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_base_ = [ |
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'../../_base_/default_runtime.py', |
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'../../_base_/schedules/schedule_sgd_100k_iters.py', |
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'../../_base_/det_models/dbnetpp_r50dcnv2_fpnc.py', |
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'../../_base_/det_datasets/synthtext.py', |
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'../../_base_/det_pipelines/dbnet_pipeline.py' |
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] |
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|
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train_list = {{_base_.train_list}} |
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test_list = {{_base_.test_list}} |
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|
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img_norm_cfg_r50dcnv2 = dict( |
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mean=[122.67891434, 116.66876762, 104.00698793], |
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std=[58.395, 57.12, 57.375], |
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to_rgb=True) |
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train_pipeline_r50dcnv2 = [ |
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), |
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dict( |
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type='LoadTextAnnotations', |
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with_bbox=True, |
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with_mask=True, |
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poly2mask=False), |
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dict(type='ColorJitter', brightness=32.0 / 255, saturation=0.5), |
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dict(type='Normalize', **img_norm_cfg_r50dcnv2), |
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dict( |
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type='ImgAug', |
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args=[['Fliplr', 0.5], |
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dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]], |
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clip_invalid_ploys=False), |
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dict(type='EastRandomCrop', target_size=(640, 640)), |
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dict(type='DBNetTargets', shrink_ratio=0.4), |
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dict(type='Pad', size_divisor=32), |
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dict( |
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type='CustomFormatBundle', |
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keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'], |
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visualize=dict(flag=False, boundary_key='gt_shrink')), |
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dict( |
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type='Collect', |
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keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask']) |
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] |
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|
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test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}} |
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|
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data = dict( |
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samples_per_gpu=16, |
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workers_per_gpu=8, |
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val_dataloader=dict(samples_per_gpu=1), |
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test_dataloader=dict(samples_per_gpu=1), |
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train=dict( |
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type='UniformConcatDataset', |
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datasets=train_list, |
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pipeline=train_pipeline_r50dcnv2), |
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val=dict( |
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type='UniformConcatDataset', |
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datasets=test_list, |
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pipeline=test_pipeline_4068_1024), |
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test=dict( |
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type='UniformConcatDataset', |
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datasets=test_list, |
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pipeline=test_pipeline_4068_1024)) |
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|
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evaluation = dict(interval=200000, metric='hmean-iou') |
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|
