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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