[Feature] Support Separated and Occluded COCO metric.

[RangiLyu]

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Motivation

Support Separated and Occluded COCO metric in paper A Tri-Layer Plugin to Improve Occluded Detection.

Modification

Add OccludedSeparatedCocoDataset

Usage

Detecting occluded objects still remains a challenge for state-of-the-art object detectors.
We implemented the metric presented in paper A Tri-Layer Plugin to Improve Occluded Detection to calculate the recall of separated and occluded masks.

There are two ways to use this metric:

Offline evaluation

We provide a script to calculate the metric with a dumped prediction file.

First, use the tools/test.py script to dump the detection results:

python tools/test.py ${CONFIG} ${MODEL_PATH} --out results.pkl

Then, run the tools/analysis_tools/coco_occluded_separated_recall.py script to get the recall of separated and occluded masks:

python tools/analysis_tools/coco_occluded_separated_recall.py results.pkl --out occluded_separated_recall.json

The output should be like this:

loading annotations into memory...
Done (t=0.51s)
creating index...
index created!
processing detection results...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 109.3 task/s, elapsed: 46s, ETA:     0s
computing occluded mask recall...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5550/5550, 780.5 task/s, elapsed: 7s, ETA:     0s
COCO occluded mask recall: 58.79%
COCO occluded mask success num: 3263
computing separated mask recall...
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3522/3522, 778.3 task/s, elapsed: 5s, ETA:     0s
COCO separated mask recall: 31.94%
COCO separated mask success num: 1125
+-----------+--------+-------------+
| mask type | recall | num correct |
+-----------+--------+-------------+
| occluded  | 58.79% | 3263        |
| separated | 31.94% | 1125        |
+-----------+--------+-------------+
Evaluation results have been saved to occluded_separated_recall.json.

Online evaluation

We implemented OccludedSeparatedCocoDataset which inherited from the CocoDataset.
To evaluate the recall of separated and occluded masks during training, just replace the validation dataset type with 'OccludedSeparatedCocoDataset' in your config:

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=train_pipeline),
    val=dict(
        type='OccludedSeparatedCocoDataset',  # modify this
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline),
    test=dict(
        type='OccludedSeparatedCocoDataset',  # modify this
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=test_pipeline))

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)

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.
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 MMCls.
4. The documentation has been modified accordingly, like docstring or example tutorials.