visualglm_6b_scienceqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMScienceQAPromptConstructor)
# 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', 'choices', 'hint', 'lecture', 'solution'
])
]
dataset = dict(type='mmpretrain.ScienceQA',
data_root='./data/scienceqa',
split='val',
split_file='pid_splits.json',
ann_file='problems.json',
image_only=True,
data_prefix=dict(img_path='val'),
pipeline=val_pipeline)
visualglm_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
visualglm_scienceqa_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMScienceQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_scienceqa_evaluator = [dict(type='mmpretrain.ScienceQAMetric')]
\ No newline at end of file
configs/multimodal/visualglm/visualglm_6b_textvqa.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
# 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',
)
visualglm_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
visualglm_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_textvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
configs/multimodal/visualglm/visualglm_6b_vizwiz.py 0 → 100644
View file @ 3f601f42
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMVQAPromptConstructor)
# 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)
visualglm_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
visualglm_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMVQAPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor)
)
# evaluation settings
visualglm_vizwiz_evaluator = [dict(type='mmpretrain.VQAAcc')]
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