55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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from opencompass.multimodal.models.instructblip import (
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InstructBlipCOCOCaotionPromptConstructor,
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InstructBlipCOCOCaptionPostProcessor,
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)
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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=(384, 384),
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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', algorithm_keys=['image_id'])
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]
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dataset = dict(type='mmpretrain.Flickr30kCaption',
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data_root='data/flickr30k',
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ann_file='annotations/dataset_flickr30k.json',
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data_prefix='images',
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split='val',
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pipeline=val_pipeline)
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instruct_blip_flickr30k_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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# model settings
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instruct_blip_flickr30k_model = dict(
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type='blip2-vicuna-instruct',
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prompt_constructor=dict(type=InstructBlipCOCOCaotionPromptConstructor),
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post_processor=dict(type=InstructBlipCOCOCaptionPostProcessor),
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freeze_vit=True,
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low_resource=False,
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llm_model='/path/to/vicuna-7b/',
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img_size=384,
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is_caption_task=True,
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)
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# evaluation settings
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instruct_blip_flickr30k_evaluator = [
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dict(
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type='mmpretrain.COCOCaption',
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ann_file='data/flickr30k/annotations/flickr30k_val_gt.json',
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) # noqa
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]
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instruct_blip_load_from = '/path/to/instruct_blip_vicuna7b_trimmed.pth'
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