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launcher. fix launcher to the autoware 2024.07 version #16

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Sep 4, 2024
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launcher. fix launcher to the autoware 2024.07 version #16

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89f3285
launcher. fix launcher to the autoware 2024.07 version
amc-nu Jul 30, 2024
d175dcf
package.xml. use new autoware package names
a-yyg Aug 28, 2024
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3 changes: 3 additions & 0 deletions edge_auto_jetson_launch/config/bytetrack.param.yaml
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/**:
ros__parameters:
track_buffer_length: 30
3 changes: 3 additions & 0 deletions edge_auto_jetson_launch/config/bytetrack_visualizer.param.yaml
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/**:
ros__parameters:
use_raw: true
39 changes: 39 additions & 0 deletions edge_auto_jetson_launch/config/yolox_tiny.param.yaml
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# cspell:ignore semseg
/**:
ros__parameters:

# refine segmentation mask by overlay roi class
# disable when sematic segmentation accuracy is good enough
is_roi_overlap_segment: true

# minimum existence_probability of detected roi considered to replace segmentation
overlap_roi_score_threshold: 0.3

# publish color mask for result visualization
is_publish_color_mask: false

roi_overlay_segment_label:
UNKNOWN : true
CAR : false
TRUCK : false
BUS : false
MOTORCYCLE : true
BICYCLE : true
PEDESTRIAN : true
ANIMAL: true

model_path: "$(var data_path)/tensorrt_yolox/$(var model_name).onnx" # The onnx file name for YOLOX model.
label_path: "$(var data_path)/tensorrt_yolox/label.txt" # The label file path for YOLOX model.
color_map_path: "$(var data_path)/tensorrt_yolox/semseg_color_map.csv"
score_threshold: 0.35 # Objects with a score lower than this value will be ignored. This threshold will be ignored if specified model contains EfficientNMS_TRT module in it.
nms_threshold: 0.7 # Detection results will be ignored if IoU over this value. This threshold will be ignored if specified model contains EfficientNMS_TRT module in it.

precision: "fp16" # Operation precision to be used on inference. Valid value is one of: [fp32, fp16, int8].
calibration_algorithm: "MinMax" # Calibration algorithm to be used for quantization when precision==int8. Valid value is one of: [Entropy, (Legacy | Percentile), MinMax].
dla_core_id: -1 # If positive ID value is specified, the node assign inference task to the DLA core.
quantize_first_layer: false # If true, set the operating precision for the first (input) layer to be fp16. This option is valid only when precision==int8.
quantize_last_layer: false # If true, set the operating precision for the last (output) layer to be fp16. This option is valid only when precision==int8.
profile_per_layer: false # If true, profiler function will be enabled. Since the profile function may affect execution speed, it is recommended to set this flag true only for development purpose.
clip_value: 0.0 # If positive value is specified, the value of each layer output will be clipped between [0.0, clip_value]. This option is valid only when precision==int8 and used to manually specify the dynamic range instead of using any calibration.
preprocess_on_gpu: true # If true, pre-processing is performed on GPU.
calibration_image_list_path: "" # Path to a file which contains path to images. Those images will be used for int8 quantization.
73 changes: 19 additions & 54 deletions edge_auto_jetson_launch/launch/bytetrack.launch.xml
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<launch>
<!-- input topic name to be subscribed (bbox) -->
<arg name="input/objects" default="/perception/object_recognition/detection/rois0" />
<!-- input topic name to be subscribed (image for visualizer) -->
<arg name="input/image" default="image_raw" />
<!-- flag to describe whether input topic is raw or compressed -->
<arg name="input/image/is_raw" default="True" />
<!-- output topic name to be published (bbox) -->
<arg name="output/objects" default="/perception/object_recognition/detection/tracked/rois0" />
<!-- container naem that this ROS node to be loaded -->
<arg name="container_name" default="" />
<!-- flag to use ROS2 intra process -->
<arg name="use_intra_process" default="True" />
<!-- flag to enable bytetrack visualization -->
<arg name="enable_bytetrack_visualizer" default="True" />
<arg name="input/image" default="/sensing/camera/camera0/image_rect_color"/>
<arg name="in_image_compressed" default="$(var input/image)/compressed"/>
<arg name="input/objects" default="/perception/object_recognition/detection/rois0"/>
<arg name="output/objects" default="/perception/object_recognition/detection/tracked/rois0"/>
<arg name="bytetrack_param_path" default="$(find-pkg-share edge_auto_jetson_launch)/config/bytetrack.param.yaml"/>
<arg name="bytetrack_visualizer_param_path" default="$(find-pkg-share edge_auto_jetson_launch)/config/bytetrack_visualizer.param.yaml"/>
<arg name="enable_visualizer" default="true"/>
<arg name="camera_id" default="0" />

<!-- algorithm parameters -->
<arg name="track_buffer_length" default="30"
description="The frame count length that a tracklet is considered to be valid" />

<let name="empty_container_is_specified" value="$(eval 'not &quot;$(var container_name)&quot;')" />
<!-- If container name is not specified,
execute function as an individual node -->
<group if="$(var empty_container_is_specified)">
<node pkg="bytetrack" exec="bytetrack_node_exe" name="bytetrack">
<remap from="~/in/rect" to="$(var input/objects)"/>
<remap from="~/out/objects" to="$(var output/objects)"/>
<remap from="~/out/objects/debug/uuid" to="$(var output/objects)/debug/uuid"/>
<param name="track_buffer_length" value="$(var track_buffer_length)"/>
</node>
</group>

<!-- If container name is specified,
execute function as a composable node and load it into the container -->
<group unless="$(var empty_container_is_specified)">
<load_composable_node target="$(var container_name)">
<composable_node pkg="bytetrack" plugin="bytetrack::ByteTrackNode" name="bytetrack">
<remap from="~/in/rect" to="$(var input/objects)"/>
<remap from="~/out/objects" to="$(var output/objects)"/>
<remap from="~/out/objects/debug/uuid" to="$(var output/objects)/debug/uuid"/>
<param name="track_buffer_length" value="$(var track_buffer_length)"/>
<extra_arg name="use_intra_process_comms" value="$(var use_intra_process)"/>
</composable_node>
</load_composable_node>
</group>
<node pkg="autoware_bytetrack" exec="bytetrack_node_exe" output="screen" name="bytetrack_node_exe_$(var camera_id)">
<remap from="~/in/rect" to="$(var input/objects)"/>
<remap from="~/out/objects" to="$(var output/objects)"/>
<remap from="~/out/objects/debug/uuid" to="$(var output/objects)/debug/uuid"/>
<param from="$(var bytetrack_param_path)"/>
</node>

<!-- Execute visualizer as a separated node -->
<node pkg="bytetrack" exec="bytetrack_visualizer_node_exe"
name="bytetrack_visualizer" if="$(var enable_bytetrack_visualizer)">
<remap from="~/in/image" to="$(var input/image)" />
<remap from="~/in/rect" to="$(var output/objects)" />
<remap from="~/in/uuid" to="$(var output/objects)/debug/uuid" />
<remap from="~/out/image" to="$(var output/objects)/debug/image" />
<remap from="~/out/image/compressed" to="$(var output/objects)/debug/image/compressed" />
<remap from="~/out/image/compressedDepth" to="$(var output/objects)/debug/image/compressedDepth" />
<remap from="~/out/image/theora" to="$(var output/objects)/debug/image/theora" />
<param name="use_raw" value="$(var input/image/is_raw)" />
<node pkg="autoware_bytetrack" exec="bytetrack_visualizer_node_exe" output="screen" if="$(var enable_visualizer)" name="bytetrack_visualizer_node_exe_$(var camera_id)">
<remap from="~/in/image" to="$(var input/image)"/>
<remap from="~/in/rect" to="$(var output/objects)"/>
<remap from="~/in/uuid" to="$(var output/objects)/debug/uuid"/>
<param from="$(var bytetrack_visualizer_param_path)"/>
</node>

</launch>
4 changes: 4 additions & 0 deletions edge_auto_jetson_launch/launch/perception_jetson0.launch.xml
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<arg name="container_name" value="$(var container_name)" />
<arg name="input/image" value="/sensing/camera/$(var camera_name)/image_raw" />
<arg name="output/objects" value="tensorrt_yolox/rois$(var camera_id)" />
<arg name="camera_id" value="$(var camera_id)" />
</include>

<include file="$(find-pkg-share edge_auto_jetson_launch)/launch/bytetrack.launch.xml">
Expand All @@ -52,6 +53,7 @@
<arg name="input/image" value="/sensing/camera/$(var camera_name)/image_raw" />
<arg name="input/image/is_raw" value="True" />
<arg name="output/objects" value="/perception/object_recognition/detection/rois$(var camera_id)" />
<arg name="camera_id" value="$(var camera_id)" />
</include>
</group>
</group> <!-- camera0 -->
Expand Down Expand Up @@ -95,12 +97,14 @@
<push-ros-namespace namespace="perception/object_recognition/detection" />
<include file="$(find-pkg-share edge_auto_jetson_launch)/launch/tensorrt_yolox.launch.xml">
<arg name="container_name" value="$(var container_name)" />
<arg name="camera_id" value="$(var camera_id)" />
<arg name="input/image" value="/sensing/camera/$(var camera_name)/image_raw" />
<arg name="output/objects" value="tensorrt_yolox/rois$(var camera_id)" />
</include>

<include file="$(find-pkg-share edge_auto_jetson_launch)/launch/bytetrack.launch.xml">
<arg name="container_name" value="$(var container_name)" />
<arg name="camera_id" value="$(var camera_id)" />
<arg name="input/objects" value="tensorrt_yolox/rois$(var camera_id)" />
<arg name="input/image" value="/sensing/camera/$(var camera_name)/image_raw" />
<arg name="input/image/is_raw" value="True" />
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104 changes: 15 additions & 89 deletions edge_auto_jetson_launch/launch/tensorrt_yolox.launch.xml
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<launch>
<!-- image topic name to be subscribed -->
<arg name="input/image" default="image_raw" />
<!-- output topic name to be published (bbox) -->
<arg name="output/objects" default="/perception/object_recognition/detection/rois0" />
<!-- path to the YOLOX model to be loaded -->
<arg name="model_path" default="$(find-pkg-share tensorrt_yolox)/data/yolox-tiny.onnx" />
<!-- path to the label file to explain category ID and string -->
<arg name="label_path" default="$(find-pkg-share tensorrt_yolox)/data/label.txt" />
<!-- container naem that this ROS node to be loaded -->
<arg name="container_name" default="" />
<!-- flag to use ROS2 intra process -->
<arg name="use_intra_process" default="True" />

<!-- algorithm parameters -->
<arg name="score_threshold" default="0.35" />
<arg name="nms_threshold" default="0.7" />
<arg name="precision" default="fp32"
description="operation precision to be used on inference.Valid value is one of: [fp32, fp16, int8]" />
<arg name="calibration_algorithm"
default="MinMax"
description="Calibration algorithm to be used for quantization when precision==int8. Valid value is one of: [Entropy, (Legacy | Percentile), MinMax]" />
<arg name="dla_core_id" default="-1"
description="If positive ID value is specified, the node assign inference task to the DLA core" />
<arg name="quantize_first_layer" default="false"
description="If true, set the operating precision for the first (input) layer to be fp16. This option is valid only when precision==int8" />
<arg name="quantize_last_layer" default="false"
description="If true, set the operating precision for the last (output) layer to be fp16. This option is valid only when precision==int8" />
<arg name="profile_per_layer" default="false"
description="If true, profiler function will be enabled. Since the profile function may affect execution speed, it is recommended to set this flag true only for development purpose." />
<arg name="clip_value" default="0.0"
description="If positive value is specified, the value of each layer output will be clipped between [0.0, clip_value]. This option is valid only when precision==int8 and used to manually specify the dynamic range instead of using any calibration." />
<arg name="preprocess_on_gpu" default="true" description="If true, pre-processing is performed on GPU" />
<arg name="calibration_image_list_path" default=""
description="Path to a file which contains path to images. Those images will be used for int8 quantization." />

<let name="empty_container_is_specified" value="$(eval 'not &quot;$(var container_name)&quot;')" />
<!-- If container name is not specified,
execute function as an individual node -->
<group if="$(var empty_container_is_specified)">
<node pkg="tensorrt_yolox" exec="tensorrt_yolox_node_exe" name="tensorrt_yolox">
<remap from="~/in/image" to="$(var input/image)" />
<remap from="~/out/objects" to="$(var output/objects)" />
<remap from="~/out/image" to="$(var output/objects)/debug/image" />
<remap from="~/out/image/compressed" to="$(var output/objects)/debug/image/compressed" />
<remap from="~/out/image/compressedDepth" to="$(var output/objects)/debug/image/compressedDepth" />
<remap from="~/out/image/theora" to="$(var output/objects)/debug/image/theora" />
<param name="score_threshold" value="$(var score_threshold)" />
<param name="nms_threshold" value="$(var nms_threshold)" />
<param name="model_path" value="$(var model_path)" />
<param name="label_path" value="$(var label_path)" />
<param name="precision" value="$(var precision)" />
<param name="calibration_algorithm" value="$(var calibration_algorithm)" />
<param name="dla_core_id" value="$(var dla_core_id)" />
<param name="quantize_first_layer" value="$(var quantize_first_layer)" />
<param name="quantize_last_layer" value="$(var quantize_last_layer)" />
<param name="profile_per_layer" value="$(var profile_per_layer)" />
<param name="clip_value" value="$(var clip_value)" />
<param name="preprocess_on_gpu" value="$(var preprocess_on_gpu)" />
<param name="calibration_image_list_path" value="$(var calibration_image_list_path)" />
</node>
</group>

<!-- If container name is specified,
execute function as a composable node and load it into the container -->
<group unless="$(var empty_container_is_specified)">
<load_composable_node target="$(var container_name)">
<composable_node pkg="tensorrt_yolox" plugin="tensorrt_yolox::TrtYoloXNode" name="tensorrt_yolox">
<remap from="~/in/image" to="$(var input/image)" />
<remap from="~/out/objects" to="$(var output/objects)" />
<remap from="~/out/image" to="$(var output/objects)/debug/image" />
<remap from="~/out/image/compressed" to="$(var output/objects)/debug/image/compressed" />
<remap from="~/out/image/compressedDepth" to="$(var output/objects)/debug/image/compressedDepth" />
<remap from="~/out/image/theora" to="$(var output/objects)/debug/image/theora" />
<param name="score_threshold" value="$(var score_threshold)" />
<param name="nms_threshold" value="$(var nms_threshold)" />
<param name="model_path" value="$(var model_path)" />
<param name="label_path" value="$(var label_path)" />
<param name="precision" value="$(var precision)" />
<param name="calibration_algorithm" value="$(var calibration_algorithm)" />
<param name="dla_core_id" value="$(var dla_core_id)" />
<param name="quantize_first_layer" value="$(var quantize_first_layer)" />
<param name="quantize_last_layer" value="$(var quantize_last_layer)" />
<param name="profile_per_layer" value="$(var profile_per_layer)" />
<param name="clip_value" value="$(var clip_value)" />
<param name="preprocess_on_gpu" value="$(var preprocess_on_gpu)" />
<param name="calibration_image_list_path" value="$(var calibration_image_list_path)" />
<extra_arg name="use_intra_process_comms" value="$(var use_intra_process)" />
</composable_node>
</load_composable_node>
</group>
<arg name="input/image" default="image_raw"/>
<arg name="output/objects" default="/perception/object_recognition/detection/rois0"/>
<arg name="model_name" default="yolox-sPlus-opt"/>
<arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
<arg name="yolox_param_path" default="$(find-pkg-share edge_auto_jetson_launch)/config/yolox_tiny.param.yaml"/>
<arg name="use_decompress" default="true" description="use image decompress"/>
<arg name="build_only" default="false" description="exit after trt engine is built"/>
<arg name="camera_id" default="0" />

<node pkg="autoware_tensorrt_yolox" exec="autoware_tensorrt_yolox_node_exe" name="tensorrt_yolox_$(var camera_id)" output="screen">
<remap from="~/in/image" to="$(var input/image)"/>
<remap from="~/out/objects" to="$(var output/objects)"/>
<param from="$(var yolox_param_path)" allow_substs="true"/>
<param name="build_only" value="$(var build_only)"/>
</node>

</launch>
6 changes: 3 additions & 3 deletions edge_auto_jetson_launch/package.xml
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Expand Up @@ -15,10 +15,10 @@
<exec_depend>image_view</exec_depend>
<exec_depend>image_transport_plugins</exec_depend>
<exec_depend>individual_params</exec_depend>
<exec_depend>tensorrt_yolox</exec_depend>
<exec_depend>bytetrack</exec_depend>
<exec_depend>autoware_tensorrt_yolox</exec_depend>
<exec_depend>autoware_bytetrack</exec_depend>
<exec_depend>intrinsic_camera_calibrator</exec_depend>
<exec_depend>image_transport_decompressor</exec_depend>
<exec_depend>autoware_image_transport_decompressor</exec_depend>

<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
Expand Down
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