Thanks for your contribution and we appreciate it a lot. The followinginstructions would make your pull request more healthy and more easilyget feedback. If you do not understand some items, don't worry, justmake the pull request and seek help from maintainers.Please describe the motivation of this PR and the goal you want toachieve through this PR.Support metrics for the depth estimation task, including RMSE, ABSRel,and etc.Please briefly describe what modification is made in this PR.Does the modification introduce changes that break thebackward-compatibility of the downstream repos?If so, please describe how it breaks the compatibility and how thedownstream projects should modify their code to keep compatibility withthis PR.Using the following configuration to compute depth metrics on NYU```pythondataset_type = 'NYUDataset'data_root = 'data/nyu'test_pipeline = [    dict(type='LoadImageFromFile'),    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'))]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_dataloaderval_evaluator = dict(type='DepthMetric', max_depth_eval=10.0, crop_type='nyu')test_evaluator = val_evaluator```Example log:![image](https://github.com/open-mmlab/mmsegmentation/assets/26127467/8101d65c-dee6-48de-916c-818659947b59)1. Pre-commit or other linting tools are used to fix the potential lintissues.2. The modification is covered by complete unit tests. If not, pleaseadd more unit test to ensure the correctness.3. If the modification has potential influence on downstream projects,this PR should be tested with downstream projects, like MMDet orMMDet3D.4. The documentation has been modified accordingly, like docstring orexample tutorials.

Thanks for your contribution and we appreciate it a lot. The followinginstructions would make your pull request more healthy and more easilyget feedback. If you do not understand some items, don't worry, justmake the pull request and seek help from maintainers.## MotivationPlease describe the motivation of this PR and the goal you want toachieve through this PR.Support metrics for the depth estimation task, including RMSE, ABSRel,and etc.## ModificationPlease briefly describe what modification is made in this PR.## BC-breaking (Optional)Does the modification introduce changes that break thebackward-compatibility of the downstream repos?If so, please describe how it breaks the compatibility and how thedownstream projects should modify their code to keep compatibility withthis PR.## Use cases (Optional)Using the following configuration to compute depth metrics on NYU```pythondataset_type = 'NYUDataset'data_root = 'data/nyu'test_pipeline = [    dict(type='LoadImageFromFile'),    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'))]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_dataloaderval_evaluator = dict(type='DepthMetric', max_depth_eval=10.0, crop_type='nyu')test_evaluator = val_evaluator```Example log:![image](https://github.com/open-mmlab/mmsegmentation/assets/26127467/8101d65c-dee6-48de-916c-818659947b59)## Checklist1. Pre-commit or other linting tools are used to fix the potential lintissues.2. The modification is covered by complete unit tests. If not, pleaseadd more unit test to ensure the correctness.3. If the modification has potential influence on downstream projects,this PR should be tested with downstream projects, like MMDet orMMDet3D.4. The documentation has been modified accordingly, like docstring orexample tutorials.