Hello, when I started isaac_ros_centerpose_sensor_rt.launch. py, an error occurred,
[component_container_mt-1] [INFO] [1736750089.050065883] [centerpose_encoder.reshape_node]: [NitrosNode] Node was started
[component_container_mt-1] 2025-01-13 14:34:49.067 ERROR /home/rx/workspaces/isaac_ros-dev/src/isaac_ros_dnn_inference/isaac_ros_gxf_extensions/gxf_isaac_tensor_rt/gxf/extensions/tensor_rt/tensor_rt_inference.cpp@543: Failed to retrieve Tensor input
[component_container_mt-1] 2025-01-13 14:34:49.067 ERROR gxf/std/entity_executor.cpp@596: Failed to tick codelet in entity: XTYBZUSNJE_inference code: GXF_FAILURE
[component_container_mt-1] 2025-01-13 14:34:49.123 WARN gxf/std/multi_thread_scheduler.cpp@343: Error while executing entity E89 named ‘XTYBZUSNJE_inference’: GXF_FAILURE
[component_container_mt-1] 2025-01-13 14:34:50.111 ERROR gxf/std/entity_executor.cpp@212: Entity with eid 89 not found!
[component_container_mt-1] [WARN] [1736750090.111103698] [centerpose_inference]: [NitrosNode] The heartbeat entity (eid=89) was stopped. The graph may have been terminated.
I don’t understand where the problem occurred. Below is the content of my modified isaac_ros_centerpose_sensor_rt.launch. py file
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Licensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
Apache License, Version 2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
SPDX-License-Identifier: Apache-2.0
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import ComposableNodeContainer
from launch_ros.descriptions import ComposableNode
def generate_launch_description():
“”“Generate launch description for Centerpose.”“”
tensor_names = [
‘bboxes’,
‘scores’,
‘kps’,
‘clses’,
‘obj_scale’,
‘kps_displacement_mean’,
‘kps_heatmap_mean’,
]
tensor_names_str = ‘[’
for tensor_name in tensor_names:
tensor_names_str += f’“{tensor_name}”’
if tensor_name != tensor_names[-1]:
tensor_names_str += ‘,’
tensor_names_str += ‘]’
launch_args = [
DeclareLaunchArgument(
'input_image_width',
default_value='640',
description='The input image width'),
DeclareLaunchArgument(
'input_image_height',
default_value='480',
description='The input image height'),
DeclareLaunchArgument(
'network_image_width',
default_value='512',
description='The input image width that the network expects'),
DeclareLaunchArgument(
'network_image_height',
default_value='512',
description='The input image height that the network expects'),
DeclareLaunchArgument(
'encoder_image_mean',
default_value='[0.408, 0.447, 0.47]',
description='The mean for image normalization'),
DeclareLaunchArgument(
'encoder_image_stddev',
default_value='[0.289, 0.274, 0.278]',
description='The standard deviation for image normalization'),
DeclareLaunchArgument(
'model_file_path',
default_value='/home/rx/workspaces/isaac_ros-dev/isaac_ros_assets/models/centerpose_shoe/1/model.onnx',
description='The absolute file path to the ONNX file'),
DeclareLaunchArgument(
'engine_file_path',
default_value='/home/rx/workspaces/isaac_ros-dev/isaac_ros_assets/models/centerpose_shoe/1/model.plan',
description='The absolute file path to the TensorRT engine file'),
DeclareLaunchArgument(
'input_tensor_names',
default_value='["input"]',
description='A list of tensor names to bound to the specified input binding names'),
DeclareLaunchArgument(
'input_binding_names',
default_value='["input"]',
description='A list of input tensor binding names (specified by model)'),
DeclareLaunchArgument(
'input_tensor_formats',
default_value='["nitros_tensor_list_nchw_rgb_f32"]',
description='The nitros format of the input tensors'),
DeclareLaunchArgument(
'output_tensor_names',
default_value=tensor_names_str,
description='A list of tensor names to bound to the specified output binding names'),
DeclareLaunchArgument(
'output_binding_names',
default_value=tensor_names_str,
description='A list of output tensor binding names (specified by model)'),
DeclareLaunchArgument(
'output_tensor_formats',
default_value='["nitros_tensor_list_nhwc_rgb_f32"]',
description='The nitros format of the output tensors'),
DeclareLaunchArgument(
'tensorrt_verbose',
default_value='False',
description='Whether TensorRT should verbosely log or not'),
DeclareLaunchArgument(
'force_engine_update',
default_value='False',
description='Whether TensorRT should update the TensorRT engine file or not'),
DeclareLaunchArgument(
'output_field_size',
default_value='[128, 128]',
description='Represents the size of the 2D keypoint decoding from the network output'),
DeclareLaunchArgument(
'cuboid_scaling_factor',
default_value='1.0',
description='Scales the cuboid used for calculating the size of the objects detected'),
DeclareLaunchArgument(
'score_threshold',
default_value='0.3',
description='The threshold for scores values to discard.'),
DeclareLaunchArgument(
'object_name',
default_value='shoe',
description='The name of the category instance that is being detected',
),
DeclareLaunchArgument(
'show_axes',
default_value='True',
description='Whether to show axes or not for visualization',
),
DeclareLaunchArgument(
'bounding_box_color',
default_value='0x000000ff',
description='The color of the bounding box for visualization',
),
]
# DNN Image Encoder parameters
input_image_width = LaunchConfiguration('input_image_width')
input_image_height = LaunchConfiguration('input_image_height')
network_image_width = LaunchConfiguration('network_image_width')
network_image_height = LaunchConfiguration('network_image_height')
encoder_image_mean = LaunchConfiguration('encoder_image_mean')
encoder_image_stddev = LaunchConfiguration('encoder_image_stddev')
# Tensor RT parameters
model_file_path = LaunchConfiguration('model_file_path')
engine_file_path = LaunchConfiguration('engine_file_path')
input_tensor_names = LaunchConfiguration('input_tensor_names')
input_binding_names = LaunchConfiguration('input_binding_names')
input_tensor_formats = LaunchConfiguration('input_tensor_formats')
output_tensor_names = LaunchConfiguration('output_tensor_names')
output_binding_names = LaunchConfiguration('output_binding_names')
output_tensor_formats = LaunchConfiguration('output_tensor_formats')
tensorrt_verbose = LaunchConfiguration('tensorrt_verbose')
force_engine_update = LaunchConfiguration('force_engine_update')
# Centerpose Decoder parameters
output_field_size = LaunchConfiguration('output_field_size')
cuboid_scaling_factor = LaunchConfiguration('cuboid_scaling_factor')
score_threshold = LaunchConfiguration('score_threshold')
object_name = LaunchConfiguration('object_name')
# Centerpose visualization parameters
show_axes = LaunchConfiguration('show_axes')
bounding_box_color = LaunchConfiguration('bounding_box_color')
centerpose_inference_node = ComposableNode(
name='centerpose_inference',
package='isaac_ros_tensor_rt',
plugin='nvidia::isaac_ros::dnn_inference::TensorRTNode',
parameters=[{
'model_file_path': model_file_path,
'engine_file_path': engine_file_path,
'input_tensor_names': input_tensor_names,
'input_binding_names': input_binding_names,
'input_tensor_formats': input_tensor_formats,
'output_tensor_names': output_tensor_names,
'output_binding_names': output_binding_names,
'output_tensor_formats': output_tensor_formats,
'verbose': tensorrt_verbose,
'force_engine_update': force_engine_update
}])
centerpose_decoder_node = ComposableNode(
name='centerpose_decoder_node',
package='isaac_ros_centerpose',
plugin='nvidia::isaac_ros::centerpose::CenterPoseDecoderNode',
parameters=[{
'output_field_size': output_field_size,
'cuboid_scaling_factor': cuboid_scaling_factor,
'score_threshold': score_threshold,
'object_name': object_name,
}],
)
centerpose_visualizer_node = ComposableNode(
name='centerpose_visualizer_node',
package='isaac_ros_centerpose',
plugin='nvidia::isaac_ros::centerpose::CenterPoseVisualizerNode',
parameters=[{
'show_axes': show_axes,
'bounding_box_color': bounding_box_color,
}],
)
rclcpp_container = ComposableNodeContainer(
name='centerpose_container',
namespace='',
package='rclcpp_components',
executable='component_container_mt',
composable_node_descriptions=[
centerpose_inference_node, centerpose_decoder_node, centerpose_visualizer_node],
output='screen',
)
encoder_dir = get_package_share_directory('isaac_ros_dnn_image_encoder')
centerpose_encoder_launch = IncludeLaunchDescription(
PythonLaunchDescriptionSource(
[os.path.join(encoder_dir, 'launch', 'dnn_image_encoder.launch.py')]
),
launch_arguments={
'input_image_width': input_image_width,
'input_image_height': input_image_height,
'network_image_width': network_image_width,
'network_image_height': network_image_height,
'image_mean': encoder_image_mean,
'image_stddev': encoder_image_stddev,
'attach_to_shared_component_container': 'True',
'component_container_name': 'centerpose_container',
'dnn_image_encoder_namespace': 'centerpose_encoder',
'image_input_topic': '/image',
'camera_info_input_topic': '/camera_info',
'tensor_output_topic': '/tensor_pub',
}.items(),
)
final_launch_container = launch_args + [rclcpp_container, centerpose_encoder_launch]
return LaunchDescription(final_launch_container)