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| _base_ = [ | |
| '../../_base_/default_runtime.py', | |
| '../../_base_/schedules/schedule_sgd_100k_iters.py', | |
| '../../_base_/det_models/dbnetpp_r50dcnv2_fpnc.py', | |
| '../../_base_/det_datasets/synthtext.py', | |
| '../../_base_/det_pipelines/dbnet_pipeline.py' | |
| ] | |
| train_list = {{_base_.train_list}} | |
| test_list = {{_base_.test_list}} | |
| img_norm_cfg_r50dcnv2 = dict( | |
| mean=[122.67891434, 116.66876762, 104.00698793], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True) | |
| train_pipeline_r50dcnv2 = [ | |
| dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadTextAnnotations', | |
| with_bbox=True, | |
| with_mask=True, | |
| poly2mask=False), | |
| dict(type='ColorJitter', brightness=32.0 / 255, saturation=0.5), | |
| dict(type='Normalize', **img_norm_cfg_r50dcnv2), | |
| dict( | |
| type='ImgAug', | |
| args=[['Fliplr', 0.5], | |
| dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]], | |
| clip_invalid_ploys=False), | |
| dict(type='EastRandomCrop', target_size=(640, 640)), | |
| dict(type='DBNetTargets', shrink_ratio=0.4), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='CustomFormatBundle', | |
| keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'], | |
| visualize=dict(flag=False, boundary_key='gt_shrink')), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask']) | |
| ] | |
| test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}} | |
| data = dict( | |
| samples_per_gpu=16, | |
| workers_per_gpu=8, | |
| val_dataloader=dict(samples_per_gpu=1), | |
| test_dataloader=dict(samples_per_gpu=1), | |
| train=dict( | |
| type='UniformConcatDataset', | |
| datasets=train_list, | |
| pipeline=train_pipeline_r50dcnv2), | |
| val=dict( | |
| type='UniformConcatDataset', | |
| datasets=test_list, | |
| pipeline=test_pipeline_4068_1024), | |
| test=dict( | |
| type='UniformConcatDataset', | |
| datasets=test_list, | |
| pipeline=test_pipeline_4068_1024)) | |
| evaluation = dict(interval=200000, metric='hmean-iou') # do not evaluate | |