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| file_client_args = dict(backend='disk') | |
| model = dict( | |
| type='DBNet', | |
| backbone=dict( | |
| type='mmdet.ResNet', | |
| depth=18, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=-1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'), | |
| norm_eval=False, | |
| style='caffe'), | |
| neck=dict( | |
| type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256), | |
| det_head=dict( | |
| type='DBHead', | |
| in_channels=256, | |
| module_loss=dict(type='DBModuleLoss'), | |
| postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')), | |
| data_preprocessor=dict( | |
| type='TextDetDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True, | |
| pad_size_divisor=32)) | |
| train_pipeline = [ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='TorchVisionWrapper', | |
| op='ColorJitter', | |
| brightness=0.12549019607843137, | |
| saturation=0.5), | |
| dict( | |
| type='ImgAugWrapper', | |
| args=[['Fliplr', 0.5], { | |
| 'cls': 'Affine', | |
| 'rotate': [-10, 10] | |
| }, ['Resize', [0.5, 3.0]]]), | |
| dict(type='RandomCrop', min_side_ratio=0.1), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict(type='Pad', size=(640, 640)), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape')) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1333, 736), keep_ratio=True), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
| ] | |
| icdar2015_textdet_data_root = 'data/det/textdet-thvote' | |
| icdar2015_textdet_train = dict( | |
| type='OCRDataset', | |
| data_root='data/det/textdet-thvote', | |
| ann_file='textdet_train.json', | |
| data_prefix=dict(img_path='imgs/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='TorchVisionWrapper', | |
| op='ColorJitter', | |
| brightness=0.12549019607843137, | |
| saturation=0.5), | |
| dict( | |
| type='ImgAugWrapper', | |
| args=[['Fliplr', 0.5], { | |
| 'cls': 'Affine', | |
| 'rotate': [-10, 10] | |
| }, ['Resize', [0.5, 3.0]]]), | |
| dict(type='RandomCrop', min_side_ratio=0.1), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict(type='Pad', size=(640, 640)), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape')) | |
| ]) | |
| icdar2015_textdet_test = dict( | |
| type='OCRDataset', | |
| data_root='data/det/textdet-thvote', | |
| ann_file='textdet_test.json', | |
| data_prefix=dict(img_path='imgs/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1333, 736), keep_ratio=True), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) | |
| ]) | |
| default_scope = 'mmocr' | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| randomness = dict(seed=None) | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=5), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=20), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| sync_buffer=dict(type='SyncBuffersHook'), | |
| visualization=dict( | |
| type='VisualizationHook', | |
| interval=1, | |
| enable=False, | |
| show=False, | |
| draw_gt=False, | |
| draw_pred=False)) | |
| log_level = 'INFO' | |
| log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True) | |
| load_from = None | |
| resume = False | |
| val_evaluator = dict(type='HmeanIOUMetric') | |
| test_evaluator = dict(type='HmeanIOUMetric') | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='TextDetLocalVisualizer', | |
| name='visualizer', | |
| vis_backends=[dict(type='LocalVisBackend')]) | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)) | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=20) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [dict(type='PolyLR', power=0.9, eta_min=1e-07, end=1200)] | |
| train_dataloader = dict( | |
| batch_size=16, | |
| num_workers=8, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='OCRDataset', | |
| data_root='data/det/textdet-thvote', | |
| ann_file='textdet_train.json', | |
| data_prefix=dict(img_path='imgs/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='TorchVisionWrapper', | |
| op='ColorJitter', | |
| brightness=0.12549019607843137, | |
| saturation=0.5), | |
| dict( | |
| type='ImgAugWrapper', | |
| args=[['Fliplr', 0.5], { | |
| 'cls': 'Affine', | |
| 'rotate': [-10, 10] | |
| }, ['Resize', [0.5, 3.0]]]), | |
| dict(type='RandomCrop', min_side_ratio=0.1), | |
| dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
| dict(type='Pad', size=(640, 640)), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape')) | |
| ])) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='OCRDataset', | |
| data_root='data/det/textdet-thvote', | |
| ann_file='textdet_test.json', | |
| data_prefix=dict(img_path='imgs/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1333, 736), keep_ratio=True), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ])) | |
| test_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='OCRDataset', | |
| data_root='data/det/textdet-thvote', | |
| ann_file='textdet_test.json', | |
| data_prefix=dict(img_path='imgs/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict( | |
| type='LoadImageFromFile', | |
| file_client_args=dict(backend='disk'), | |
| color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1333, 736), keep_ratio=True), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True), | |
| dict( | |
| type='PackTextDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ])) | |
| auto_scale_lr = dict(base_batch_size=16) | |
| launcher = 'none' | |
| work_dir = './work_dirs/dbnet_resnet18_fpnc_1200e_icdar2015' | |