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| _base_ = [ | |
| '_base_fcenet_resnet50_fpn.py', | |
| '../_base_/datasets/totaltext.py', | |
| '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_sgd_base.py', | |
| ] | |
| default_hooks = dict( | |
| checkpoint=dict( | |
| type='CheckpointHook', | |
| save_best='icdar/hmean', | |
| rule='greater', | |
| _delete_=True)) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True, | |
| ), | |
| dict(type='FixInvalidPolygon'), | |
| dict( | |
| type='RandomResize', | |
| scale=(800, 800), | |
| ratio_range=(0.75, 2.5), | |
| keep_ratio=True), | |
| dict( | |
| type='TextDetRandomCropFlip', | |
| crop_ratio=0.5, | |
| iter_num=1, | |
| min_area_ratio=0.2), | |
| dict( | |
| type='RandomApply', | |
| transforms=[dict(type='RandomCrop', min_side_ratio=0.3)], | |
| prob=0.8), | |
| dict( | |
| type='RandomApply', | |
| transforms=[ | |
| dict( | |
| type='RandomRotate', | |
| max_angle=30, | |
| pad_with_fixed_color=False, | |
| use_canvas=True) | |
| ], | |
| prob=0.5), | |
| dict( | |
| type='RandomChoice', | |
| transforms=[[ | |
| dict(type='Resize', scale=800, keep_ratio=True), | |
| dict(type='SourceImagePad', target_scale=800) | |
| ], | |
| dict(type='Resize', scale=800, keep_ratio=False)], | |
| prob=[0.6, 0.4]), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict( | |
| type='TorchVisionWrapper', | |
| op='ColorJitter', | |
| brightness=32.0 / 255, | |
| saturation=0.5, | |
| contrast=0.5), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1280, 960), keep_ratio=True), | |
| # add loading annotation after ``Resize`` because ground truth | |
| # does not need to do resize data transform | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict(type='FixInvalidPolygon'), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
| ] | |
| optim_wrapper = dict(optimizer=dict(lr=1e-3, weight_decay=5e-4)) | |
| train_cfg = dict(max_epochs=1500) | |
| # learning policy | |
| param_scheduler = [ | |
| dict(type='StepLR', gamma=0.8, step_size=200, end=1200), | |
| ] | |
| # dataset settings | |
| totaltext_textdet_train = _base_.totaltext_textdet_train | |
| totaltext_textdet_test = _base_.totaltext_textdet_test | |
| totaltext_textdet_train.pipeline = train_pipeline | |
| totaltext_textdet_test.pipeline = test_pipeline | |
| train_dataloader = dict( | |
| batch_size=16, | |
| num_workers=16, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=totaltext_textdet_train) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=1, | |
| persistent_workers=True, | |
| pin_memory=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=totaltext_textdet_test) | |
| test_dataloader = val_dataloader | |
| auto_scale_lr = dict(base_batch_size=16) | |
| find_unused_parameters = True | |