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3f601f420b
* [Feat] Add public dataset support of VisualGLM. * [Feat] Refactor LLaVA. * [Feat] Add public dataset support of LlaVA. * [Fix] Add arg.
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from opencompass.multimodal.models.visualglm import (VisualGLMBasePostProcessor, VisualGLMMMBenchPromptConstructor)
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# dataloader settings
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val_pipeline = [
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dict(type='mmpretrain.torchvision/Resize',
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size=(224, 224),
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interpolation=3),
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dict(type='mmpretrain.torchvision/ToTensor'),
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dict(type='mmpretrain.torchvision/Normalize',
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mean=(0.48145466, 0.4578275, 0.40821073),
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std=(0.26862954, 0.26130258, 0.27577711)),
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dict(type='mmpretrain.PackInputs',
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algorithm_keys=[
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'question', 'options', 'category', 'l2-category', 'context',
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'index', 'options_dict'
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])
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]
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dataset = dict(type='opencompass.MMBenchDataset',
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data_file='data/mmbench/mmbench_test_20230712.tsv',
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pipeline=val_pipeline)
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mmbench_dataloader = dict(batch_size=1,
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num_workers=4,
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dataset=dataset,
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collate_fn=dict(type='pseudo_collate'),
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sampler=dict(type='DefaultSampler', shuffle=False))
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# model settings
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visualglm_model = dict(
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type='visualglm',
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pretrained_path='/path/to/visualglm', # or Huggingface repo id
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prompt_constructor=dict(type=VisualGLMMMBenchPromptConstructor),
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post_processor=dict(type=VisualGLMBasePostProcessor)
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)
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# evaluation settings
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mmbench_evaluator = [
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dict(type='opencompass.DumpResults',
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save_path='work_dirs/visualglm-6b-mmbench.xlsx')
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]
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