• Hardware Platform (Jetson / GPU) GPU • DeepStream Version 6.0 • JetPack Version (valid for Jetson only) N.A. • TensorRT Version 7.0 / 8.0 • NVIDIA GPU Driver Version (valid for GPU only) 470.82.01 • Issue Type( questions, new requirements, bugs) Question • How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing) N.A. • Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description) N.A.
We have a non-detection / non-classification model trained using PyTorch and converted to ONNX. The model take one frame as input and output a frame with same size.
We can run this model using TensorRT NvOnnxParser and NvInfer. Now we would like to integrate this model into our DeepStream pipeline. May I know what is the proper way to do it and any samples?
Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) • DeepStream Version • JetPack Version (valid for Jetson only) • TensorRT Version • NVIDIA GPU Driver Version (valid for GPU only) • Issue Type( questions, new requirements, bugs) • How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing) • Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
• Hardware Platform (Jetson / GPU) GPU • DeepStream Version 6.0 • JetPack Version (valid for Jetson only) N.A. • TensorRT Version 7.0 / 8.0 • NVIDIA GPU Driver Version (valid for GPU only) 470.82.01 • Issue Type( questions, new requirements, bugs) Question • How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing) N.A. • Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description) N.A.
Input layer dimension: 3x640x640 in NCHW
Output layer dimension: 3x640x640 in NCHW
Pre-process: Channel based mean subtraction and normalization.
Post-process: None.