Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) Jetson Nano
• DeepStream Version 6.0
• JetPack Version (valid for Jetson only) 5.6
• TensorRT Version 8.0.1
• Issue Type( questions, new requirements, bugs) New requirements / question
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
My requirement / question is regarding the gst-nvinfer plugin. In the comments for the DsNvInferImpl class (gstinver_impl.h:191) it is explained that a model can be updated at runtime by setting “config-file-path”. This is the function that On the Fly Model Update is using in deepstream-app-5. On the corresponding page from the DeepStream 6 docs (and README for deepstream-app-5), it notes the assumption:
New model must have same network parameter configuration as of previous model (e.g. network resolution, network architecture, number of classes).
In the source code comments however, it only states that the input resolution, channels and the model type (Detection/Classification/Segmentation) should not change. It does not note the constraint of network architecture.
It turns out that in my application I need to change the model (preferably during runtime, but not necessarily) without changing input resolution or channels. The model should also always stay a detection model. In my current test application (it is based on deepstream-app-1), I can change the model during runtime using this functionality, but only when I don’t change the network architecture. I cannot change the network architecture (for example ResNet to FasterRCNN). When I try to, somewhere after the function NvDsInferContextImpl::buildModel() is called a segmentation fault occurs.
My question is whether the documentation on the DeepStream docs page is leading in this case, meaning that the functionality of updating “config-file-path” does have the constraint that you cannot change the network architecture. If so, is there another possibility to change the model architecture during runtime? Will this functionality be implemented soon? If not, what am I doing wrong in my case?