Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Feature] Support depth metrics #3297

Merged
merged 4 commits into from
Aug 31, 2023

Conversation

Ben-Louis
Copy link
Contributor

@Ben-Louis Ben-Louis commented Aug 30, 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.

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

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

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.

Comment on lines 30 to 31
crop_type (str, optional): Type of cropping to be used during
evaluation.
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Might list optional values

Comment on lines 126 to 128
if self.crop_type == 'nyu_crop':
crop_mask = torch.zeros_like(valid_mask)
crop_mask[45:471, 41:601] = 1
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Might add a ref link.

@xiexinch xiexinch merged commit 35ff78a into open-mmlab:dev-1.x Aug 31, 2023
1 of 2 checks passed
emily-lin pushed a commit to emily-lin/mmsegmentation that referenced this pull request 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.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants