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* [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>
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
from opencompass.multimodal.models.openflamingo import OpenFlamingoVQAPromptConstructor
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# dataloader settings
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val_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='mmpretrain.ResizeEdge',
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scale=224,
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interpolation='bicubic',
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backend='pillow'),
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dict(type='CenterCrop', crop_size=(224, 224)),
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dict(
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type='mmpretrain.PackInputs',
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algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
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meta_keys=['question_id', 'image_id'],
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)
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]
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dataset = dict(
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type='mmpretrain.COCOVQA',
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data_root='data/okvqa',
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question_file='annotations/OpenEnded_mscoco_val2014_questions.json',
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ann_file='annotations/mscoco_val2014_annotations.json',
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pipeline=val_pipeline,
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data_prefix='images/val2014',
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)
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openflamingo_okvqa_dataloader = dict(
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batch_size=8,
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num_workers=4,
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dataset=dataset,
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sampler=dict(type='DefaultSampler', shuffle=False),
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collate_fn=dict(type='default_collate'),
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persistent_workers=True,
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)
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# model settings
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openflamingo_okvqa_model = dict(
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type='openflamingo',
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data_preprocessor=dict(
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type='mmpretrain.MultiModalDataPreprocessor',
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mean=[122.770938, 116.7460125, 104.09373615],
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std=[68.5005327, 66.6321579, 70.32316305],
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to_rgb=True,
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),
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tokenizer=dict(type='mmpretrain.LlamaTokenizer',
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name_or_path='decapoda-research/llama-7b-hf'),
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vision_encoder=dict(
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type='mmpretrain.VisionTransformer',
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arch='l',
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patch_size=14,
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pre_norm=True,
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norm_cfg=dict(type='LN', eps=1e-5),
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layer_cfgs=dict(act_cfg=dict(type='mmpretrain.QuickGELU')),
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final_norm=False,
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out_type='raw',
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pretrained= # noqa: E251
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'/path/to/vision/encoder', # noqa
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),
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lang_encoder=dict(
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base=dict(type='mmpretrain.AutoModelForCausalLM',
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name_or_path=
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'decapoda-research/llama-7b-hf',
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local_files_only=True),
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adapter=dict(type='mmpretrain.FlamingoLMAdapter',
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vis_hidden_size=1024,
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cross_attn_every_n_layers=4,
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use_media_placement_augmentation=False),
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),
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task='vqa',
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generation_cfg=dict(num_beams=3, max_new_tokens=20, length_penalty=-2.0),
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prompt_constructor=dict(type=OpenFlamingoVQAPromptConstructor)
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)
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# evaluation settings
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openflamingo_okvqa_evaluator = [dict(type='mmpretrain.VQAAcc')]
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openflamingo_load_from = '/path/to/pretrained/weights' # noqa
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