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from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMMMBenchPromptConstructor)
# 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', 'options', 'category', 'l2-category', 'context',
'index', 'options_dict'
])
]
dataset = dict(type='opencompass.MMBenchDataset',
data_file='data/mmbench/mmbench_test_20230712.tsv',
pipeline=val_pipeline)
visualglm_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
visualglm_mmbench_model = dict(
type='visualglm',
pretrained_path='/path/to/visualglm', # or Huggingface repo id
prompt_constructor=dict(type=VisualGLMMMBenchPromptConstructor),
post_processor=dict(type=VisualGLMBasePostProcessor),
gen_kwargs=dict(max_new_tokens=50,num_beams=5,do_sample=False,repetition_penalty=1.0,length_penalty=-1.0)
)
# evaluation settings
visualglm_mmbench_evaluator = [
dict(type='opencompass.DumpResults',
save_path='work_dirs/visualglm-6b-mmbench.xlsx')
]
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