mirror of https://github.com/open-mmlab/mmocr.git
163 lines
4.2 KiB
Python
163 lines
4.2 KiB
Python
_base_ = [
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'_base_svtr-tiny.py',
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'../_base_/default_runtime.py',
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'../_base_/datasets/mjsynth.py',
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'../_base_/datasets/synthtext.py',
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'../_base_/datasets/cute80.py',
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'../_base_/datasets/iiit5k.py',
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'../_base_/datasets/svt.py',
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'../_base_/datasets/svtp.py',
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'../_base_/datasets/icdar2013.py',
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'../_base_/datasets/icdar2015.py',
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'../_base_/schedules/schedule_adam_base.py',
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]
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(
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type='AdamW',
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lr=5 / (10**4) * 2048 / 2048,
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betas=(0.9, 0.99),
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eps=8e-8,
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weight_decay=0.05))
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=0.5,
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end_factor=1.,
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end=2,
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verbose=False,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=19,
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begin=2,
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end=20,
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verbose=False,
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convert_to_iter_based=True),
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]
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file_client_args = dict(backend='disk')
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train_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args=file_client_args,
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ignore_empty=True,
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min_size=5),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(type='TextRecogGeneralAug', ),
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],
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),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(type='CropHeight', ),
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],
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),
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dict(
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type='ConditionApply',
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condition='min(results["img_shape"])>10',
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true_transforms=dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(
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type='TorchVisionWrapper',
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op='GaussianBlur',
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kernel_size=5,
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sigma=1,
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),
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],
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)),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(
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type='TorchVisionWrapper',
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op='ColorJitter',
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brightness=0.5,
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saturation=0.5,
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contrast=0.5,
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hue=0.1),
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]),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(type='ImageContentJitter', ),
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],
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),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(
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type='ImgAugWrapper',
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args=[dict(cls='AdditiveGaussianNoise', scale=0.1**0.5)]),
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],
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),
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dict(
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type='RandomApply',
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prob=0.4,
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transforms=[
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dict(type='ReversePixels', ),
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],
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),
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dict(type='Resize', scale=(256, 64)),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
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]
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test_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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dict(type='Resize', scale=(256, 64)),
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dict(type='LoadOCRAnnotations', with_text=True),
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dict(
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type='PackTextRecogInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'valid_ratio'))
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]
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# dataset settings
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train_list = [_base_.mjsynth_textrecog_test, _base_.synthtext_textrecog_train]
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test_list = [
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_base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test,
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_base_.svt_textrecog_test, _base_.svtp_textrecog_test,
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_base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
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]
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val_evaluator = dict(
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dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15'])
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test_evaluator = val_evaluator
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train_dataloader = dict(
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batch_size=512,
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num_workers=24,
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persistent_workers=True,
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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=128,
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num_workers=8,
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persistent_workers=True,
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pin_memory=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type='ConcatDataset', datasets=test_list, pipeline=test_pipeline))
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test_dataloader = val_dataloader
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