| |
| dataset_type = 'PotsdamDataset' |
| data_root = 'data/potsdam' |
| crop_size = (512, 512) |
| train_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict(type='LoadAnnotations', reduce_zero_label=True), |
| dict( |
| type='RandomResize', |
| scale=(512, 512), |
| 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=(512, 512), keep_ratio=True), |
| |
| |
| dict(type='LoadAnnotations', reduce_zero_label=True), |
| 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 |
|
|
