llava_7b_scienceqa.py

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', '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)

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')]