[Feature] Support DGCNN

.dev_scripts/gather_models.py

@@ -25,6 +25,7 @@
  '_6x_': 73,
  '_50e_': 50,
  '_80e_': 80,
+ '_100e_': 100,
  '_150e_': 150,
  '_200e_': 200,
  '_250e_': 250,

README.md

@@ -75,6 +75,7 @@ Support backbones:
 - [x] PointNet (CVPR'2017)
 - [x] PointNet++ (NeurIPS'2017)
 - [x] RegNet (CVPR'2020)
+- [x] DGCNN (TOG'2019)
 
 Support methods
 
@@ -94,25 +95,27 @@ Support methods
 - [x] [Group-Free-3D (Arxiv'2021)](configs/groupfree3d/README.md)
 - [x] [ImVoxelNet (Arxiv'2021)](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)
+
+| | ResNet | ResNeXt | SENet |PointNet++ |DGCNN | HRNet | RegNetX | Res2Net |
+|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|
+| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ |
+| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
 
 Other features
 - [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)

README_zh-CN.md

@@ -74,6 +74,7 @@ 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)
 
 已支持的算法：
 
@@ -93,25 +94,27 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 - [x] [Group-Free-3D (Arxiv'2021)](configs/groupfree3d/README.md)
 - [x] [ImVoxelNet (Arxiv'2021)](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)
+
+| | ResNet | ResNeXt | SENet |PointNet++ |DGCNN | HRNet | RegNetX | Res2Net |
+|--------------------|:--------:|:--------:|:--------:|:---------:|:---------:|:-----:|:--------:|:-----:|
+| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ |
+| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ |
+| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
+| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
 
 其他特性
 - [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)

configs/_base_/models/dgcnn.py

@@ -0,0 +1,28 @@
+# model settings
+model = dict(
+ type='EncoderDecoder3D',
+ backbone=dict(
+ type='DGCNN',
+ 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_/schedules/seg_cosine_100e.py

@@ -0,0 +1,8 @@
+# optimizer
+# This schedule is mainly used on S3DIS dataset in segmentation task
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='CosineAnnealing', warmup=None, min_lr=1e-5)
+
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=100)

configs/dgcnn/README.md

@@ -0,0 +1,43 @@
+# Dynamic Graph CNN for Learning on Point Clouds
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+We implement DGCNN and provide the result and checkpoints on S3DIS dataset.
+
+```
+@article{dgcnn,
+ title={Dynamic Graph CNN for Learning on Point Clouds},
+ author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
+ journal={ACM Transactions on Graphics (TOG)},
+ year={2019}
+}
+```
+
+**Notice**: We follow the implementations in the original DGCNN paper and a PyTorch implementation of DGCNN [code](https://github.com/AnTao97/dgcnn.pytorch).
+
+## Results
+
+### S3DIS
+
+| Method | Split | Lr schd | Mem (GB) | Inf time (fps) | mIoU (Val set) | Download |
+| :-------------------------------------------------------------------------: | :----: | :--------: | :------: | :------------: | :------------: | :----------------------: |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_1 | cosine 100e | 13.1 | | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734-39658f14.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area1/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_000734.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_2 | cosine 100e | 13.1 | | 40.68 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648-aea9ecb6.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area2/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210731_144648.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_3 | cosine 100e | 13.1 | | 69.38 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629-2ff50ee0.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area3/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210801_154629.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_4 | cosine 100e | 13.1 | | 50.07 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551-dffab9cd.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area4/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_073551.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_5 | cosine 100e | 13.1 | | 50.59 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824-f277e0c5.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area5/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210730_235824.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | Area_6 | cosine 100e | 13.1 | | 77.94 | [model](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317-e3511b32.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class/area6/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class_20210802_154317.log.json) |
+| [DGCNN](./dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py) | 6-fold | | | | 59.43 | |
+
+**Notes:**
+
+- We use XYZ+Color+Normalized_XYZ as input in all the experiments on S3DIS dataset.
+- `Area_5` Split means training the model on Area_1, 2, 3, 4, 6 and testing on Area_5.
+- `6-fold` Split means the overall result of 6 different splits (Area_1, Area_2, Area_3, Area_4, Area_5 and Area_6 Splits).
+- Modify `train_area` and `test_area` in the S3DIS dataset's [config](./configs/_base_/datasets/s3dis_seg-3d-13class.py) to set the training areas and testing areas respectively.
+
+## Indeterminism
+
+Since DGCNN testing adopts sliding patch inference which involves random point sampling, and the test script uses fixed random seeds while the random seeds of validation in training are not fixed, the test results may be slightly different from the results reported above.

configs/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py

@@ -0,0 +1,24 @@
+_base_ = [
+ '../_base_/datasets/s3dis_seg-3d-13class.py', '../_base_/models/dgcnn.py',
+ '../_base_/schedules/seg_cosine_100e.py', '../_base_/default_runtime.py'
+]
+
+# data settings
+data = dict(samples_per_gpu=2)
+evaluation = dict(interval=2)
+
+# model settings
+model = dict(
+ backbone=dict(in_channels=9), # [xyz, rgb, normalized_xyz]
+ decode_head=dict(
+ num_classes=13, ignore_index=13,
+ loss_decode=dict(class_weight=None)), # S3DIS doesn't use class_weight
+ test_cfg=dict(
+ num_points=4096,
+ block_size=1.0,
+ sample_rate=0.5,
+ use_normalized_coord=True,
+ batch_size=24))
+
+# runtime settings
+checkpoint_config = dict(interval=2)

configs/dgcnn/metafile.yml

@@ -0,0 +1,24 @@
+Collections:
+ - Name: DGCNN
+ Metadata:
+ Training Techniques:
+ - SGD
+ Training Resources: 4x Titan XP GPUs
+ Architecture:
+ - DGCNN
+ Paper: https://arxiv.org/abs/1801.07829
+ README: configs/dgcnn/README.md
+
+Models:
+ - Name: dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py
+ In Collection: DGCNN
+ Config: configs/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class.py
+ Metadata:
+ Training Data: S3DIS
+ Training Memory (GB): 13.1
+ Results:
+ - Task: 3D Semantic Segmentation
+ Dataset: S3DIS
+ Metrics:
+ mIoU: 59.43
+ Weights: https://download.openmmlab.com/mmdetection3d/v0.17.0_models/dgcnn/dgcnn_32x4_cosine_100e_s3dis_seg-3d-13class

docs/model_zoo.md

@@ -77,3 +77,7 @@ Please refer to [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/ma
 ### PAConv
 
 Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) for details. We provide PAConv baselines on S3DIS dataset.
+
+### DGCNN
+
+Please refer to [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dgcnn) for details. We provide DGCNN baselines on S3DIS dataset.

docs_zh-CN/model_zoo.md

@@ -75,3 +75,11 @@
 ### ImVoxelNet
 
 请参考 [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/imvoxelnet) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果。
+
+### PAConv
+
+请参考 [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果.
+
+### DGCNN
+
+请参考 [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dgcnn) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果.

mmdet3d/models/backbones/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from mmdet.models.backbones import SSDVGG, HRNet, ResNet, ResNetV1d, ResNeXt
+from .dgcnn import DGCNN
 from .multi_backbone import MultiBackbone
 from .nostem_regnet import NoStemRegNet
 from .pointnet2_sa_msg import PointNet2SAMSG
@@ -8,5 +9,5 @@
 
 __all__ = [
  'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 'NoStemRegNet',
- 'SECOND', 'PointNet2SASSG', 'PointNet2SAMSG', 'MultiBackbone'
+ 'SECOND', 'DGCNN', 'PointNet2SASSG', 'PointNet2SAMSG', 'MultiBackbone'
 ]