mirror of https://github.com/open-mmlab/mmocr.git
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
_base_ = [
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'../_base_/datasets/ctw1500.py',
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'../_base_/default_runtime.py',
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'../_base_/schedules/schedule_adam_600e.py',
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'_base_panet_resnet18_fpem-ffm.py',
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]
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model = dict(det_head=dict(module_loss=dict(shrink_ratio=(1, 0.7))))
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default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), )
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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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color_type='color_ignore_orientation'),
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dict(
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type='LoadOCRAnnotations',
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with_polygon=True,
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with_bbox=True,
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with_label=True,
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),
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dict(type='ShortScaleAspectJitter', short_size=640, scale_divisor=32),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(type='RandomRotate', max_angle=10),
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dict(type='TextDetRandomCrop', target_size=(640, 640)),
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dict(type='Pad', size=(640, 640)),
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dict(
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type='TorchVisionWrapper',
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op='ColorJitter',
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brightness=32.0 / 255,
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saturation=0.5),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
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]
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test_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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color_type='color_ignore_orientation'),
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# TODO Replace with mmcv.RescaleToShort when it's ready
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dict(
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type='ShortScaleAspectJitter',
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short_size=640,
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scale_divisor=1,
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ratio_range=(1.0, 1.0),
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aspect_ratio_range=(1.0, 1.0)),
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dict(
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type='LoadOCRAnnotations',
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with_polygon=True,
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with_bbox=True,
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with_label=True),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
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]
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# dataset settings
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ctw1500_textdet_train = _base_.ctw1500_textdet_train
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ctw1500_textdet_test = _base_.ctw1500_textdet_test
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# pipeline settings
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ctw1500_textdet_train.pipeline = train_pipeline
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ctw1500_textdet_test.pipeline = test_pipeline
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train_dataloader = dict(
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batch_size=16,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=ctw1500_textdet_train)
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val_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=ctw1500_textdet_test)
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test_dataloader = val_dataloader
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val_evaluator = dict(
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type='HmeanIOUMetric', pred_score_thrs=dict(start=0.3, stop=1, step=0.05))
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test_evaluator = val_evaluator
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auto_scale_lr = dict(base_batch_size=16)
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