mirror of https://github.com/open-mmlab/mmocr.git
118 lines
3.2 KiB
Python
118 lines
3.2 KiB
Python
_base_ = [
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'_base_fcenet_resnet50_fpn.py',
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'../_base_/datasets/totaltext.py',
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'../_base_/default_runtime.py',
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'../_base_/schedules/schedule_sgd_base.py',
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]
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default_hooks = dict(
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checkpoint=dict(
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type='CheckpointHook',
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save_best='icdar/hmean',
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rule='greater',
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_delete_=True))
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train_pipeline = [
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dict(type='LoadImageFromFile', 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='FixInvalidPolygon'),
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dict(
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type='RandomResize',
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scale=(800, 800),
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ratio_range=(0.75, 2.5),
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keep_ratio=True),
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dict(
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type='TextDetRandomCropFlip',
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crop_ratio=0.5,
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iter_num=1,
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min_area_ratio=0.2),
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dict(
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type='RandomApply',
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transforms=[dict(type='RandomCrop', min_side_ratio=0.3)],
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prob=0.8),
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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='RandomRotate',
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max_angle=30,
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pad_with_fixed_color=False,
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use_canvas=True)
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],
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prob=0.5),
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dict(
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type='RandomChoice',
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transforms=[[
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dict(type='Resize', scale=800, keep_ratio=True),
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dict(type='SourceImagePad', target_scale=800)
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],
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dict(type='Resize', scale=800, keep_ratio=False)],
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prob=[0.6, 0.4]),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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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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contrast=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(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(type='Resize', scale=(1280, 960), keep_ratio=True),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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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(type='FixInvalidPolygon'),
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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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optim_wrapper = dict(optimizer=dict(lr=1e-3, weight_decay=5e-4))
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train_cfg = dict(max_epochs=1500)
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# learning policy
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param_scheduler = [
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dict(type='StepLR', gamma=0.8, step_size=200, end=1200),
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]
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# dataset settings
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totaltext_textdet_train = _base_.totaltext_textdet_train
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totaltext_textdet_test = _base_.totaltext_textdet_test
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totaltext_textdet_train.pipeline = train_pipeline
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totaltext_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=16,
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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=totaltext_textdet_train)
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val_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=totaltext_textdet_test)
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test_dataloader = val_dataloader
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auto_scale_lr = dict(base_batch_size=16)
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find_unused_parameters = True
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