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| # dataset settings | |
| dataset_type = 'iSAIDDataset' | |
| data_root = 'data/iSAID' | |
| """ | |
| This crop_size setting is followed by the implementation of | |
| `PointFlow: Flowing Semantics Through Points for Aerial Image | |
| Segmentation <https://arxiv.org/pdf/2103.06564.pdf>`_. | |
| """ | |
| crop_size = (896, 896) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| type='RandomResize', | |
| scale=(896, 896), | |
| ratio_range=(0.5, 2.0), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=(896, 896), keep_ratio=True), | |
| # add loading annotation after ``Resize`` because ground truth | |
| # does not need to do resize data transform | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs') | |
| ] | |
| img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] | |
| tta_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict( | |
| type='TestTimeAug', | |
| transforms=[ | |
| [ | |
| dict(type='Resize', scale_factor=r, keep_ratio=True) | |
| for r in img_ratios | |
| ], | |
| [ | |
| dict(type='RandomFlip', prob=0., direction='horizontal'), | |
| dict(type='RandomFlip', prob=1., direction='horizontal') | |
| ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] | |
| ]) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=4, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='InfiniteSampler', shuffle=True), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict( | |
| img_path='img_dir/train', seg_map_path='ann_dir/train'), | |
| pipeline=train_pipeline)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'), | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) | |
| test_evaluator = val_evaluator | |