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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', 'has_image'
])
]
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_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
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')] |