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| _base_ = [ | |
| '../_base_/models/deeplabv3_r50-d8.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_40k.py' | |
| ] | |
| crop_size = (512, 1024) | |
| data_preprocessor = dict(size=crop_size) | |
| model = dict(data_preprocessor=data_preprocessor) | |
| # dataset settings | |
| dataset_type = 'DSDLSegDataset' | |
| data_root = 'data/CityScapes' | |
| img_prefix = 'raw/CityScapes' | |
| train_ann = 'dsdl/dsdl_SemSeg_full/set-train/train.yaml' | |
| val_ann = 'dsdl/dsdl_SemSeg_full/set-val/val.yaml' | |
| used_labels = [ | |
| 'road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic_light', | |
| 'traffic_sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', | |
| 'truck', 'bus', 'train', 'motorcycle', 'bicycle' | |
| ] | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| type='RandomResize', | |
| scale=(2048, 1024), | |
| 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=(2048, 1024), 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') | |
| ] | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='InfiniteSampler', shuffle=True), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict(img_path=img_prefix, seg_map_path=img_prefix), | |
| ann_file=train_ann, | |
| used_labels=used_labels, | |
| 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_prefix, seg_map_path=img_prefix), | |
| ann_file=val_ann, | |
| used_labels=used_labels, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) | |
| test_evaluator = val_evaluator | |