From 17f86a06f76ea50b1f6d64f28896e6f577558934 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kaan=20=C3=87olak?= Date: Mon, 13 Nov 2023 23:43:44 +0300 Subject: [PATCH 1/8] docs(how-to-guides): add training docs for centerpoint MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Kaan Çolak --- .../training-models.md | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index fa692c53666..4b9d3e96747 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -28,8 +28,17 @@ an example dataset containing three distinct classes (green, yellow, red), which Detailed instructions for training the traffic light classifier model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/traffic_light_classifier/README.md)**. - +To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the +**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training CenterPoint model. + +In order to assist you with your training process, we have also included an example dataset in the Tier4 dataset format. +This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. +You can utilize this example dataset to facilitate your training efforts. + +Detailed instructions for training the CenterPoint model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/lidar_centerpoint/README.md)**. From be964e5c05c18d83c57197fb9744815cbb7e2822 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kaan=20=C3=87olak?= Date: Tue, 14 Nov 2023 11:03:15 +0300 Subject: [PATCH 2/8] Update docs/how-to-guides/training-machine-learning-models/training-models.md Co-authored-by: Yukihiro Saito --- .../training-machine-learning-models/training-models.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index 4b9d3e96747..bcc28e39bbc 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -37,7 +37,7 @@ fork the original repository and made the necessary code modifications to suppor To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the **"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training CenterPoint model. -In order to assist you with your training process, we have also included an example dataset in the Tier4 dataset format. +In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format. This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. You can utilize this example dataset to facilitate your training efforts. From 6039d458c4de420084589e7d03ce4793ef7582f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kaan=20=C3=87olak?= Date: Thu, 7 Dec 2023 15:20:58 +0900 Subject: [PATCH 3/8] docs(how-to-guides): small fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Kaan Çolak --- .../training-machine-learning-models/training-models.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index bcc28e39bbc..027a02fd6e4 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -31,11 +31,11 @@ Detailed instructions for training the traffic light classifier model can be fou ## Training CenterPoint 3D object detection model The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. CenterPoint -implementation of mmdetection3d uses 10 input feature for PointPillars voxel encoder. However, Autoware employs 9 pillar features for CenterPoint, in accordance with the original research paper. Therefore, we have -fork the original repository and made the necessary code modifications to support the use of 9 PointPillar input features. The forked repository can be found **[here](https://github.com/autowarefoundation/mmdetection3d)**. +implementation of mmdetection3d uses 10 input features for the PointPillars voxel encoder. However, Autoware employs 9 pillar features for CenterPoint, in accordance with the original research paper. Therefore, we have +forked the original repository and made the necessary code modifications to support the use of 9 PointPillar input features. The forked repository can be found **[here](https://github.com/autowarefoundation/mmdetection3d)**. To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the -**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training CenterPoint model. +**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training the CenterPoint model. In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format. This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. From 5468a2bd1e12dee345444cebe0c361fccafafa64 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kaan=20=C3=87olak?= Date: Tue, 27 Feb 2024 20:19:18 +0300 Subject: [PATCH 4/8] docs(how-to-guides): small fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Kaan Çolak --- .../training-machine-learning-models/training-models.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index 027a02fd6e4..15bc565ad61 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -30,9 +30,10 @@ Detailed instructions for training the traffic light classifier model can be fou ## Training CenterPoint 3D object detection model -The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. CenterPoint -implementation of mmdetection3d uses 10 input features for the PointPillars voxel encoder. However, Autoware employs 9 pillar features for CenterPoint, in accordance with the original research paper. Therefore, we have -forked the original repository and made the necessary code modifications to support the use of 9 PointPillar input features. The forked repository can be found **[here](https://github.com/autowarefoundation/mmdetection3d)**. +The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. +The mmdetection3d repository includes an additional voxel encoder feature, known as voxel center z, not originally used in the **[main implementation](https://github.com/tianweiy/CenterPoint)**, +Autoware maintains consistency with the input size of the original implementation. Consequently, to ensure seamless integration with Autoware's lidar centerpoint package, +Therefore, we have forked the original repository and made the requisite code modifications. The forked repository is accessible **[here](https://github.com/autowarefoundation/mmdetection3d)**. To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the **"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training the CenterPoint model. From 3d70030d78a46baa8f8bcd79e3b4b884ed39b00a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 27 Feb 2024 17:19:44 +0000 Subject: [PATCH 5/8] style(pre-commit): autofix --- .../training-machine-learning-models/training-models.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index 15bc565ad61..0afe81938cc 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -30,8 +30,8 @@ Detailed instructions for training the traffic light classifier model can be fou ## Training CenterPoint 3D object detection model -The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. -The mmdetection3d repository includes an additional voxel encoder feature, known as voxel center z, not originally used in the **[main implementation](https://github.com/tianweiy/CenterPoint)**, +The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. +The mmdetection3d repository includes an additional voxel encoder feature, known as voxel center z, not originally used in the **[main implementation](https://github.com/tianweiy/CenterPoint)**, Autoware maintains consistency with the input size of the original implementation. Consequently, to ensure seamless integration with Autoware's lidar centerpoint package, Therefore, we have forked the original repository and made the requisite code modifications. The forked repository is accessible **[here](https://github.com/autowarefoundation/mmdetection3d)**. From 25f7f0f0c0c1e409df897deb6c5dbea1d8026cd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kaan=20=C3=87olak?= Date: Wed, 28 Feb 2024 12:31:33 +0300 Subject: [PATCH 6/8] doc(how-to-guides): refactor MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Kaan Çolak --- .../training-models.md | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index 0afe81938cc..3f834c455db 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -30,16 +30,11 @@ Detailed instructions for training the traffic light classifier model can be fou ## Training CenterPoint 3D object detection model -The CenterPoint 3D object detection model within the Autoware has been trained using the **[open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)** repository. -The mmdetection3d repository includes an additional voxel encoder feature, known as voxel center z, not originally used in the **[main implementation](https://github.com/tianweiy/CenterPoint)**, -Autoware maintains consistency with the input size of the original implementation. Consequently, to ensure seamless integration with Autoware's lidar centerpoint package, -Therefore, we have forked the original repository and made the requisite code modifications. The forked repository is accessible **[here](https://github.com/autowarefoundation/mmdetection3d)**. +The CenterPoint 3D object detection model within the Autoware has been trained using the **[autowarefoundation/mmdetection3d](https://github.com/autowarefoundation/mmdetection3d/blob/main/projects/AutowareCenterPoint/README.md)** repository. -To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with the -**"lidar_centerpoint"** package. These instructions will provide a step-by-step guide for training the CenterPoint model. +To train custom CenterPoint models and convert them into ONNX format for deployment in Autoware, please refer to the instructions provided in the README file included with Autoware's +**[lidar_centerpoint](https://autowarefoundation.github.io/autoware.universe/main/perception/lidar_centerpoint/)** package. These instructions will provide a step-by-step guide for training the CenterPoint model. In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format. This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. -You can utilize this example dataset to facilitate your training efforts. - -Detailed instructions for training the CenterPoint model can be found **[here](https://github.com/autowarefoundation/autoware.universe/blob/main/perception/lidar_centerpoint/README.md)**. +You can utilize this example dataset to facilitate your training efforts. \ No newline at end of file From f529a3c689cacaa276368f56434881c87037c17a Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 28 Feb 2024 09:32:03 +0000 Subject: [PATCH 7/8] style(pre-commit): autofix --- .../training-machine-learning-models/training-models.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index 3f834c455db..a6f5f74270b 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -37,4 +37,4 @@ To train custom CenterPoint models and convert them into ONNX format for deploym In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format. This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. -You can utilize this example dataset to facilitate your training efforts. \ No newline at end of file +You can utilize this example dataset to facilitate your training efforts. From 3edd8015062f0fba8c2bc5e0bd567cdcb7ee30eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Fatih=20C=C4=B1r=C4=B1t?= Date: Tue, 26 Mar 2024 20:44:09 +0300 Subject: [PATCH 8/8] Update docs/how-to-guides/training-machine-learning-models/training-models.md --- .../training-machine-learning-models/training-models.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/how-to-guides/training-machine-learning-models/training-models.md b/docs/how-to-guides/training-machine-learning-models/training-models.md index a6f5f74270b..4f9e55cfef5 100644 --- a/docs/how-to-guides/training-machine-learning-models/training-models.md +++ b/docs/how-to-guides/training-machine-learning-models/training-models.md @@ -36,5 +36,7 @@ To train custom CenterPoint models and convert them into ONNX format for deploym **[lidar_centerpoint](https://autowarefoundation.github.io/autoware.universe/main/perception/lidar_centerpoint/)** package. These instructions will provide a step-by-step guide for training the CenterPoint model. In order to assist you with your training process, we have also included an example dataset in the TIER IV dataset format. + This dataset contains 600 lidar frames and covers 5 classes, including 6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks. + You can utilize this example dataset to facilitate your training efforts.