OpenCompass/configs/multimodal/minigpt_4/minigpt_4_7b_mmbench.py
2023-08-08 14:21:58 +08:00

43 lines
1.4 KiB
Python

# 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_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_model = dict(
type='minigpt-4-mmbench',
low_resource=True,
llama_model='/path/to/vicuna',
sys_prompt= # noqa: E251
'###Human: What is the capital of China? There are several options:\nA. Beijing\nB. Shanghai\nC. Guangzhou\nD. Shenzhen\n###Assistant: A\n'
)
# evaluation settings
minigpt_4_evaluator = [
dict(type='opencompass.DumpResults',
save_path='work_dirs/minigpt-4-7b-mmbench.xlsx')
]
minigpt_4_load_from = '/path/to/minigpt-4' # noqa