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* [Feat] Migrate blip caption to mmpretrain. (#50) * Migrate blip caption to mmpretrain * minor fix * support train * [Feature] Support OFA caption task. (#51) * [Feature] Support OFA caption task. * Remove duplicated files. * [Feature] Support OFA vqa task. (#58) * [Feature] Support OFA vqa task. * Fix lint. * [Feat] Add BLIP retrieval to mmpretrain. (#55) * init * minor fix for train * fix according to comments * refactor * Update Blip retrieval. (#62) * [Feature] Support OFA visual grounding task. (#59) * [Feature] Support OFA visual grounding task. * minor add TODO --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feat] Add flamingos coco caption and vqa. (#60) * first init * init flamingo coco * add vqa * minor fix * remove unnecessary modules * Update config * Use `ApplyToList`. --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature]: BLIP2 coco retrieval (#53) * [Feature]: Add blip2 retriever * [Feature]: Add blip2 all modules * [Feature]: Refine model * [Feature]: x1 * [Feature]: Runnable coco ret * [Feature]: Runnable version * [Feature]: Fix lint * [Fix]: Fix lint * [Feature]: Use 364 img size * [Feature]: Refactor blip2 * [Fix]: Fix lint * refactor files * minor fix * minor fix --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * Remove * fix blip caption inputs (#68) * [Feat] Add BLIP NLVR support. (#67) * first init * init flamingo coco * add vqa * add nlvr * refactor nlvr * minor fix * minor fix * Update dataset --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature]: BLIP2 Caption (#70) * [Feature]: Add language model * [Feature]: blip2 caption forward * [Feature]: Reproduce the results * [Feature]: Refactor caption * refine config --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feat] Migrate BLIP VQA to mmpretrain (#69) * reformat * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * change * refactor code --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * Update RefCOCO dataset * [Fix] fix lint * [Feature] Implement inference APIs for multi-modal tasks. (#65) * [Feature] Implement inference APIs for multi-modal tasks. * [Project] Add gradio demo. * [Improve] Update requirements * Update flamingo * Update blip * Add NLVR inferencer * Update flamingo * Update hugging face model register * Update ofa vqa * Update BLIP-vqa (#71) * Update blip-vqa docstring (#72) * Refine flamingo docstring (#73) * [Feature]: BLIP2 VQA (#61) * [Feature]: VQA forward * [Feature]: Reproduce accuracy * [Fix]: Fix lint * [Fix]: Add blank line * minor fix --------- Co-authored-by: yingfhu <yingfhu@gmail.com> * [Feature]: BLIP2 docstring (#74) * [Feature]: Add caption docstring * [Feature]: Add docstring to blip2 vqa * [Feature]: Add docstring to retrieval * Update BLIP-2 metafile and README (#75) * [Feature]: Add readme and docstring * Update blip2 results --------- Co-authored-by: mzr1996 <mzr1996@163.com> * [Feature] BLIP Visual Grounding on MMPretrain Branch (#66) * blip grounding merge with mmpretrain * remove commit * blip grounding test and inference api * refcoco dataset * refcoco dataset refine config * rebasing * gitignore * rebasing * minor edit * minor edit * Update blip-vqa docstring (#72) * rebasing * Revert "minor edit" This reverts commit 639cec757c215e654625ed0979319e60f0be9044. * blip grounding final * precommit * refine config * refine config * Update blip visual grounding --------- Co-authored-by: Yiqin Wang 王逸钦 <wyq1217@outlook.com> Co-authored-by: mzr1996 <mzr1996@163.com> * Update visual grounding metric * Update OFA docstring, README and metafiles. (#76) * [Docs] Update installation docs and gradio demo docs. (#77) * Update OFA name * Update Visual Grounding Visualizer * Integrate accelerate support * Fix imports. * Fix timm backbone * Update imports * Update README * Update circle ci * Update flamingo config * Add gradio demo README * [Feature]: Add scienceqa (#1571) * [Feature]: Add scienceqa * [Feature]: Change param name * Update docs * Update video --------- Co-authored-by: Hubert <42952108+yingfhu@users.noreply.github.com> Co-authored-by: yingfhu <yingfhu@gmail.com> Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> Co-authored-by: Yiqin Wang 王逸钦 <wyq1217@outlook.com> Co-authored-by: Rongjie Li <limo97@163.com>
106 lines
2.6 KiB
Python
106 lines
2.6 KiB
Python
# data settings
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data_preprocessor = dict(
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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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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomApply',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.1,
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backend='cv2')
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],
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prob=0.5),
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dict(
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type='mmdet.RandomCrop',
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crop_type='relative_range',
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crop_size=(0.8, 0.8),
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allow_negative_crop=False),
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dict(
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type='RandomChoiceResize',
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scales=[(384, 384), (360, 360), (344, 344), (312, 312), (300, 300),
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(286, 286), (270, 270)],
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keep_ratio=False),
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dict(
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type='RandomTranslatePad',
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size=384,
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aug_translate=True,
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),
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dict(type='CleanCaption', keys='text'),
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dict(
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type='PackInputs',
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algorithm_keys=['text', 'gt_bboxes', 'scale_factor'],
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meta_keys=['image_id'],
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),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='Resize',
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scale=(384, 384),
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interpolation='bicubic',
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backend='pillow'),
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dict(type='CleanCaption', keys='text'),
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dict(
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type='PackInputs',
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algorithm_keys=['text', 'gt_bboxes', 'scale_factor'],
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meta_keys=['image_id'],
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),
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]
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train_dataloader = dict(
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batch_size=16,
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num_workers=8,
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dataset=dict(
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type='RefCOCO',
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data_root='data/coco',
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data_prefix='train2014',
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ann_file='refcoco/instances.json',
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split_file='refcoco/refs(unc).p',
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split='train',
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pipeline=train_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=True),
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drop_last=True,
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)
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val_dataloader = dict(
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batch_size=16,
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num_workers=8,
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dataset=dict(
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type='RefCOCO',
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data_root='data/coco',
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data_prefix='train2014',
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ann_file='refcoco/instances.json',
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split_file='refcoco/refs(unc).p',
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split='val',
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pipeline=test_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=False),
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)
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val_evaluator = dict(type='VisualGroundingMetric')
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test_dataloader = dict(
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batch_size=16,
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num_workers=8,
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dataset=dict(
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type='RefCOCO',
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data_root='data/coco',
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data_prefix='train2014',
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ann_file='refcoco/instances.json',
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split_file='refcoco/refs(unc).p',
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split='testA', # or 'testB'
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pipeline=test_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=False),
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)
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test_evaluator = val_evaluator
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