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Unverified Commit 32a4328b authored by Wenwei Zhang's avatar Wenwei Zhang Committed by GitHub
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Bump version to V1.0.0rc0 

Bump version to V1.0.0rc0
parents 86cc487c a8817998
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Showing with 331 additions and 122 deletions
.dev_scripts/gather_models.py
View file @ 32a4328b
......@@ -3,17 +3,28 @@
Usage:
python gather_models.py ${root_path} ${out_dir}
Example:
python gather_models.py \
work_dirs/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d \
work_dirs/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d
Note that before running the above command, rename the directory with the
config name if you did not use the default directory name, create
a corresponding directory 'pgd' under the above path and put the used config
into it.
"""
import argparse
import glob
import json
import mmcv
import shutil
import subprocess
import torch
from os import path as osp
import mmcv
import torch
# build schedule look-up table to automatically find the final model
SCHEDULES_LUT = {
'_1x_': 12,
......@@ -25,6 +36,7 @@ SCHEDULES_LUT = {
'_6x_': 73,
'_50e_': 50,
'_80e_': 80,
'_100e_': 100,
'_150e_': 150,
'_200e_': 200,
'_250e_': 250,
......@@ -35,16 +47,18 @@ SCHEDULES_LUT = {
RESULTS_LUT = {
'coco': ['bbox_mAP', 'segm_mAP'],
'nus': ['pts_bbox_NuScenes/NDS', 'NDS'],
'kitti-3d-3class': [
'KITTI/Overall_3D_moderate',
'Overall_3D_moderate',
],
'kitti-3d-3class': ['KITTI/Overall_3D_moderate', 'Overall_3D_moderate'],
'kitti-3d-car': ['KITTI/Car_3D_moderate_strict', 'Car_3D_moderate_strict'],
'lyft': ['score'],
'scannet_seg': ['miou'],
's3dis_seg': ['miou'],
'scannet': ['mAP_0.50'],
'sunrgbd': ['mAP_0.50']
'sunrgbd': ['mAP_0.50'],
'kitti-mono3d': [
'img_bbox/KITTI/Car_3D_AP40_moderate_strict',
'Car_3D_AP40_moderate_strict'
],
'nus-mono3d': ['img_bbox_NuScenes/NDS', 'NDS']
}
......@@ -144,15 +158,13 @@ def main():
# and parse the best performance
model_infos = []
for used_config in used_configs:
exp_dir = osp.join(models_root, used_config)
# get logs
log_json_path = glob.glob(osp.join(exp_dir, '*.log.json'))[0]
log_txt_path = glob.glob(osp.join(exp_dir, '*.log'))[0]
log_json_path = glob.glob(osp.join(models_root, '*.log.json'))[0]
log_txt_path = glob.glob(osp.join(models_root, '*.log'))[0]
model_performance = get_best_results(log_json_path)
final_epoch = model_performance['epoch']
final_model = 'epoch_{}.pth'.format(final_epoch)
model_path = osp.join(exp_dir, final_model)
model_path = osp.join(models_root, final_model)
# skip if the model is still training
if not osp.exists(model_path):
......@@ -181,7 +193,7 @@ def main():
model_name = model['config'].split('/')[-1].rstrip(
'.py') + '_' + model['model_time']
publish_model_path = osp.join(model_publish_dir, model_name)
trained_model_path = osp.join(models_root, model['config'],
trained_model_path = osp.join(models_root,
'epoch_{}.pth'.format(model['epochs']))
# convert model
......@@ -190,11 +202,10 @@ def main():
# copy log
shutil.copy(
osp.join(models_root, model['config'], model['log_json_path']),
osp.join(models_root, model['log_json_path']),
osp.join(model_publish_dir, f'{model_name}.log.json'))
shutil.copy(
osp.join(models_root, model['config'],
model['log_json_path'].rstrip('.json')),
osp.join(models_root, model['log_json_path'].rstrip('.json')),
osp.join(model_publish_dir, f'{model_name}.log'))
# copy config to guarantee reproducibility
......
.dev_scripts/test_benchmark.sh
View file @ 32a4328b
......@@ -25,11 +25,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION fcos3d_r
$CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py/latest.pth --eval map \
2>&1|tee $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py/FULL_LOG.txt &
echo 'configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
mkdir -p $CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
$CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/latest.pth --eval map \
2>&1|tee $CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
echo 'configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
$CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/latest.pth --eval map \
2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
echo 'configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py' &
mkdir -p $CHECKPOINT_DIR/configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py
......
.dev_scripts/train_benchmark.sh
View file @ 32a4328b
......@@ -25,11 +25,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION fcos3d_
$CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
2>&1|tee $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py/FULL_LOG.txt &
echo 'configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
mkdir -p $CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
$CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
2>&1|tee $CHECKPOINT_DIR/configs/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
echo 'configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
$CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
echo 'configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py' &
mkdir -p $CHECKPOINT_DIR/configs/free_anchor/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d.py
......
.github/ISSUE_TEMPLATE/error-report.md
View file @ 32a4328b
......@@ -28,7 +28,7 @@ A placeholder for the command.
**Environment**
1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment infomation and paste it here.
1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment information and paste it here.
2. You may add addition that may be helpful for locating the problem, such as
- How you installed PyTorch [e.g., pip, conda, source]
- Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
......
.github/ISSUE_TEMPLATE/reimplementation_questions.md
View file @ 32a4328b
......@@ -46,7 +46,7 @@ A placeholder for the config.
**Environment**
1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment infomation and paste it here.
1. Please run `python mmdet3d/utils/collect_env.py` to collect necessary environment information and paste it here.
2. You may add addition that may be helpful for locating the problem, such as
- How you installed PyTorch [e.g., pip, conda, source]
- Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
......
.pre-commit-config.yaml
View file @ 32a4328b
......@@ -3,12 +3,8 @@ repos:
rev: 3.8.3
hooks:
- id: flake8
- repo: https://github.com/asottile/seed-isort-config
rev: v2.2.0
hooks:
- id: seed-isort-config
- repo: https://github.com/timothycrosley/isort
rev: 5.0.2
- repo: https://github.com/PyCQA/isort
rev: 5.10.1
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-yapf
......@@ -43,3 +39,9 @@ repos:
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0 # Use the ref you want to point at
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmdet3d"] # replace the dir_to_check with your expected directory to check
README.md
View file @ 32a4328b
......@@ -25,16 +25,12 @@
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**News**: We released the codebase v0.18.1.
**News**: We released the codebase v1.0.0rc0.
In addition, we have preliminarily supported several new models on the [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) branch, including [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/dgcnn/README.md), [SMOKE](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/smoke/README.md) and [PGD](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/pgd/README.md).
Note: We are going through large refactoring to provide simpler and more unified usage of many modules. Thus, few features will be added to the master branch in the following months.
Note: We are going through large refactoring to provide simpler and more unified usage of many modules.
The compatibilities of models are broken due to the unification and simplification of coordinate systems. For now, most models are benchmarked with similar performance, though few models are still being benchmarked.
You can start experiments with [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) if you are interested. Please note that our new features will only be supported in v1.0.0 branch afterward.
In the [nuScenes 3D detection challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
Code and models for the best vision-only method, [FCOS3D](https://arxiv.org/abs/2104.10956), have been released. Please stay tuned for [MoCa](https://arxiv.org/abs/2012.12741).
......@@ -87,11 +83,9 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v0.18.1 was released in 1/2/2022.
v1.0.0rc0 was released in 18/2/2022.
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
For branch v1.0.0.dev0, please refer to [changelog_v1.0.md](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) for our latest features and more details.
## Benchmark and model zoo
Supported methods and backbones are shown in the below table.
......@@ -102,6 +96,8 @@ Support backbones:
- [x] PointNet (CVPR'2017)
- [x] PointNet++ (NeurIPS'2017)
- [x] RegNet (CVPR'2020)
- [x] DGCNN (TOG'2019)
- [x] DLA (CVPR'2018)
Support methods
......@@ -121,25 +117,31 @@ Support methods
- [x] [Group-Free-3D (ICCV'2021)](configs/groupfree3d/README.md)
- [x] [ImVoxelNet (WACV'2022)](configs/imvoxelnet/README.md)
- [x] [PAConv (CVPR'2021)](configs/paconv/README.md)
| | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net |
|--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:|
| SECOND | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| SSN | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ☐ | ☐ | ✗ | ☐ | ☐ | ☐ |
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
- [x] [DGCNN (TOG'2019)](configs/dgcnn/README.md)
- [x] [SMOKE (CVPRW'2020)](configs/smoke/README.md)
- [x] [PGD (CoRL'2021)](configs/pgd/README.md)
| | ResNet | ResNeXt | SENet |PointNet++ |DGCNN | HRNet | RegNetX | Res2Net | DLA |
|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|:---:|
| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓
| PGD | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗
Other features
- [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
......
README_zh-CN.md
View file @ 32a4328b
......@@ -25,16 +25,12 @@
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**新闻**: 我们发布了版本 v0.18.1.
**新闻**: 我们发布了版本 v1.0.0rc0.
另外，我们在 [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) 分支初步支持了多个新模型，包括 [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/dgcnn/README.md), [SMOKE](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/smoke/README.md) 和 [PGD](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/configs/pgd/README.md)。
说明：我们正在进行大规模的重构，以提供对许多模块更简单、更统一的使用。因此，在接下来的几个月里，很少有功能会添加到主分支中。
说明：我们正在进行大规模的重构，以提供对许多模块更简单、更统一的使用。
由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。
如果您感兴趣，可以开始使用 [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) 分支进行实验。请注意，我们的新功能将只支持在 v1.0.0 分支。
在第三届 [nuScenes 3D 检测挑战赛](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any)（第五届 AI Driving Olympics, NeurIPS 2020）中，我们获得了最佳 PKL 奖、第三名和最好的纯视觉的结果，相关的代码和模型将会在不久后发布。
最好的纯视觉方法 [FCOS3D](https://arxiv.org/abs/2104.10956) 的代码和模型已经发布。请继续关注我们的多模态检测器 [MoCa](https://arxiv.org/abs/2012.12741)。
......@@ -87,11 +83,9 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
## 更新日志
最新的版本 v0.18.1 在 2022.2.1 发布。
最新的版本 v1.0.0rc0 在 2022.2.18 发布。
如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/zh_cn/changelog.md)。
对于分支 [v1.0.0.dev0](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0) ，请参考 [v1.0 更新日志](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) 来了解我们的最新功能和更多细节。
## 基准测试和模型库
测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
......@@ -101,6 +95,8 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
- [x] PointNet (CVPR'2017)
- [x] PointNet++ (NeurIPS'2017)
- [x] RegNet (CVPR'2020)
- [x] DGCNN (TOG'2019)
- [x] DLA (CVPR'2018)
已支持的算法：
......@@ -120,25 +116,31 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
- [x] [Group-Free-3D (ICCV'2021)](configs/groupfree3d/README.md)
- [x] [ImVoxelNet (WACV'2022)](configs/imvoxelnet/README.md)
- [x] [PAConv (CVPR'2021)](configs/paconv/README.md)
| | ResNet | ResNeXt | SENet |PointNet++ | HRNet | RegNetX | Res2Net |
|--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:|
| SECOND | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| SSN | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ☐ | ☐ | ✗ | ☐ | ☐ | ☐ |
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
- [x] [DGCNN (TOG'2019)](configs/dgcnn/README.md)
- [x] [SMOKE (CVPRW'2020)](configs/smoke/README.md)
- [x] [PGD (CoRL'2021)](configs/pgd/README.md)
| | ResNet | ResNeXt | SENet |PointNet++ |DGCNN | HRNet | RegNetX | Res2Net | DLA |
|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|:---:|
| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗
| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗
| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓
| PGD | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗
其他特性
- [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
......
configs/3dssd/README.md
View file @ 32a4328b
# 3DSSD: Point-based 3D Single Stage Object Detector
## Introduction
> [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/abs/2002.10187)
<!-- [ALGORITHM] -->
We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
## Abstract
```
@inproceedings{yang20203dssd,
author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
title = {3DSSD: Point-based 3D Single Stage Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020}
}
```
Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a lightweight and effective point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. We novelly propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet with our demand of accuracy and speed. Our paradigm is an elegant single stage anchor-free framework, showing great superiority to other existing methods. We evaluate 3DSSD on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
<div align=center>
<img src="https://user-images.githubusercontent.com/30491025/143854187-54ed1257-a046-4764-81cd-d2c8404137d3.png" width="800"/>
</div>
### Experiment details on KITTI datasets
## Introduction
We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
Some settings in our implementation are different from the [official implementation](https://github.com/Jia-Research-Lab/3DSSD), which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
1. We keep the scenes without any object while the official code skips these scenes in training. In the official implementation, only 3229 and 3394 samples are used as training and validation sets, respectively. In our implementation, we keep using 3712 and 3769 samples as training and validation sets, respectively, as those used for all the other models in our implementation on KITTI datasets.
......@@ -23,7 +22,7 @@ Some settings in our implementation are different from the [official implementat
3. While using [`DataBaseSampler`](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/dbsampler.py#L80) for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.
4. We perform detection using LIDAR coordinates while the official code uses camera coordinates.
## Results
## Results and models
### KITTI
......@@ -31,4 +30,15 @@ Some settings in our implementation are different from the [official implementat
| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py)| Car |72e|4.7||78.69(81.27)<sup>1</sup>|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438-b4276f56.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438.log.json)|
[1]: We report two different 3D object detection performance here. 78.69mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implemention in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
[1]: We report two different 3D object detection performance here. 78.69mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
## Citation
```latex
@inproceedings{yang20203dssd,
author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
title = {3DSSD: Point-based 3D Single Stage Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020}
}
```
configs/_base_/datasets/kitti-mono3d.py 0 → 100644
View file @ 32a4328b
dataset_type = 'KittiMonoDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=True)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='Resize', img_scale=(1242, 375), keep_ratio=True),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_3d', 'gt_labels_3d',
'centers2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='MultiScaleFlipAug',
img_scale=(1242, 375),
flip=False,
transforms=[
dict(type='RandomFlip3D'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img']),
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train_mono3d.coco.json',
info_file=data_root + 'kitti_infos_train.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
box_type_3d='Camera'),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
info_file=data_root + 'kitti_infos_val.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
info_file=data_root + 'kitti_infos_val.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'))
evaluation = dict(interval=2)
configs/_base_/default_runtime.py
View file @ 32a4328b
......@@ -16,3 +16,8 @@ work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'
configs/_base_/models/dgcnn.py 0 → 100644
View file @ 32a4328b
# model settings
model = dict(
type='EncoderDecoder3D',
backbone=dict(
type='DGCNNBackbone',
in_channels=9, # [xyz, rgb, normal_xyz], modified with dataset
num_samples=(20, 20, 20),
knn_modes=('D-KNN', 'F-KNN', 'F-KNN'),
radius=(None, None, None),
gf_channels=((64, 64), (64, 64), (64, )),
fa_channels=(1024, ),
act_cfg=dict(type='LeakyReLU', negative_slope=0.2)),
decode_head=dict(
type='DGCNNHead',
fp_channels=(1216, 512),
channels=256,
dropout_ratio=0.5,
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
loss_decode=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=None, # modified with dataset
loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='slide'))
configs/_base_/models/fcos3d.py
View file @ 32a4328b
model = dict(
type='FCOSMono3D',
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
......@@ -9,7 +8,10 @@ model = dict(
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe'),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
......@@ -29,6 +31,7 @@ model = dict(
pred_attrs=True,
pred_velo=True,
dir_offset=0.7854, # pi/4
dir_limit_offset=0,
strides=[8, 16, 32, 64, 128],
group_reg_dims=(2, 1, 3, 1, 2), # offset, depth, size, rot, velo
cls_branch=(256, ),
......@@ -54,6 +57,7 @@ model = dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
bbox_coder=dict(type='FCOS3DBBoxCoder', code_size=9),
norm_on_bbox=True,
centerness_on_reg=True,
center_sampling=True,
......
configs/_base_/models/hv_pointpillars_fpn_nus.py
View file @ 32a4328b
......@@ -49,8 +49,8 @@ model = dict(
ranges=[[-50, -50, -1.8, 50, 50, -1.8]],
scales=[1, 2, 4],
sizes=[
[0.8660, 2.5981, 1.], # 1.5/sqrt(3)
[0.5774, 1.7321, 1.], # 1/sqrt(3)
[2.5981, 0.8660, 1.], # 1.5 / sqrt(3)
[1.7321, 0.5774, 1.], # 1 / sqrt(3)
[1., 1., 1.],
[0.4, 0.4, 1],
],
......@@ -59,8 +59,7 @@ model = dict(
reshape_out=True),
assigner_per_size=False,
diff_rad_by_sin=True,
dir_offset=0.7854, # pi/4
dir_limit_offset=0,
dir_offset=-0.7854, # -pi / 4
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=9),
loss_cls=dict(
type='FocalLoss',
......
configs/_base_/models/hv_pointpillars_secfpn_kitti.py
View file @ 32a4328b
......@@ -34,6 +34,7 @@ model = dict(
in_channels=384,
feat_channels=384,
use_direction_classifier=True,
assign_per_class=True,
anchor_generator=dict(
type='AlignedAnchor3DRangeGenerator',
ranges=[
......@@ -41,7 +42,7 @@ model = dict(
[0, -39.68, -0.6, 69.12, 39.68, -0.6],
[0, -39.68, -1.78, 69.12, 39.68, -1.78],
],
sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
......
configs/_base_/models/hv_pointpillars_secfpn_waymo.py
View file @ 32a4328b
......@@ -48,15 +48,14 @@ model = dict(
[-74.88, -74.88, -0.1188, 74.88, 74.88, -0.1188],
[-74.88, -74.88, 0, 74.88, 74.88, 0]],
sizes=[
[2.08, 4.73, 1.77], # car
[0.84, 1.81, 1.77], # cyclist
[0.84, 0.91, 1.74] # pedestrian
[4.73, 2.08, 1.77], # car
[1.81, 0.84, 1.77], # cyclist
[0.91, 0.84, 1.74] # pedestrian
],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
dir_offset=0.7854, # pi/4
dir_limit_offset=0,
dir_offset=-0.7854, # -pi / 4
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7),
loss_cls=dict(
type='FocalLoss',
......
configs/_base_/models/hv_second_secfpn_kitti.py
View file @ 32a4328b
......@@ -37,7 +37,7 @@ model = dict(
[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -1.78, 70.4, 40.0, -1.78],
],
sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
......
configs/_base_/models/hv_second_secfpn_waymo.py
View file @ 32a4328b
......@@ -42,15 +42,14 @@ model = dict(
[-76.8, -51.2, 0, 76.8, 51.2, 0],
[-76.8, -51.2, -0.1188, 76.8, 51.2, -0.1188]],
sizes=[
[2.08, 4.73, 1.77], # car
[0.84, 0.91, 1.74], # pedestrian
[0.84, 1.81, 1.77] # cyclist
[4.73, 2.08, 1.77], # car
[0.91, 0.84, 1.74], # pedestrian
[1.81, 0.84, 1.77] # cyclist
],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
dir_offset=0.7854, # pi/4
dir_limit_offset=0,
dir_offset=-0.7854, # -pi / 4
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7),
loss_cls=dict(
type='FocalLoss',
......
configs/_base_/models/parta2.py
View file @ 32a4328b
......@@ -38,7 +38,7 @@ model = dict(
ranges=[[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -0.6, 70.4, 40.0, -0.6],
[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
sizes=[[0.8, 0.6, 1.73], [1.76, 0.6, 1.73], [3.9, 1.6, 1.56]],
rotations=[0, 1.57],
reshape_out=False),
diff_rad_by_sin=True,
......
configs/_base_/models/pgd.py 0 → 100644
View file @ 32a4328b
_base_ = './fcos3d.py'
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='PGDHead',
num_classes=10,
in_channels=256,
stacked_convs=2,
feat_channels=256,
use_direction_classifier=True,
diff_rad_by_sin=True,
pred_attrs=True,
pred_velo=True,
pred_bbox2d=True,
pred_keypoints=False,
dir_offset=0.7854, # pi/4
strides=[8, 16, 32, 64, 128],
group_reg_dims=(2, 1, 3, 1, 2), # offset, depth, size, rot, velo
cls_branch=(256, ),
reg_branch=(
(256, ), # offset
(256, ), # depth
(256, ), # size
(256, ), # rot
() # velo
),
dir_branch=(256, ),
attr_branch=(256, ),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_attr=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
norm_on_bbox=True,
centerness_on_reg=True,
center_sampling=True,
conv_bias=True,
dcn_on_last_conv=True,
use_depth_classifier=True,
depth_branch=(256, ),
depth_range=(0, 50),
depth_unit=10,
division='uniform',
depth_bins=6,
bbox_coder=dict(type='PGDBBoxCoder', code_size=9)),
test_cfg=dict(nms_pre=1000, nms_thr=0.8, score_thr=0.01, max_per_img=200))
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