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| _base_ = ['mmdet::_base_/models/mask-rcnn_r50_fpn.py'] | |
| mask_rcnn = _base_.pop('model') | |
| # Adapt Mask R-CNN model to OCR task | |
| mask_rcnn.update( | |
| dict( | |
| data_preprocessor=dict(pad_mask=False), | |
| rpn_head=dict( | |
| anchor_generator=dict( | |
| scales=[4], ratios=[0.17, 0.44, 1.13, 2.90, 7.46])), | |
| roi_head=dict( | |
| bbox_head=dict(num_classes=1), | |
| mask_head=dict(num_classes=1), | |
| ))) | |
| model = dict(type='MMDetWrapper', text_repr_type='poly', cfg=mask_rcnn) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
| dict( | |
| type='LoadOCRAnnotations', | |
| with_polygon=True, | |
| with_bbox=True, | |
| with_label=True, | |
| ), | |
| dict( | |
| type='TorchVisionWrapper', | |
| op='ColorJitter', | |
| brightness=32.0 / 255, | |
| saturation=0.5, | |
| contrast=0.5), | |
| dict( | |
| type='RandomResize', | |
| scale=(640, 640), | |
| ratio_range=(1.0, 4.125), | |
| keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='TextDetRandomCrop', target_size=(640, 640)), | |
| dict(type='MMOCR2MMDet', poly2mask=True), | |
| dict( | |
| type='mmdet.PackDetInputs', | |
| meta_keys=('img_path', 'ori_shape', 'img_shape', 'flip', | |
| 'scale_factor', 'flip_direction')) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), | |
| dict(type='Resize', scale=(1920, 1920), 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')) | |
| ] | |