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:
## 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.
## Motivation
1. It is used to save the segmentation predictions as files and upload
these files to a test server
## Modification
1. Add output_file and format only in `IoUMetric`
## BC-breaking (Optional)
No
## Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases
here, and update the documentation.
## Checklist
1. Pre-commit or other linting tools are used to fix the potential lint
issues.
3. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
4. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
5. The documentation has been modified accordingly, like docstring or
example tutorials.
* [WIP] Refactor data flow
* model return
* [WIP] Refactor data flow
* support data_samples is optional
* fix benchmark
* fix base
* minors
* rebase
* fix api
* ut
* fix api inference
* comments
* docstring
* docstring
* docstring
* fix bug of slide inference
* add assert c > 1
Peng Lu
Miao Zheng
谢昕辰