# Test Results Submission

## Panoptic segmentation test results submission

The following sections introduce how to produce the prediction results of panoptic segmentation models on the COCO test-dev set and submit the predictions to [COCO evaluation server](https://competitions.codalab.org/competitions/19507).

### Prerequisites

- Download [COCO test dataset images](http://images.cocodataset.org/zips/test2017.zip), [testing image info](http://images.cocodataset.org/annotations/image_info_test2017.zip), and [panoptic train/val annotations](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip), then unzip them, put 'test2017' to `data/coco/`, put json files and annotation files to `data/coco/annotations/`.

```shell
# suppose data/coco/ does not exist
mkdir -pv data/coco/

# download test2017
wget -P data/coco/ http://images.cocodataset.org/zips/test2017.zip
wget -P data/coco/ http://images.cocodataset.org/annotations/image_info_test2017.zip
wget -P data/coco/ http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip

# unzip them
unzip data/coco/test2017.zip -d data/coco/
unzip data/coco/image_info_test2017.zip -d data/coco/
unzip data/coco/panoptic_annotations_trainval2017.zip -d data/coco/

# remove zip files (optional)
rm -rf data/coco/test2017.zip data/coco/image_info_test2017.zip data/coco/panoptic_annotations_trainval2017.zip
```

- Run the following code to update category information in testing image info. Since the attribute `isthing` is missing in category information of 'image_info_test-dev2017.json', we need to update it with the category information in 'panoptic_val2017.json'.

```shell
python tools/misc/gen_coco_panoptic_test_info.py data/coco/annotations
```

After completing the above preparations, your directory structure of `data` should be like this:

```text
data
`-- coco
    |-- annotations
    |   |-- image_info_test-dev2017.json
    |   |-- image_info_test2017.json
    |   |-- panoptic_image_info_test-dev2017.json
    |   |-- panoptic_train2017.json
    |   |-- panoptic_train2017.zip
    |   |-- panoptic_val2017.json
    |   `-- panoptic_val2017.zip
    `-- test2017
```

### Inference on coco test-dev

To do inference on coco test-dev, we should update the setting of `test_dataloder` and `test_evaluator` first. There two ways to do this: 1. update them in config file; 2. update them in command line.

#### Update them in config file

The relevant settings are provided at the end of `configs/_base_/datasets/coco_panoptic.py`, as below.

```python
test_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotations/panoptic_image_info_test-dev2017.json',
        data_prefix=dict(img='test2017/'),
        test_mode=True,
        pipeline=test_pipeline))
test_evaluator = dict(
    type='CocoPanopticMetric',
    format_only=True,
    ann_file=data_root + 'annotations/panoptic_image_info_test-dev2017.json',
    outfile_prefix='./work_dirs/coco_panoptic/test')
```

Any of the following way can be used to update the setting for inference on coco test-dev set.

Case 1: Directly uncomment the setting in `configs/_base_/datasets/coco_panoptic.py`.

Case 2: Copy the following setting to the config file you used now.

```python
test_dataloader = dict(
    dataset=dict(
        ann_file='annotations/panoptic_image_info_test-dev2017.json',
        data_prefix=dict(img='test2017/', _delete_=True)))
test_evaluator = dict(
    format_only=True,
    ann_file=data_root + 'annotations/panoptic_image_info_test-dev2017.json',
    outfile_prefix='./work_dirs/coco_panoptic/test')
```

Then infer on coco test-dev et by the following command.

```shell
python tools/test.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE}
```

#### Update them in command line

The command for update of the related settings and inference on coco test-dev are as below.

```shell
# test with single gpu
CUDA_VISIBLE_DEVICES=0 python tools/test.py \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    --cfg-options \
    test_dataloader.dataset.ann_file=annotations/panoptic_image_info_test-dev2017.json \
    test_dataloader.dataset.data_prefix.img=test2017 \
    test_dataloader.dataset.data_prefix._delete_=True \
    test_evaluator.format_only=True \
    test_evaluator.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json \
    test_evaluator.outfile_prefix=${WORK_DIR}/results

# test with four gpus
CUDA_VISIBLE_DEVICES=0,1,3,4 bash tools/dist_test.sh \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    8 \  # eights gpus
    --cfg-options \
    test_dataloader.dataset.ann_file=annotations/panoptic_image_info_test-dev2017.json \
    test_dataloader.dataset.data_prefix.img=test2017 \
    test_dataloader.dataset.data_prefix._delete_=True \
    test_evaluator.format_only=True \
    test_evaluator.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json \
    test_evaluator.outfile_prefix=${WORK_DIR}/results

# test with slurm
GPUS=8 tools/slurm_test.sh \
    ${Partition} \
    ${JOB_NAME} \
    ${CONFIG_FILE} \
    ${CHECKPOINT_FILE} \
    --cfg-options \
    test_dataloader.dataset.ann_file=annotations/panoptic_image_info_test-dev2017.json \
    test_dataloader.dataset.data_prefix.img=test2017 \
    test_dataloader.dataset.data_prefix._delete_=True \
    test_evaluator.format_only=True \
    test_evaluator.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json \
    test_evaluator.outfile_prefix=${WORK_DIR}/results
```

Example

Suppose we perform inference on `test2017` using pretrained MaskFormer with ResNet-50 backbone.

```shell
# test with single gpu
CUDA_VISIBLE_DEVICES=0 python tools/test.py \
    configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py \
    checkpoints/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth \
    --cfg-options \
    test_dataloader.dataset.ann_file=annotations/panoptic_image_info_test-dev2017.json \
    test_dataloader.dataset.data_prefix.img=test2017 \
    test_dataloader.dataset.data_prefix._delete_=True \
    test_evaluator.format_only=True \
    test_evaluator.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json \
    test_evaluator.outfile_prefix=work_dirs/maskformer/results
```

### Rename files and zip results

After inference, the panoptic segmentation results (a json file and a directory where the masks are stored) will be in `WORK_DIR`. We should rename them according to the naming convention described on [COCO's Website](https://cocodataset.org/#upload). Finally, we need to compress the json and the directory where the masks are stored into a zip file, and rename the zip file according to the naming convention. Note that the zip file should **directly** contains the above two files.

The commands to rename files and zip results:

```shell
# In WORK_DIR, we have panoptic segmentation results: 'panoptic' and 'results.panoptic.json'.
cd ${WORK_DIR}

# replace '[algorithm_name]' with the name of algorithm you used.
mv ./panoptic ./panoptic_test-dev2017_[algorithm_name]_results
mv ./results.panoptic.json ./panoptic_test-dev2017_[algorithm_name]_results.json
zip panoptic_test-dev2017_[algorithm_name]_results.zip -ur panoptic_test-dev2017_[algorithm_name]_results panoptic_test-dev2017_[algorithm_name]_results.json
```