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from opencompass.multimodal.models.minigpt_4 import (
MiniGPT4MMBenchPromptConstructor, MiniGPT4MMBenchPostProcessor)
# dataloader settings
val_pipeline = [
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', 'category', 'l2-category', 'context', 'index',
'options_dict', 'options', 'split'
])
]
dataset = dict(type='opencompass.MMBenchDataset',
data_file='data/mmbench/mmbench_test_20230712.tsv',
pipeline=val_pipeline)
minigpt_4_mmbench_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_mmbench_model = dict(
type='minigpt-4',
low_resource=False,
llama_model='/path/to/vicuna-7b/',
prompt_constructor=dict(type=MiniGPT4MMBenchPromptConstructor,
image_prompt='###Human: <Img><ImageHere></Img>',
reply_prompt='###Assistant:'),
post_processor=dict(type=MiniGPT4MMBenchPostProcessor))
# evaluation settings
minigpt_4_mmbench_evaluator = [
dict(type='opencompass.DumpResults',
save_path='work_dirs/minigpt-4-7b-mmbench.xlsx')
]
minigpt_4_mmbench_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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