# dataset settings dataset_type = 'NYUDataset' data_root = 'data/nyu' train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3), dict(type='RandomDepthMix', prob=0.25), dict(type='RandomFlip', prob=0.5), dict(type='RandomCrop', crop_size=(480, 480)), dict( type='Albu', transforms=[ dict(type='RandomBrightnessContrast'), dict(type='RandomGamma'), dict(type='HueSaturationValue'), ]), dict( type='PackSegInputs', meta_keys=('img_path', 'depth_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'category_id')), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', scale=(2000, 480), keep_ratio=True), dict(dict(type='LoadDepthAnnotation', depth_rescale_factor=1e-3)), dict( type='PackSegInputs', meta_keys=('img_path', 'depth_map_path', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'category_id')) ] train_dataloader = dict( batch_size=8, num_workers=8, persistent_workers=True, sampler=dict(type='InfiniteSampler', shuffle=True), dataset=dict( type=dataset_type, data_root=data_root, data_prefix=dict( img_path='images/train', depth_map_path='annotations/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, test_mode=True, data_prefix=dict( img_path='images/test', depth_map_path='annotations/test'), pipeline=test_pipeline)) test_dataloader = val_dataloader val_evaluator = dict( type='DepthMetric', min_depth_eval=0.001, max_depth_eval=10.0, crop_type='nyu_crop') test_evaluator = val_evaluator