Merge pull request #5 from NVIDIA-ISAAC-ROS/release-dp

Isaac ROS 0.10.0 (DP)

.gitattributes

@@ -3,22 +3,24 @@
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
 *.psd filter=lfs diff=lfs merge=lfs -text
+
 # Archives
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.tar filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
+
 # Documents
 *.pdf filter=lfs diff=lfs merge=lfs -text
-# Numpy data
-*.npy filter=lfs diff=lfs merge=lfs -text
-# Debian package
-*.deb filter=lfs diff=lfs merge=lfs -text
+
 # Shared libraries
 *.so filter=lfs diff=lfs merge=lfs -text
 *.so.* filter=lfs diff=lfs merge=lfs -text
-# PCD files
-*.pcd filter=lfs diff=lfs merge=lfs -text
-# Model files
+
+# ROS Bags
+**/resources/**/*.db3 filter=lfs diff=lfs merge=lfs -text
+**/resources/**/*.yaml filter=lfs diff=lfs merge=lfs -text
+
+# DNN Model files
 *.onnx filter=lfs diff=lfs merge=lfs -text
 .trt filter=lfs diff=lfs merge=lfs -text
 *.trt filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,6 +1,6 @@
 # Ignore all pycache files
 **/__pycache__/**
-build/
-install/
-install_aarch64/
-log/
+
+# Ignore TensorRT plan files
+*.plan
+*.engine

docs/training-detectnet-model-in-sim.md

@@ -0,0 +1,54 @@
+## Training a model using simulation
+
+There are multiple ways to train your own `Detectnet_v2` base model. Note that you will need to update parameters, launch files, and more to match your specific trained model.
+
+### Use the TAO toolkit launcher
+The `Train and Optimize` tookit from NVIDIA has all the tools you need to prepare a dataset and re-train a detector with an easy to follow Jupyter notebook tutorial.
+
+1. Install the `tao` command line utilities
+   ```bash
+   pip3 install jupyterlab nvidia-pyindex nvidia-tao
+   ```
+2. Obtain an [NGC API key](https://ngc.nvidia.com/setup/api-key).
+3. Install and configure `ngc cli` from [NVIDIA NGC CLI Setup](https://ngc.nvidia.com/setup/installers/cli).
+   ```bash
+     wget -O ngccli_linux.zip https://ngc.nvidia.com/downloads/ngccli_linux.zip && unzip -o ngccli_linux.zip && chmod u+x ngc && \
+     md5sum -c ngc.md5 && \
+     echo "export PATH=\"\$PATH:$(pwd)\"" >> ~/.bash_profile && source ~/.bash_profile && \
+     ngc config set
+   ```
+4. Download the TAO cv examples to a local folder
+   ```bash
+     ngc registry resource download-version "nvidia/tao/cv_samples:v1.3.0"
+   ```
+5. Run the `DetectNet_v2` Jupyter notebook server.
+   ```bash
+     cd cv_samples_vv1.3.0 && jupyter-notebook --ip 0.0.0.0 --port 8888 --allow-root
+   ```
+6. Navigate to the DetectNet v2 notebook in `detectnet_v2/detectnet_v2.ipynb` or go to
+   ```
+     http://0.0.0.0:8888/notebooks/detectnet_v2/detectnet_v2.ipynb
+   ```
+   And follow the instructions on the tutorial.
+
+### Training object detection in simulation
+
+If you wish to generate training data from simulation using 3D models of the object classes you would like to detect, consider following the tutorial [Training Object detection from Simulation](https://docs.nvidia.com/isaac/doc/tutorials/training_in_docker.html).
+
+The tutorial will use simulation to create a dataset that can then be used to train a `DetectNet_v2` based model. It's an easy to use tool with full access to customize training parameters in a Jupyter notebook.
+
+Once you follow through the tutorial, you should have an `ETLT` file in `~/isaac-experiments/tlt-experiments/experiment_dir_final/resnet18_detector.etlt`.
+
+Consult the spec file in `~/isaac-experiments/specs/isaac_detect_net_inference.json` for the values to use in the following section when preparing the model for usage with this package.
+
+### Using the included dummy model for testing
+
+In this package, you will find a pre-trained DetectNet model that was trained solely for detecting tennis balls using the described simulation method. Please use this model only for verification or exploring the pipeline.
+
+> **Note**: Do not use this tennis ball detection model in a production environment.
+
+You can find the `ETLT` file in `isaac_ros_detectnet/test/dummy_model/detectnet/1/resnet18_detector.etlt` and use the ETLT key `"object-detection-from-sim-pipeline"`, including the double quotes.
+
+```bash
+export PRETRAINED_MODEL_ETLT_KEY=\"object-detection-from-sim-pipeline\"
+```

isaac_ros_detectnet/CMakeLists.txt

@@ -32,9 +32,9 @@ message( STATUS "Architecture: ${ARCHITECTURE}" )
 
 add_library(nvdsinfer_dbscan SHARED IMPORTED)
 if( ${ARCHITECTURE} STREQUAL "x86_64" )
-  set_property(TARGET nvdsinfer_dbscan PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/dbscan/x86_64_cuda_11_4/libnvds_dbscan.so)
+  set_property(TARGET nvdsinfer_dbscan PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/dbscan/x86_64/libnvds_dbscan.so)
 elseif( ${ARCHITECTURE} STREQUAL "aarch64" )
-  set_property(TARGET nvdsinfer_dbscan PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/dbscan/jetpack46_1/libnvds_dbscan.so)
+  set_property(TARGET nvdsinfer_dbscan PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/dbscan/jetpack50/libnvds_dbscan.so)
 endif()
 
 
@@ -45,8 +45,8 @@ target_compile_definitions(detectnet_decoder_node
 )
 target_link_libraries(detectnet_decoder_node nvdsinfer_dbscan)
 
-rclcpp_components_register_nodes(detectnet_decoder_node "isaac_ros::detectnet::DetectNetDecoderNode")
-set(node_plugins "${node_plugins}isaac_ros::detectnet::DetectNetDecoderNode;$<TARGET_FILE:detectnet_decoder_node>\n")
+rclcpp_components_register_nodes(detectnet_decoder_node "nvidia::isaac_ros::detectnet::DetectNetDecoderNode")
+set(node_plugins "${node_plugins}nvidia::isaac_ros::detectnet::DetectNetDecoderNode;$<TARGET_FILE:detectnet_decoder_node>\n")
 
 install(TARGETS detectnet_decoder_node
   ARCHIVE DESTINATION lib
@@ -75,6 +75,11 @@ install(PROGRAMS
   DESTINATION lib/${PROJECT_NAME}
 )
 
+install(DIRECTORY
+  resources/rosbags/detectnet_rosbag
+  DESTINATION share/${PROJECT_NAME}
+)
+
 ament_auto_package(INSTALL_TO_SHARE launch)
 
 find_package(vision_msgs REQUIRED)

isaac_ros_detectnet/include/isaac_ros_detectnet/detectnet_decoder_node.hpp

@@ -1,5 +1,5 @@
 /**
- * Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.
  *
  * NVIDIA CORPORATION and its licensors retain all intellectual property
  * and proprietary rights in and to this software, related documentation
@@ -15,10 +15,12 @@
 #include <string>
 #include <vector>
 
-#include "isaac_ros_nvengine_interfaces/msg/tensor_list.hpp"
+#include "isaac_ros_tensor_list_interfaces/msg/tensor_list.hpp"
 #include "vision_msgs/msg/detection2_d_array.hpp"
 #include "rclcpp/rclcpp.hpp"
 
+namespace nvidia
+{
 namespace isaac_ros
 {
 namespace detectnet
@@ -38,13 +40,11 @@ class DetectNetDecoderNode : public rclcpp::Node
  * @param tensor_list_msg The TensorList msg representing the detection list output by DetectNet
  */
   void DetectNetDecoderCallback(
-    const isaac_ros_nvengine_interfaces::msg::TensorList::ConstSharedPtr tensor_list_msg);
+    const isaac_ros_tensor_list_interfaces::msg::TensorList::ConstSharedPtr tensor_list_msg);
 
   // Queue size of subscriber
   int queue_size_;
 
-  // Frame id that the message should be in
-  std::string header_frame_id_;
   // A list of class labels in the order they are used in the model
   std::vector<std::string> label_names_;
   // coverage threshold to discard detections.
@@ -65,7 +65,8 @@ class DetectNetDecoderNode : public rclcpp::Node
   int clustering_algorithm_;
 
   // Subscribes to a Tensor that will be converted to a detection list
-  rclcpp::Subscription<isaac_ros_nvengine_interfaces::msg::TensorList>::SharedPtr tensor_list_sub_;
+  rclcpp::Subscription<isaac_ros_tensor_list_interfaces::msg::TensorList>::SharedPtr
+    tensor_list_sub_;
 
   // Publishes the processed Tensor as an array of detections (Detection2DArray)
   rclcpp::Publisher<vision_msgs::msg::Detection2DArray>::SharedPtr detections_pub_;
@@ -75,5 +76,6 @@ class DetectNetDecoderNode : public rclcpp::Node
 
 }  // namespace detectnet
 }  // namespace isaac_ros
+}  // namespace nvidia
 
 #endif  // ISAAC_ROS_DETECTNET__DETECTNET_DECODER_NODE_HPP_

isaac_ros_detectnet/launch/isaac_ros_detectnet.launch.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.
 #
 # NVIDIA CORPORATION and its licensors retain all intellectual property
 # and proprietary rights in and to this software, related documentation
@@ -21,43 +21,42 @@ def generate_launch_description():
     encoder_node = ComposableNode(
         name='dnn_image_encoder',
         package='isaac_ros_dnn_encoders',
-        plugin='isaac_ros::dnn_inference::DnnImageEncoderNode',
+        plugin='nvidia::isaac_ros::dnn_inference::DnnImageEncoderNode',
         parameters=[{
             'network_image_width': 640,
-            'network_image_height': 368,
-            'network_image_encoding': 'rgb8',
-            'network_normalization_type': 'positive_negative',
-            'tensor_name': 'input_tensor'
+            'network_image_height': 368
         }],
         remappings=[('encoded_tensor', 'tensor_pub')]
     )
 
     triton_node = ComposableNode(
         name='triton_node',
         package='isaac_ros_triton',
-        plugin='isaac_ros::dnn_inference::TritonNode',
+        plugin='nvidia::isaac_ros::dnn_inference::TritonNode',
         parameters=[{
             'model_name': 'detectnet',
             'model_repository_paths': ['/tmp/models'],
             'input_tensor_names': ['input_tensor'],
             'input_binding_names': ['input_1'],
+            'input_tensor_formats': ['nitros_tensor_list_nchw_rgb_f32'],
             'output_tensor_names': ['output_cov', 'output_bbox'],
             'output_binding_names': ['output_cov/Sigmoid', 'output_bbox/BiasAdd'],
+            'output_tensor_formats': ['nitros_tensor_list_nhwc_rgb_f32'],
             'log_level': 0
         }])
 
     detectnet_decoder_node = ComposableNode(
         name='detectnet_decoder_node',
         package='isaac_ros_detectnet',
-        plugin='isaac_ros::detectnet::DetectNetDecoderNode',
+        plugin='nvidia::isaac_ros::detectnet::DetectNetDecoderNode',
         parameters=[config]
     )
 
     container = ComposableNodeContainer(
         name='detectnet_container',
         namespace='detectnet_container',
         package='rclcpp_components',
-        executable='component_container',
+        executable='component_container_mt',
         composable_node_descriptions=[encoder_node, triton_node, detectnet_decoder_node],
         output='screen'
     )

isaac_ros_detectnet/launch/isaac_ros_detectnet_demo.launch.py

@@ -0,0 +1,41 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+import os
+
+from ament_index_python.packages import get_package_share_directory
+from launch import actions, LaunchDescription
+from launch.actions import IncludeLaunchDescription
+from launch.launch_description_sources import PythonLaunchDescriptionSource
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    my_package_dir = get_package_share_directory('isaac_ros_detectnet')
+    return LaunchDescription([
+        actions.ExecuteProcess(
+            cmd=['ros2', 'bag', 'play', '-l',
+                 os.path.join(my_package_dir, 'detectnet_rosbag')]
+        ),
+        IncludeLaunchDescription(
+            PythonLaunchDescriptionSource([os.path.join(
+                my_package_dir, 'launch'),
+                '/isaac_ros_detectnet.launch.py'])
+        ),
+        Node(
+            package='isaac_ros_detectnet',
+            executable='isaac_ros_detectnet_visualizer.py',
+            name='detectnet_visualizer'
+        ),
+        Node(
+            package='rqt_image_view',
+            executable='rqt_image_view',
+            name='image_view',
+            arguments=['/detectnet_processed_image']
+        )
+    ])

isaac_ros_detectnet/package.xml

@@ -20,6 +20,8 @@ license agreement from NVIDIA CORPORATION is strictly prohibited.
   <license>NVIDIA Isaac ROS Software License</license>
   <url type="website">https://developer.nvidia.com/isaac-ros-gems/</url>
   <author>Herón Ordóñez Guillén</author>
+  <author>Jay Monga</author>
+
   <buildtool_depend>ament_cmake</buildtool_depend>
   <buildtool_depend>ament_cmake_python</buildtool_depend>
 
@@ -30,14 +32,12 @@ license agreement from NVIDIA CORPORATION is strictly prohibited.
   <depend>rclcpp_components</depend>
   <depend>vision_msgs</depend>
   <depend>isaac_ros_dnn_encoders</depend>
-  <depend>isaac_ros_nvengine_interfaces</depend>
+  <depend>isaac_ros_tensor_list_interfaces</depend>
 
   <test_depend>ament_lint_auto</test_depend>
   <test_depend>ament_lint_common</test_depend>
   <test_depend>isaac_ros_test</test_depend>
-  <test_depend>isaac_ros_nvengine</test_depend>
 
-  <exec_depend>isaac_ros_nvengine</exec_depend>
   <exec_depend>isaac_ros_triton</exec_depend>
 
   <export>

isaac_ros_detectnet/resources/detectnet_sample_config.pbtxt

@@ -0,0 +1,30 @@
+name: "detectnet"
+platform: "tensorrt_plan"
+max_batch_size: 16
+input [
+  {
+    name: "input_1"
+    data_type: TYPE_FP32
+    format: FORMAT_NCHW
+    dims: [ 3, 368, 640 ]
+  }
+]
+output [
+    {
+        name: "output_bbox/BiasAdd"
+        data_type: TYPE_FP32
+        dims: [ 8, 23, 40 ]
+    },
+    {
+        name: "output_cov/Sigmoid"
+        data_type: TYPE_FP32
+        dims: [ 2, 23, 40 ]
+    }
+]
+dynamic_batching { }
+version_policy: {
+  specific {
+    versions: [ 1 ]
+  }
+}
+