_base_ = './cityscapes.py' crop_size = (769, 769) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', scale=(2049, 1025), 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=(2049, 1025), 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(dataset=dict(pipeline=train_pipeline)) val_dataloader = dict(dataset=dict(pipeline=test_pipeline)) test_dataloader = val_dataloader val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) test_evaluator = val_evaluator