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* [Feat] Support multi-modal evaluation on MME benchmark. * [Fix] Remove debug code. * [Fix] Remove redundant codes and add type hints. * [Fix] Rename in config. * [Fix] Rebase main. * [Fix] Fix isort and yapf conflict.
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from opencompass.multimodal.models.minigpt_4 import (MiniGPT4MMEPostProcessor, MiniGPT4MMEPromptConstructor)
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# dataloader settings
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val_pipeline = [
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dict(type='mmpretrain.LoadImageFromFile'),
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dict(type='mmpretrain.ToPIL', to_rgb=True),
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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', 'answer', 'task'
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])
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]
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dataset = dict(type='opencompass.MMEDataset',
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data_dir='/path/to/MME',
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pipeline=val_pipeline)
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minigpt_4_mme_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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minigpt_4_model = dict(
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type='minigpt-4',
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low_resource=False,
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llama_model='/path/to/vicuna/',
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prompt_constructor=dict(type=MiniGPT4MMEPromptConstructor),
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post_processor=dict(type=MiniGPT4MMEPostProcessor))
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# evaluation settings
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minigpt_4_mme_evaluator = [
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dict(type='opencompass.MMEMetric')
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]
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minigpt_4_load_from = '/path/to/prerained_minigpt4_7b.pth' # noqa
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