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Commit e8298d24 authored by zhangwenwei's avatar zhangwenwei
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Merge branch 'getting_started' into 'master' 

Getting started

See merge request open-mmlab/mmdet.3d!108
parents dd2f285b fb8f331b
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configs/votenet/README.md
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......@@ -16,9 +16,9 @@ We implement VoteNet and provide the result and checkpoints on ScanNet and SUNRG
### ScanNet
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x |4.1||62.90|39.91|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238-4483c0c0.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238.log.json)|
| [PointNet++](./votenet_8x8_scannet-3d-18class.py) | 3x |4.1||62.90|39.91|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238.log.json)|
### SUNRGBD
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [PointNet++](./votenet_16x8_sunrgbd-3d-10class.py) | 3x |8.1||59.07|35.77|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_8x8_scannet-3d-18class/votenet_8x8_scannet-3d-18class_20200620_230238.log.json)|
| [PointNet++](./votenet_16x8_sunrgbd-3d-10class.py) | 3x |8.1||59.07|35.77|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238-4483c0c0.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20200620_230238.log.json)|
docs/getting_started.md
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......@@ -117,65 +117,58 @@ Examples:
Assume that you have already downloaded the checkpoints to the directory `checkpoints/`.
1. Test Faster R-CNN and visualize the results. Press any key for the next image.
1. Test votenet on ScanNet and save the points and prediction visualization results.
```shell
python tools/test.py configs/faster_rcnn_r50_fpn_1x_coco.py \
checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth \
--show
python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
--show --show-dir ./data/scannet/show_results
```
2. Test Faster R-CNN and save the painted images for latter visualization.
2. Test votenet on ScanNet, save the points, prediction, groundtruth visualization results, and evaluate the mAP.
```shell
python tools/test.py configs/faster_rcnn_r50_fpn_1x.py \
checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth \
--show-dir faster_rcnn_r50_fpn_1x_results
```
3. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP.
```shell
python tools/test.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \
checkpoints/SOME_CHECKPOINT.pth \
python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
--eval mAP
--options 'show=True' 'out_dir=./data/scannet/show_results'
```
4. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP.
3. Test votenet on ScanNet (without saving the test results) and evaluate the mAP.
```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x_coco.py \
checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
8 --out results.pkl --eval bbox segm
python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
--eval mAP
```
5. Test Mask R-CNN with 8 GPUs, and evaluate the **classwise** bbox and mask AP.
4. Test SECOND with 8 GPUs, and evaluate the mAP.
```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x_coco.py \
checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
8 --out results.pkl --eval bbox segm --options "classwise=True"
./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth \
--out results.pkl --eval mAP
```
6. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate the json file to be submit to the official evaluation server.
5. Test PointPillars on nuscenes with 8 GPUs, and generate the json file to be submit to the official evaluation server.
```shell
./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x_coco.py \
checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \
8 --format-only --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py \
checkpoints/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth \
--format-only --options 'jsonfile_prefix=./pointpillars_nuscenes_results'
```
You will get two json files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`.
The generated results be under `./pointpillars_nuscenes_results` directory.
7. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate the txt and png files to be submit to the official evaluation server.
6. Test SECOND on KITTI with 8 GPUs, and generate the pkl files and submission datas to be submit to the official evaluation server.
```shell
./tools/dist_test.sh configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \
checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \
8 --format-only --options "txtfile_prefix=./mask_rcnn_cityscapes_test_results"
./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth \
--format-only --options 'pklfile_prefix=./second_kitti_results' 'submission_prefix=./second_kitti_results'
```
The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory.
The generated results be under `./second_kitti_results` directory.
### Visualization
......@@ -189,7 +182,7 @@ Aftering running this command, plotted results ***_points.obj and ***_pred.ply f
To see the points, detection results and ground truth of SUNRGBD, ScanNet or KITTI during evaluation time, you can run the following command
```bash
python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --options "show=True" "out_dir=${SHOW_DIR}"
python tools/test.py ${CONFIG_FILE} ${CKPT_PATH} --eval 'mAP' --options 'show=True' 'out_dir=${SHOW_DIR}'
```
After running this command, you will obtain ***_points.ob, ***_pred.ply files and ***_gt.ply in `${SHOW_DIR}`.
......
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