Skip to content

GitLab

Unverified Commit c9ff1832 authored by ChaimZhu's avatar ChaimZhu Committed by GitHub
Browse files

[Refactor] refactor demo doc (#1762) 

* refactor_part_of_doc

* rename demo -> inference
parent f0e485bd
Show whitespace changes
Inline Side-by-side
Showing with 10 additions and 10 deletions
docs/en/user_guides/demo.md → docs/en/user_guides/inference.md
View file @ c9ff1832
# Demo # Inference
## Introduction ## Introduction
...@@ -21,13 +21,13 @@ The visualization results including a point cloud and predicted 3D bounding boxe ...@@ -21,13 +21,13 @@ The visualization results including a point cloud and predicted 3D bounding boxe
Example on KITTI data using [SECOND](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/second) model: Example on KITTI data using [SECOND](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/second) model:
```shell ```shell
python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth python demo/pcd_demo.py demo/data/kitti/000008.bin configs/second/second_hv-secfpn_8xb6-80e_kitti-3d-car.py checkpoints/second_hv-secfpn_8xb6-80e_kitti-3d-car_20200620_230238-393f000c.pth
``` ```
Example on SUN RGB-D data using [VoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet) model: Example on SUN RGB-D data using [VoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet) model:
```shell ```shell
python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_16x8_sunrgbd-3d-10class.py checkpoints/votenet_16x8_sunrgbd-3d-10class_20200620_230238-4483c0c0.pth python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_8xb16_sunrgbd-3d.py checkpoints/votenet_8xb16_sunrgbd-3d_20200620_230238-4483c0c0.pth
``` ```
Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/README.md/) for detailed instructions on how to convert the checkpoint. Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/README.md/) for detailed instructions on how to convert the checkpoint.
...@@ -45,13 +45,13 @@ where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix. The v ...@@ -45,13 +45,13 @@ where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix. The v
Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/mvxnet) model: Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/mvxnet) model:
```shell ```shell
python demo/multi_modality_demo.py demo/data/kitti/kitti_000008.bin demo/data/kitti/kitti_000008.png demo/data/kitti/kitti_000008_infos.pkl configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py checkpoints/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20200621_003904-10140f2d.pth python demo/multi_modality_demo.py demo/data/kitti/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/mvxnet/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class.py checkpoints/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class_20200621_003904-10140f2d.pth
``` ```
Example on SUN RGB-D data using [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/imvotenet) model: Example on SUN RGB-D data using [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/imvotenet) model:
```shell ```shell
python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/data/sunrgbd/sunrgbd_000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class.py checkpoints/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210323_184021-d44dcb66.pth python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/data/sunrgbd/sunrgbd_000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd.py checkpoints/imvotenet_stage2_8xb16_sunrgbd_20210323_184021-d44dcb66.pth
``` ```
### Monocular 3D Detection ### Monocular 3D Detection
...@@ -59,15 +59,15 @@ python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/dat ...@@ -59,15 +59,15 @@ python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/dat
To test a monocular 3D detector on image data, simply run: To test a monocular 3D detector on image data, simply run:
```shell ```shell
python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show] python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--cam-type ${CAM_TYPE}] [--score-thr ${SCORE-THR}] [--out-dir ${OUT_DIR}] [--show]
``` ```
where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix (camera intrinsic matrix). The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in `${OUT_DIR}/PCD_NAME`. where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix (camera intrinsic matrix), and `CAM_TYPE` should be specified according to dataset. For example, if you want to inference on the front camera image, the `CAM_TYPE` should be set as `CAM_2` for KITTI, and `CAM_FRONT` for nuScenes. By specifying `CAM_TYPE`, you can even infer on any camera images for datasets with multi-view cameras, such as nuScenes and Waymo. `SCORE-THR` is the 3D bbox threshold while visualization. The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in `${OUT_DIR}/IMG_NAME`.
Example on nuScenes data using [FCOS3D](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fcos3d) model: Example on nuScenes data using [FCOS3D](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fcos3d) model:
```shell ```shell
python demo/mono_det_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.jpg demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525_mono3d.coco.json configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py checkpoints/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth python demo/mono_det_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.jpg demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.pkl configs/fcos3d/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune.py checkpoints/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth
``` ```
Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. See more details and examples in PR [#744](https://github.com/open-mmlab/mmdetection3d/pull/744). Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. See more details and examples in PR [#744](https://github.com/open-mmlab/mmdetection3d/pull/744).
...@@ -77,7 +77,7 @@ Note that when visualizing results of monocular 3D detection for flipped images, ...@@ -77,7 +77,7 @@ Note that when visualizing results of monocular 3D detection for flipped images,
To test a 3D segmentor on point cloud data, simply run: To test a 3D segmentor on point cloud data, simply run:
```shell ```shell
python demo/pc_seg_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show] python demo/pcd_seg_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show]
``` ```
The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in `${OUT_DIR}/PCD_NAME`. The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in `${OUT_DIR}/PCD_NAME`.
...@@ -85,5 +85,5 @@ The visualization results including a point cloud and its predicted 3D segmentat ...@@ -85,5 +85,5 @@ The visualization results including a point cloud and its predicted 3D segmentat
Example on ScanNet data using [PointNet++ (SSG)](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointnet2) model: Example on ScanNet data using [PointNet++ (SSG)](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointnet2) model:
```shell ```shell
python demo/pc_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py checkpoints/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth python demo/pc_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py checkpoints/pointnet2_ssg_2xb16-cosine-200e_scannet-seg_20210514_143644-ee73704a.pth
``` ```
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment