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https://github.com/open-mmlab/mmocr.git
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7571763376
* [Refactor] Use MMOCR's registry 1. Define MMOCR's registries as a child of MMDet's 2. Register all models to MMOCR's own registries 3. Modify some model configs so that some models in MMDet can be correctly located 4. Remove some outdated demo scripts * add detectors
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
_base_ = [
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'../../_base_/schedules/schedule_1200e.py', '../../_base_/runtime_10e.py'
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]
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load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth'
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model = dict(
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type='DBNet',
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backbone=dict(
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type='mmdet.ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=-1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=False,
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style='caffe',
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dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
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stage_with_dcn=(False, True, True, True)),
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neck=dict(
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type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256),
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bbox_head=dict(
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type='DBHead',
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text_repr_type='quad',
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in_channels=256,
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loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
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train_cfg=None,
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test_cfg=None)
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dataset_type = 'IcdarDataset'
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data_root = 'data/icdar2015/'
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# img_norm_cfg = dict(
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# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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# from official dbnet code
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img_norm_cfg = dict(
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mean=[122.67891434, 116.66876762, 104.00698793],
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std=[255, 255, 255],
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to_rgb=False)
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# for visualizing img, pls uncomment it.
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# img_norm_cfg = dict(mean=[0, 0, 0], std=[1, 1, 1], to_rgb=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='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),
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# img aug
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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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# random crop
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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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# for visualizing img and gts, pls set visualize = True
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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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test_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(4068, 1024),
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flip=False,
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transforms=[
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dict(type='Resize', img_scale=(4068, 1024), keep_ratio=True),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=4,
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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=dataset_type,
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ann_file=data_root + '/instances_training.json',
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# for debugging top k imgs
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# select_first_k=200,
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img_prefix=data_root + '/imgs',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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ann_file=data_root + '/instances_test.json',
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img_prefix=data_root + '/imgs',
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# select_first_k=100,
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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ann_file=data_root + '/instances_test.json',
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img_prefix=data_root + '/imgs',
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# select_first_k=100,
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pipeline=test_pipeline))
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evaluation = dict(interval=100, metric='hmean-iou')
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