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[Feature] Support depth metrics #3297
Merged
xiexinch
merged 4 commits into
open-mmlab:dev-1.x
from
Ben-Louis:lupeng/support-depth-metrics
Aug 31, 2023
Merged
[Feature] Support depth metrics #3297
xiexinch
merged 4 commits into
open-mmlab:dev-1.x
from
Ben-Louis:lupeng/support-depth-metrics
Aug 31, 2023
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xiexinch
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Aug 31, 2023
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crop_type (str, optional): Type of cropping to be used during | ||
evaluation. |
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Might list optional values
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if self.crop_type == 'nyu_crop': | ||
crop_mask = torch.zeros_like(valid_mask) | ||
crop_mask[45:471, 41:601] = 1 |
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Might add a ref link.
xiexinch
approved these changes
Aug 31, 2023
xiexinch
approved these changes
Aug 31, 2023
emily-lin
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Nov 18, 2023
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. 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. Please briefly describe what modification is made in this PR. 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. 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) 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.
nahidnazifi87
pushed a commit
to nahidnazifi87/mmsegmentation_playground
that referenced
this pull request
Apr 5, 2024
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.
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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
Example log:
Checklist