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README.md
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......@@ -18,10 +18,11 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
[2020-07-29] `OpenPCDet` v0.3.0 is released with the following features:
* The Point-based and Anchor-Free models (`PointRCNN`, `PartA2-Free`) are supported now.
* The NuScenes dataset is supported with strong baseline results (`CBGS`).
[2020-07-30] `OpenPCDet` v0.3.0 is released with the following features:
* The Point-based and Anchor-Free models ([`PointRCNN`](#KITTI-3D-Object-Detection-Baselines), [`PartA2-Free`](#KITTI-3D-Object-Detection-Baselines)) are supported now.
* The NuScenes dataset is supported with strong baseline results ([`SECOND-MultiHead (CBGS)`](#NuScenes-3D-Object-Detection-Baselines) and [`PointPillar-MultiHead`](#NuScenes-3D-Object-Detection-Baselines)).
* High efficiency than last version, support `PyTorch 1.1~1.5` and `spconv 1.0~1.2` simultaneously.
[2020-07-17] Add simple visualization codes and a quick demo to test with custom data.
[2020-06-24] `OpenPCDet` v0.2.0 is released with pretty new structures to support more models and datasets.
......@@ -81,17 +82,18 @@ Contributions are also welcomed.
### KITTI 3D Object Detection Baselines
Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset.
All models are trained with 8 GTX 1080Ti GPUs and are available for download.
* All models are trained with 8 GTX 1080Ti GPUs and are available for download.
* The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
| | training time | Car | Pedestrian | Cyclist | download |
|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|
| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) |
| [SECOND](tools/cfgs/kitti_models/second.yaml) | ~2 hours | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
| [PointPillar](tools/cfgs/kitti_models/pointpillar.yaml) |~1.2 hours| 77.28 | 52.29 | 62.68 | [model-18M](https://drive.google.com/file/d/1wMxWTpU1qUoY3DsCH31WJmvJxcjFXKlm/view?usp=sharing) |
| [SECOND](tools/cfgs/kitti_models/second.yaml) | ~1.7 hours | 78.62 | 52.98 | 67.15 | [model-20M](https://drive.google.com/file/d/1-01zsPOsqanZQqIIyy7FpNXStL3y4jdR/view?usp=sharing) |
| [PointRCNN](tools/cfgs/kitti_models/pointrcnn.yaml) | ~3 hours | 78.70 | 54.41 | 72.11 | [model-16M](https://drive.google.com/file/d/1BCX9wMn-GYAfSOPpyxf6Iv6fc0qKLSiU/view?usp=sharing)|
| [PointRCNN-IoU](tools/cfgs/kitti_models/pointrcnn_iou.yaml) | ~3 hours | 78.75 | 58.32 | 71.34 | [model-16M](https://drive.google.com/file/d/1V0vNZ3lAHpEEt0MlT80eL2f41K2tHm_D/view?usp=sharing)|
| [Part-A^2-Free](tools/cfgs/kitti_models/PartA2_free.yaml) | ~4 hours| 78.72 | 65.99 | 74.29 | [model-226M](https://drive.google.com/file/d/1lcUUxF8mJgZ_e-tZhP1XNQtTBuC-R0zr/view?usp=sharing) |
| [Part-A^2-Anchor](tools/cfgs/kitti_models/PartA2.yaml) | ~5 hours| 79.40 | 60.05 | 69.90 | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
| [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~6 hours| 83.61 | 57.90 | 70.47 | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) |
| [Part-A^2-Free](tools/cfgs/kitti_models/PartA2_free.yaml) | ~3.8 hours| 78.72 | 65.99 | 74.29 | [model-226M](https://drive.google.com/file/d/1lcUUxF8mJgZ_e-tZhP1XNQtTBuC-R0zr/view?usp=sharing) |
| [Part-A^2-Anchor](tools/cfgs/kitti_models/PartA2.yaml) | ~4.3 hours| 79.40 | 60.05 | 69.90 | [model-244M](https://drive.google.com/file/d/10GK1aCkLqxGNeX3lVu8cLZyE0G8002hY/view?usp=sharing) |
| [PV-RCNN](tools/cfgs/kitti_models/pv_rcnn.yaml) | ~5 hours| 83.61 | 57.90 | 70.47 | [model-50M](https://drive.google.com/file/d/1lIOq4Hxr0W3qsX83ilQv0nk1Cls6KAr-/view?usp=sharing) |
### NuScenes 3D Object Detection Baselines
All models are trained with 8 GTX 1080Ti GPUs and are available for download.
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docs/GETTING_STARTED.md
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......@@ -50,7 +50,7 @@ OpenPCDet
pip install nuscenes-devkit==1.0.5
```
* Generate the data infos by running the following command:
* Generate the data infos by running the following command (it may take several hours):
```python
python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
--cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \
......
docs/INSTALL.md
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......@@ -4,9 +4,9 @@
All the codes are tested in the following environment:
* Linux (tested on Ubuntu 14.04/16.04)
* Python 3.6+
* PyTorch 1.1 or higher (tested on PyTorch 1.1)
* CUDA 9.0 or higher
* `spconv v1.0` ([commit 8da6f96](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634))
* PyTorch 1.1 or higher (tested on PyTorch 1.1, 1,3, 1,5)
* CUDA 9.0 or higher (PyTorch 1.3+ needs CUDA 9.2+)
* [`spconv v1.0 (commit 8da6f96)`](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634) or [`spconv v1.2`](https://github.com/traveller59/spconv)
### Install `pcdet v0.3`
......@@ -24,8 +24,9 @@ b. Install the dependent libraries as follows:
pip install -r requirements.txt
```
* Install the SparseConv library, we use the non-official implementation from [`spconv`](https://github.com/traveller59/spconv).
Note that we use the initial version of `spconv`, make sure you install the `spconv v1.0` ([commit 8da6f96](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634)) instead of the latest one.
* Install the SparseConv library, we use the implementation from [`[spconv]`](https://github.com/traveller59/spconv).
* If you use PyTorch 1.1, then make sure you install the `spconv v1.0` with ([commit 8da6f96](https://github.com/traveller59/spconv/tree/8da6f967fb9a054d8870c3515b1b44eca2103634)) instead of the latest one.
* If you use PyTorch 1.3+, then you need to install the `spconv v1.2`. As mentioned by the author of [`spconv`](https://github.com/traveller59/spconv), you need to use their docker if you use PyTorch 1.4+.
c. Install this `pcdet` library by running the following command:
```shell
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tools/test.py
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......@@ -133,13 +133,18 @@ def main():
args, cfg = parse_config()
if args.launcher == 'none':
dist_test = False
total_gpus = 1
else:
total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
args.tcp_port, args.local_rank, backend='nccl'
)
dist_test = True
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU if args.batch_size is None else args.batch_size
if args.batch_size is None:
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
else:
assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
args.batch_size = args.batch_size // total_gpus
output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
output_dir.mkdir(parents=True, exist_ok=True)
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tools/train.py
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......@@ -57,13 +57,19 @@ def main():
args, cfg = parse_config()
if args.launcher == 'none':
dist_train = False
total_gpus = 1
else:
total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
args.tcp_port, args.local_rank, backend='nccl'
)
dist_train = True
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU if args.batch_size is None else args.batch_size
if args.batch_size is None:
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
else:
assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
args.batch_size = args.batch_size // total_gpus
args.epochs = cfg.OPTIMIZATION.NUM_EPOCHS if args.epochs is None else args.epochs
if args.fix_random_seed:
......
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