| inference_service: support RKNN distributed runtime
Support RKNN artifacts in the distributed inference flow so the board runtime can
load .rknn models while keeping LeRobot checkpoint metadata for edge-side
preprocessing and postprocessing.
Align the live dispatcher defaults, board packaging, launch docs, and reliable
controller command QoS with the measured RKNN deployment path.
Defer optional backend imports so RKNN and CPU paths remain importable and
testable without Ascend runtime packages installed.
Signed-off-by: XiaoqiangWu <wuxiaoqiang.rtos@huawei.com>
| 24 天前 |
| feat(model_utils): add ONNX export and loss compare nodes
Add two utility nodes for LeRobot ACT policy evaluation:
1. ExportOnnxNode - Convert PyTorch policies to ONNX format
- Dynamic batch size support
- Dictionary input wrapper for observation format
- ONNX simplification using onnx-simplifier
- Configurable via ROS2 parameters
2. LossCompareNode - Compare model outputs and compute L1 loss
- generate_target mode: Generate target outputs from batch data
- compute_loss mode: Compute L1 loss between predictions and targets
- Batch processing with progress tracking
- Auto policy path detection
Files added:
- export_onnx_node.py: ONNX export node
- loss_compare_node.py: Loss comparison node
- setup.py: Package configuration
- package.xml: ROS2 package manifest
Usage:
ros2 run model_utils export_onnx_node --ros-args \
-p policy_path:=<path> -p device:=cuda
ros2 run model_utils loss_compare_node --ros-args \
-p mode:=compute_loss -p batch_path:=<path>
| 2 个月前 |
| feat(model_utils): add ONNX export and loss compare nodes
Add two utility nodes for LeRobot ACT policy evaluation:
1. ExportOnnxNode - Convert PyTorch policies to ONNX format
- Dynamic batch size support
- Dictionary input wrapper for observation format
- ONNX simplification using onnx-simplifier
- Configurable via ROS2 parameters
2. LossCompareNode - Compare model outputs and compute L1 loss
- generate_target mode: Generate target outputs from batch data
- compute_loss mode: Compute L1 loss between predictions and targets
- Batch processing with progress tracking
- Auto policy path detection
Files added:
- export_onnx_node.py: ONNX export node
- loss_compare_node.py: Loss comparison node
- setup.py: Package configuration
- package.xml: ROS2 package manifest
Usage:
ros2 run model_utils export_onnx_node --ros-args \
-p policy_path:=<path> -p device:=cuda
ros2 run model_utils loss_compare_node --ros-args \
-p mode:=compute_loss -p batch_path:=<path>
| 2 个月前 |
| feat(model_utils): add ONNX export and loss compare nodes
Add two utility nodes for LeRobot ACT policy evaluation:
1. ExportOnnxNode - Convert PyTorch policies to ONNX format
- Dynamic batch size support
- Dictionary input wrapper for observation format
- ONNX simplification using onnx-simplifier
- Configurable via ROS2 parameters
2. LossCompareNode - Compare model outputs and compute L1 loss
- generate_target mode: Generate target outputs from batch data
- compute_loss mode: Compute L1 loss between predictions and targets
- Batch processing with progress tracking
- Auto policy path detection
Files added:
- export_onnx_node.py: ONNX export node
- loss_compare_node.py: Loss comparison node
- setup.py: Package configuration
- package.xml: ROS2 package manifest
Usage:
ros2 run model_utils export_onnx_node --ros-args \
-p policy_path:=<path> -p device:=cuda
ros2 run model_utils loss_compare_node --ros-args \
-p mode:=compute_loss -p batch_path:=<path>
| 2 个月前 |
| fix(model_utils): fix 3 bugs in model utils
* 1. remove exec cmd in ros setup.py
* 2. remove redundant concat in export 3403 onnx path
* 3. make sure --device takes effect when comparing loss
Signed-off-by: YidaHao <haoyida@huawei.com>
| 1 个月前 |
