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bd50bad8b5
* [Feat] Add public dataset support for visualglm, qwenvl, and flamingo * [Fix] MMBench related changes. * [Fix] Openflamingo inference. * [Fix] Hide ckpt path. * [Fix] Pre-commit. --------- Co-authored-by: Haodong Duan <dhd.efz@gmail.com>
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
from opencompass.multimodal.models.mplug_owl import (
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MplugOwlMMBenchPostProcessor, MplugOwlMMBenchPromptConstructor)
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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(
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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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),
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dict(
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type='mmpretrain.PackInputs',
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algorithm_keys=[
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'question', 'answer', 'category', 'l2-category', 'context',
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'index', 'options_dict', 'options'
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],
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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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mplug_owl_mmbench_dataloader = dict(
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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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)
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# model settings
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mplug_owl_mmbench_model = dict(
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type='mplug_owl-7b',
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model_path='/mplug-owl-llama-7b-ft',
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prompt_constructor=dict(type=MplugOwlMMBenchPromptConstructor),
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post_processor=dict(type=MplugOwlMMBenchPostProcessor)
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) # noqa
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# evaluation settings
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mplug_owl_mmbench_evaluator = [
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dict(type='opencompass.DumpResults',
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save_path='work_dirs/mplug_owl-7b-mmagibench-v0.1.0.xlsx')
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]
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