mmsegmentation/mmseg/evaluation/__init__.py

# Copyright (c) OpenMMLab. All rights reserved.
from .metrics import CityscapesMetric, DepthMetric, IoUMetric

__all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric']
[Refactor] Refactor IoU metrics 2022-06-02 14:15:28 +00:00			`# Copyright (c) OpenMMLab. All rights reserved.`
[Feature] Support depth metrics (#3297) Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Please describe the motivation of this PR and the goal you want to achieve through this PR. Support metrics for the depth estimation task, including RMSE, ABSRel, and etc. ## Modification Please briefly describe what modification is made in this PR. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) Using the following configuration to compute depth metrics on NYU ```python dataset_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_dataloader val_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) ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add 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 or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. 2023-08-31 12:02:19 +08:00			`from .metrics import CityscapesMetric, DepthMetric, IoUMetric`
[Refactor] Refactor IoU metrics 2022-06-02 14:15:28 +00:00
[Feature] Support depth metrics (#3297) Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Please describe the motivation of this PR and the goal you want to achieve through this PR. Support metrics for the depth estimation task, including RMSE, ABSRel, and etc. ## Modification Please briefly describe what modification is made in this PR. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) Using the following configuration to compute depth metrics on NYU ```python dataset_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_dataloader val_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) ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add 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 or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. 2023-08-31 12:02:19 +08:00			`__all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric']`