mirror of
https://github.com/open-mmlab/mmocr.git
synced 2025-06-03 21:54:47 +08:00
4f7270e574
* update coco ref * init_cfg for dbnet * initcfg for mask_rcnn * textsnake init_cfg * fix dbnet * panet initcfg * psenet initcfg * fcenet initcfg * drrg initcfg * add init_cfg to detectors * update maskrcnn config file to support mmdet * fix init_cfg of fce_head * crnn initcfg * init_weights in training * nrtr initcfg * robust_scanner initcfg * sar init_cfg * seg init_cfg * tps_crnn init_cfg * sdmgr initcfg * ner init_cfg * fix textsnake * sdmgr initcfg * move "pretrained" to "init_cfg" for config files * Moduleslist update * fix seg * ner init_cfg * fix base * fix encode decode recognizer * revert dbnet config * fix crnn * fix base.py * fix robust_scanner * fix panet * fix test * remove redundant init_weights() in fcehead * clean up * relex mmdet version in workflow * Add dependency version check * Update mmocr/models/textdet/dense_heads/pse_head.py Co-authored-by: Hongbin Sun <hongbin306@gmail.com> Co-authored-by: Hongbin Sun <hongbin306@gmail.com>
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
_base_ = [
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'../../_base_/schedules/schedule_sgd_600e.py',
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'../../_base_/runtime_10e.py'
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]
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model = dict(
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type='PSENet',
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backbone=dict(
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type='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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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
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norm_eval=True,
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style='caffe'),
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neck=dict(
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type='FPNF',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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fusion_type='concat'),
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bbox_head=dict(
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type='PSEHead',
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text_repr_type='quad',
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in_channels=[256],
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out_channels=7,
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loss=dict(type='PSELoss')),
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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/icdar2017/'
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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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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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dict(
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type='ScaleAspectJitter',
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img_scale=[(3000, 736)],
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ratio_range=(0.5, 3),
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aspect_ratio_range=(1, 1),
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multiscale_mode='value',
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long_size_bound=1280,
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short_size_bound=640,
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resize_type='long_short_bound',
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keep_ratio=False),
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dict(type='PSENetTargets'),
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dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
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dict(type='RandomRotateTextDet'),
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dict(
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type='RandomCropInstances',
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target_size=(640, 640),
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instance_key='gt_kernels'),
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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_kernels', 'gt_mask'],
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visualize=dict(flag=False, boundary_key='gt_kernels')),
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dict(type='Collect', keys=['img', 'gt_kernels', 'gt_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=(2240, 2200),
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flip=False,
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transforms=[
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dict(type='Resize', img_scale=(2240, 2200), 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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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_val.json',
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img_prefix=data_root + '/imgs',
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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_val.json',
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img_prefix=data_root + '/imgs',
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pipeline=test_pipeline))
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evaluation = dict(interval=10, metric='hmean-iou')
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