fix: ml model path

launch/tier4_perception_launch/launch/object_recognition/detection/lidar_based_detection.launch.xml

@@ -10,7 +10,7 @@
   <arg name="use_validator" default="true" description="use obstacle_pointcloud based validator"/>
   <arg name="lidar_detection_score_threshold" default="0.35"/>
   <arg name="centerpoint_model_name" default="centerpoint_tiny"/>
-  <arg name="centerpoint_model_path" default="$(find-pkg-share lidar_centerpoint)/data"/>
+  <arg name="centerpoint_model_path" default="$(var data_path)/lidar_centerpoint"/>
   <arg name="lidar_model_param_path" default="$(find-pkg-share lidar_centerpoint)/config"/>
   <arg name="objects_validation_method" default="obstacle_pointcloud"/>
   <arg name="objects_filter_method" default="lanelet_filter"/>

launch/tier4_perception_launch/launch/perception.launch.xml

@@ -25,11 +25,12 @@
 
   <!-- CenterPoint model parameters -->
   <arg name="centerpoint_model_name" default="centerpoint_tiny" description="options: `centerpoint` or `centerpoint_tiny`"/>
-  <arg name="centerpoint_model_path" default="$(find-pkg-share lidar_centerpoint)/data"/>
+  <arg name="centerpoint_model_path" default="$(var data_path)/lidar_centerpoint"/>
 
   <!-- Common parameters -->
   <arg name="input/pointcloud" default="/sensing/lidar/concatenated/pointcloud" description="The topic will be used in the detection module"/>
   <arg name="mode" default="camera_lidar_fusion" description="options: `camera_lidar_radar_fusion`, `camera_lidar_fusion`, `lidar_radar_fusion`, `lidar` or `radar`"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
   <arg name="lidar_detection_model" default="centerpoint" description="options: `centerpoint`, `apollo`, `pointpainting`, `clustering`"/>
   <arg name="image_raw0" default="/sensing/camera/camera0/image_rect_color" description="image raw topic name"/>
   <arg name="camera_info0" default="/sensing/camera/camera0/camera_info" description="camera info topic name"/>
@@ -78,11 +79,11 @@
   <arg name="traffic_light_image_number" default="1" description="choose traffic light image raw number(1-2)"/>
   <arg
     name="traffic_light_fine_detector_model_path"
-    default="$(find-pkg-share traffic_light_fine_detector)/data"
+    default="$(var data_path)/traffic_light_fine_detector"
     description="options: `tlr_yolox_s_batch_**`. The batch number must be either one of 1, 4, 6"
   />
   <arg name="traffic_light_fine_detector_model_name" default="tlr_yolox_s_batch_6" description="options: `tlr_yolox_s_batch_**`. The batch number must be either one of 1, 4, 6"/>
-  <arg name="traffic_light_classifier_model_path" default="$(find-pkg-share traffic_light_classifier)/data" description="classifier onnx model path"/>
+  <arg name="traffic_light_classifier_model_path" default="$(var data_path)/traffic_light_classifier" description="classifier onnx model path"/>
   <arg
     name="traffic_light_classifier_model_name"
     default="traffic_light_classifier_mobilenetv2_batch_6"

perception/image_projection_based_fusion/launch/pointpainting_fusion.launch.xml

@@ -18,8 +18,9 @@
   <arg name="input/camera_info7" default="/camera_info7"/>
   <arg name="input/pointcloud" default="/sensing/lidar/top/rectified/pointcloud"/>
   <arg name="output/objects" default="objects"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
   <arg name="model_name" default="pointpainting" description="options: `pointpainting`"/>
-  <arg name="model_path" default="$(find-pkg-share image_projection_based_fusion)/data"/>
+  <arg name="model_path" default="$(var data_path)/image_projection_based_fusion"/>
   <arg name="model_param_path" default="$(find-pkg-share image_projection_based_fusion)/config/$(var model_name).param.yaml"/>
   <arg name="class_remapper_param_path" default="$(find-pkg-share lidar_centerpoint)/config/detection_class_remapper.param.yaml"/>
   <arg name="sync_param_path" default="$(find-pkg-share image_projection_based_fusion)/config/roi_sync.param.yaml"/>

perception/lidar_centerpoint/launch/centerpoint_vs_centerpoint-tiny/centerpoint_vs_centerpoint-tiny.launch.xml

@@ -5,6 +5,7 @@
   <arg name="score_threshold" default="0.35"/>
   <arg name="rviz_config1" default="$(var rviz_path)/centerpoint_tiny.rviz" description="rviz config"/>
   <arg name="rviz_config2" default="$(var rviz_path)/centerpoint.rviz" description="rviz config"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
 
   <set_parameter name="use_sim_time" value="true"/>
   <group>
@@ -21,7 +22,7 @@
         <arg name="input/pointcloud" value="$(var input/pointcloud)"/>
         <arg name="score_threshold" value="$(var score_threshold)"/>
         <arg name="model_name" value="centerpoint"/>
-        <arg name="model_path" value="$(find-pkg-share lidar_centerpoint)/data"/>
+        <arg name="model_path" value="$(var data_path)/lidar_centerpoint"/>
         <arg name="model_param_path" value="$(find-pkg-share lidar_centerpoint)/config/$(var model_name).param.yaml"/>
       </include>
     </group>
@@ -35,7 +36,7 @@
         <arg name="input/pointcloud" value="$(var input/pointcloud)"/>
         <arg name="score_threshold" value="$(var score_threshold)"/>
         <arg name="model_name" value="centerpoint_tiny"/>
-        <arg name="model_path" value="$(find-pkg-share lidar_centerpoint)/data"/>
+        <arg name="model_path" value="$(var data_path)/lidar_centerpoint"/>
         <arg name="model_param_path" value="$(find-pkg-share lidar_centerpoint)/config/$(var model_name).param.yaml"/>
       </include>
     </group>

perception/lidar_centerpoint/launch/lidar_centerpoint.launch.xml

@@ -2,8 +2,9 @@
 <launch>
   <arg name="input/pointcloud" default="/sensing/lidar/pointcloud"/>
   <arg name="output/objects" default="objects"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
   <arg name="model_name" default="centerpoint_tiny" description="options: `centerpoint` or `centerpoint_tiny`"/>
-  <arg name="model_path" default="$(find-pkg-share lidar_centerpoint)/data"/>
+  <arg name="model_path" default="$(var data_path)/lidar_centerpoint"/>
   <arg name="model_param_path" default="$(find-pkg-share lidar_centerpoint)/config/$(var model_name).param.yaml"/>
   <arg name="class_remapper_param_path" default="$(find-pkg-share lidar_centerpoint)/config/detection_class_remapper.param.yaml"/>
   <arg name="score_threshold" default="0.35"/>

perception/lidar_centerpoint/launch/single_inference_lidar_centerpoint.launch.xml

@@ -1,7 +1,8 @@
 <?xml version="1.0"?>
 <launch>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
   <arg name="model_name" default="centerpoint_tiny" description="options: `centerpoint` or `centerpoint_tiny`"/>
-  <arg name="model_path" default="$(find-pkg-share lidar_centerpoint)/data"/>
+  <arg name="model_path" default="$(var data_path)/lidar_centerpoint"/>
   <arg name="model_param_path" default="$(find-pkg-share lidar_centerpoint)/config/$(var model_name).param.yaml"/>
   <arg name="class_remapper_param_path" default="$(find-pkg-share lidar_centerpoint)/config/detection_class_remapper.param.yaml"/>
   <arg name="score_threshold" default="0.35"/>

perception/tensorrt_yolo/README.md

@@ -55,6 +55,7 @@ Jocher, G., et al. (2021). ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Rob
 
 | Name                    | Type   | Default Value | Description                                                        |
 | ----------------------- | ------ | ------------- | ------------------------------------------------------------------ |
+| `data_path`             | string | ""            | Packages data and artifacts directory path                         |
 | `onnx_file`             | string | ""            | The onnx file name for yolo model                                  |
 | `engine_file`           | string | ""            | The tensorrt engine file name for yolo model                       |
 | `label_file`            | string | ""            | The label file with label names for detected objects written on it |
@@ -71,7 +72,7 @@ This package includes multiple licenses.
 
 All YOLO ONNX models are converted from the officially trained model. If you need information about training datasets and conditions, please refer to the official repositories.
 
-All models are downloaded automatically when building. When launching the node with a model for the first time, the model is automatically converted to TensorRT, although this may take some time.
+All models are downloaded during env preparation by ansible (as mention in [installation](https://autowarefoundation.github.io/autoware-documentation/main/installation/autoware/source-installation/)). It is also possible to download them manually, see [Manual downloading of artifacts](https://github.com/autowarefoundation/autoware/tree/main/ansible/roles/artifacts) . When launching the node with a model for the first time, the model is automatically converted to TensorRT, although this may take some time.
 
 ### YOLOv3
 

perception/tensorrt_yolo/launch/tensorrt_yolo.launch.xml

@@ -3,19 +3,20 @@
   <arg name="label_file" default="coco.names"/>
   <arg name="input_topic" default="/image_raw"/>
   <arg name="output_topic" default="rois"/>
-  <arg name="engine_file" default="$(find-pkg-share tensorrt_yolo)/data/$(var yolo_type).engine"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="engine_file" default="$(var data_path)/tensorrt_yolo/$(var yolo_type).engine"/>
   <arg name="calib_image_directory" default="$(find-pkg-share tensorrt_yolo)/calib_image/"/>
   <arg name="mode" default="FP32"/>
   <arg name="gpu_id" default="0"/>
   <node pkg="tensorrt_yolo" exec="tensorrt_yolo_node" name="$(anon tensorrt_yolo)" output="screen">
     <remap from="in/image" to="$(var input_topic)"/>
     <remap from="out/objects" to="$(var output_topic)"/>
     <remap from="out/image" to="$(var output_topic)/debug/image"/>
-    <param name="onnx_file" type="str" value="$(find-pkg-share tensorrt_yolo)/data/$(var yolo_type).onnx"/>
+    <param name="onnx_file" type="str" value="$(var data_path)/tensorrt_yolo/$(var yolo_type).onnx"/>
     <param name="engine_file" type="str" value="$(var engine_file)"/>
-    <param name="label_file" type="str" value="$(find-pkg-share tensorrt_yolo)/data/$(var label_file)"/>
+    <param name="label_file" type="str" value="$(var data_path)/tensorrt_yolo/$(var label_file)"/>
     <param name="calib_image_directory" type="str" value="$(var calib_image_directory)"/>
-    <param name="calib_cache_file" type="str" value="$(find-pkg-share tensorrt_yolo)/data/$(var yolo_type).cache"/>
+    <param name="calib_cache_file" type="str" value="$(var data_path)/tensorrt_yolo/$(var yolo_type).cache"/>
     <param name="mode" type="str" value="$(var mode)"/>
     <param name="gpu_id" type="int" value="$(var gpu_id)"/>
     <param from="$(find-pkg-share tensorrt_yolo)/config/$(var yolo_type).param.yaml"/>

perception/tensorrt_yolox/README.md

@@ -71,7 +71,7 @@ those are labeled as `UNKNOWN`, while detected rectangles are drawn in the visua
 
 ## Onnx model
 
-A sample model (named `yolox-tiny.onnx`) is downloaded automatically during the build process.
+A sample model (named `yolox-tiny.onnx`) is downloaded by ansible script on env preparation stage, if not, please, follow [Manual downloading of artifacts](https://github.com/autowarefoundation/autoware/tree/main/ansible/roles/artifacts).
 To accelerate Non-maximum-suppression (NMS), which is one of the common post-process after object detection inference,
 `EfficientNMS_TRT` module is attached after the ordinal YOLOX (tiny) network.
 The `EfficientNMS_TRT` module contains fixed values for `score_threshold` and `nms_threshold` in it,
@@ -146,7 +146,7 @@ Please refer [the official document](https://github.com/Megvii-BaseDetection/YOL
 
 ## Label file
 
-A sample label file (named `label.txt`)is also downloaded automatically during the build process
+A sample label file (named `label.txt`)is also downloaded automatically during env preparation process
 (**NOTE:** This file is incompatible with models that output labels for the COCO dataset (e.g., models from the official YOLOX repository)).
 
 This file represents the correspondence between class index (integer outputted from YOLOX network) and

perception/tensorrt_yolox/launch/yolox_s_plus_opt.launch.xml

@@ -4,7 +4,8 @@
   <arg name="input/image" default="/sensing/camera/camera0/image_rect_color"/>
   <arg name="output/objects" default="/perception/object_recognition/detection/rois0"/>
   <arg name="model_name" default="yolox-sPlus-T4-960x960-pseudo-finetune"/>
-  <arg name="model_path" default="$(find-pkg-share tensorrt_yolox)/data"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="model_path" default="$(var data_path)/tensorrt_yolox"/>
   <arg name="score_threshold" default="0.35"/>
   <arg name="nms_threshold" default="0.7"/>
   <arg name="precision" default="int8" description="operation precision to be used on inference. Valid value is one of: [fp32, fp16, int8]"/>

perception/tensorrt_yolox/launch/yolox_tiny.launch.xml

@@ -3,7 +3,8 @@
   <arg name="input/image" default="/sensing/camera/camera0/image_rect_color"/>
   <arg name="output/objects" default="/perception/object_recognition/detection/rois0"/>
   <arg name="model_name" default="yolox-tiny"/>
-  <arg name="model_path" default="$(find-pkg-share tensorrt_yolox)/data"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="model_path" default="$(var data_path)/tensorrt_yolox"/>
   <arg name="score_threshold" default="0.35"/>
   <arg name="nms_threshold" default="0.7"/>
   <arg name="precision" default="fp16" description="operation precision to be used on inference. Valid value is one of: [fp32, fp16, int8]"/>

perception/traffic_light_classifier/README.md

@@ -55,6 +55,7 @@
 | Name              | Type | Description                                 |
 | ----------------- | ---- | ------------------------------------------- |
 | `classifier_type` | int  | if the value is `1`, cnn_classifier is used |
+| `data_path`       | str  | packages data and artifacts directory path  |
 
 ### Core Parameters
 
@@ -147,7 +148,7 @@
 
 #### Prerequisites
 
 **Step 1.** Download and install Miniconda from the [official website](https://mmpretrain.readthedocs.io/en/latest/get_started.html).
 
 **Step 2.** Create a conda virtual environment and activate it
 
@@ -164,9 +165,9 @@
 conda install pytorch==1.13.1 torchvision==0.14.1 pytorch-cuda=11.6 -c pytorch -c nvidia
 ```
 
 #### Install mmlab/mmpretrain
 
 **Step 1.** Install mmpretrain from source
 
 ```bash
 cd ~/
@@ -177,7 +178,7 @@
 
 ### Training
 
 MMPretrain offers a training script that is controlled through a configuration file.
 Leveraging an inheritance design pattern, you can effortlessly tailor the training script
 using Python files as configuration files.
 
@@ -292,13 +293,13 @@
 python tools/train.py configs/mobilenet_v2/mobilenet-v2_8xb32_custom.py
 ```
 
 Training logs and weights will be saved in the `work_dirs/mobilenet-v2_8xb32_custom` folder.
 
 ### Convert PyTorh model to ONNX model
 
 #### Install mmdeploy
 
 The 'mmdeploy' toolset is designed for deploying your trained model onto various target devices.
 With its capabilities, you can seamlessly convert PyTorch models into the ONNX format.
 
 ```bash
@@ -338,7 +339,7 @@
 --work-dir mmdeploy_model/mobilenet_v2
 ```
 
 Converted ONNX model will be saved in the `mmdeploy/mmdeploy_model/mobilenet_v2` folder.
 
 After obtaining your onnx model, update parameters defined in the launch file (e.g. `model_file_path`, `label_file_path`, `input_h`, `input_w`...).
 Note that, we only support labels defined in [tier4_perception_msgs::msg::TrafficLightElement](https://github.com/tier4/tier4_autoware_msgs/blob/tier4/universe/tier4_perception_msgs/msg/traffic_light/TrafficLightElement.msg).

perception/traffic_light_classifier/launch/traffic_light_classifier.launch.xml

@@ -2,8 +2,9 @@
   <arg name="input/image" default="~/image_raw"/>
   <arg name="input/rois" default="~/rois"/>
   <arg name="output/traffic_signals" default="classified/traffic_signals"/>
-  <arg name="classifier_label_path" default="$(find-pkg-share traffic_light_classifier)/data/lamp_labels.txt" description="classifier label path"/>
-  <arg name="classifier_model_path" default="$(find-pkg-share traffic_light_classifier)/data/traffic_light_classifier_mobilenetv2_batch_6.onnx" description="classifier onnx model path"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="classifier_label_path" default="$(var data_path)/traffic_light_classifier/lamp_labels.txt" description="classifier label path"/>
+  <arg name="classifier_model_path" default="$(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx" description="classifier onnx model path"/>
   <arg name="classifier_precision" default="fp16"/>
 
   <arg name="use_gpu" default="true"/>

perception/traffic_light_fine_detector/README.md

@@ -50,12 +50,13 @@ Based on the camera image and the global ROI array detected by `map_based_detect
 
 ### Node Parameters
 
-| Name                       | Type   | Default Value | Description                                                        |
-| -------------------------- | ------ | ------------- | ------------------------------------------------------------------ |
-| `fine_detector_model_path` | string | ""            | The onnx file name for yolo model                                  |
-| `fine_detector_label_path` | string | ""            | The label file with label names for detected objects written on it |
-| `fine_detector_precision`  | string | "fp32"        | The inference mode: "fp32", "fp16"                                 |
-| `approximate_sync`         | bool   | false         | Flag for whether to ues approximate sync policy                    |
+| Name                       | Type   | Default Value               | Description                                                        |
+| -------------------------- | ------ | --------------------------- | ------------------------------------------------------------------ |
+| `data_path`                | string | "$(env HOME)/autoware_data" | packages data and artifacts directory path                         |
+| `fine_detector_model_path` | string | ""                          | The onnx file name for yolo model                                  |
+| `fine_detector_label_path` | string | ""                          | The label file with label names for detected objects written on it |
+| `fine_detector_precision`  | string | "fp32"                      | The inference mode: "fp32", "fp16"                                 |
+| `approximate_sync`         | bool   | false                       | Flag for whether to ues approximate sync policy                    |
 
 ## Assumptions / Known limits
 

perception/traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml

@@ -1,6 +1,7 @@
 <launch>
-  <arg name="fine_detector_label_path" default="$(find-pkg-share traffic_light_fine_detector)/data/tlr_labels.txt" description="fine detector label path"/>
-  <arg name="fine_detector_model_path" default="$(find-pkg-share traffic_light_fine_detector)/data/tlr_yolox_s_batch_6.onnx" description="fine detector onnx model path"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="fine_detector_label_path" default="$(var data_path)/traffic_light_fine_detector/tlr_labels.txt" description="fine detector label path"/>
+  <arg name="fine_detector_model_path" default="$(var data_path)/traffic_light_fine_detector/tlr_yolox_s_batch_6.onnx" description="fine detector onnx model path"/>
   <arg name="fine_detector_precision" default="fp16"/>
   <arg name="fine_detector_score_thresh" default="0.3"/>
   <arg name="fine_detector_nms_thresh" default="0.65"/>