Update the link of pointpillars log on the Lyft dataset

README.md

@@ -159,7 +159,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
             <li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li>
             <li><a href="configs/ssn">SSN (ECCV'2020)</a></li>
             <li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li>
-            <li><a href="configs/ point_rcnn">PointRCNN</a></li>
+            <li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li>
             <li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li>
             <li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li>
         </ul>

README_zh-CN.md

@@ -159,7 +159,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
             <li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li>
             <li><a href="configs/ssn">SSN (ECCV'2020)</a></li>
             <li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li>
-            <li><a href="configs/ point_rcnn">PointRCNN</a></li>
+            <li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li>
             <li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li>
             <li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li>
         </ul>

configs/pointpillars/README.md

@@ -39,7 +39,7 @@ We implement PointPillars and provide the results and checkpoints on KITTI, nuSc
 |  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
 |[SECFPN](./hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py)|2x|12.2||13.8|14.1|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210829_100455-82b81c39.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d_20210829_100455.log.json)|
-|[FPN](./hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d.py)|2x|9.2||14.8|15.0|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429-0b3d6196.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822.log.json)|
+|[FPN](./hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d.py)|2x|9.2||14.8|15.0|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429-0b3d6196.pth) | [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d/hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d_20210822_095429.log.json)|
 
 ### Waymo
 

docs/en/1_exist_data_model.md

@@ -201,10 +201,23 @@ GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_16
 
 You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
 
-If you have just multiple machines connected with ethernet, you can refer to
-PyTorch [launch utility](https://pytorch.org/docs/stable/distributed.html).
+If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
+
+On the first machine:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
+```
+
+On the second machine:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
+```
+
 Usually it is slow if you do not have high speed networking like InfiniBand.
 
+
 ### Launch multiple jobs on a single machine
 
 If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,

docs/zh_cn/1_exist_data_model.md

@@ -199,7 +199,21 @@ GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_16
 
 你可以查看 [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) 来获取所有的参数和环境变量。
 
-如果你有多个机器连接到以太网，可以参考 PyTorch 的 [launch utility](https://pytorch.org/docs/stable/distributed.html)，如果你没有像 InfiniBand 一样的高速率网络，通常会很慢。
+如果您想使用由 ethernet 连接起来的多台机器， 您可以使用以下命令:
+
+在第一台机器上:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
+```
+
+在第二台机器上:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
+```
+
+但是，如果您不使用高速网路连接这几台机器的话，训练将会非常慢。
 
 ### 在单个机器上启动多个任务
 

docs/zh_cn/getting_started.md

@@ -148,6 +148,15 @@ git checkout v0.20.0  # switch to v0.20.0 branch
 pip install -e .  # or "python setup.py develop"
 ```
 
+**f. 克隆 MMDetection3D 代码仓库**
+
+```shell
+git clone https://github.com/open-mmlab/mmdetection3d.git
+cd mmdetection3d
+```
+
+
+
 **g. 安装依赖包和 MMDetection3D.**
 
 ```shell

mmdet3d/apis/inference.py

@@ -16,6 +16,7 @@
 from mmdet3d.core.bbox import get_box_type
 from mmdet3d.datasets.pipelines import Compose
 from mmdet3d.models import build_model
+from mmdet3d.utils import get_root_logger
 
 
 def convert_SyncBN(config):
@@ -66,7 +67,12 @@ def init_model(config, checkpoint=None, device='cuda:0'):
         if 'PALETTE' in checkpoint['meta']:  # 3D Segmentor
             model.PALETTE = checkpoint['meta']['PALETTE']
     model.cfg = config  # save the config in the model for convenience
-    torch.cuda.set_device(device)
+    if device != 'cpu':
+        torch.cuda.set_device(device)
+    else:
+        logger = get_root_logger()
+        logger.warning('Don\'t suggest using CPU device. '
+                       'Some functions are not supported for now.')
     model.to(device)
     model.eval()
     return model

tools/dist_test.sh

@@ -3,8 +3,20 @@
 CONFIG=$1
 CHECKPOINT=$2
 GPUS=$3
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
 PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
 
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
-    $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/test.py \
+    $CONFIG \
+    $CHECKPOINT \
+    --launcher pytorch \
+    ${@:4}

tools/dist_train.sh

@@ -2,8 +2,19 @@
 
 CONFIG=$1
 GPUS=$2
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
 PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
 
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
-    $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/train.py \
+    $CONFIG \
+    --seed 0 \
+    --launcher pytorch ${@:3}