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from opencompass.multimodal.models.qwen import QwenVLMMBenchPromptConstructor, QwenVLBasePostProcessor
# dataloader settings
val_pipeline = [
dict(type='mmpretrain.torchvision/Resize',
size=(448, 448),
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)
qwen_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
qwen_model = dict(
type='qwen-vl-base',
pretrained_path='Qwen/Qwen-VL', # or Huggingface repo id
prompt_constructor=dict(type=QwenMMBenchPromptConstructor),
post_processor=dict(type=QwenVLBasePostProcessor)
)
# evaluation settings
qwen_mmbench_evaluator = [
dict(type='opencompass.DumpResults',
save_path='work_dirs/qwenvl-base-7b-mmbench.xlsx')
]
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