Two rgb inputs to a duel encoder semantic segmetation model


I’m doing inference with a semantic segmentation model that takes two input images and outputs a binary mask of segmentation results.

Is there a way to configure nvinfer to accept those two inputs?

from nvdsinfer_custom_impl.h File Reference

It says:

"By default “nvinfer” works with networks having only one input layer for video frames. If the network has more than one input layer, the custom library can implement NvDsInferInitializeInputLayers interface for initializing the other input layers. “nvinfer” assumes that the other input layers have static input information and hence this method is called only once before the first inference.
Refer the FasterRCNN sample provided with the SDK for a sample implementation of the interface."

How this can help if i need to update that static input every now and then while the stream is up and running?


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)

Hi, I’m using nvinfer with this setup:

• Hardware Platform (Jetson / GPU) : GPU
• DeepStream Version : 6.0
• TensorRT Version:
• Issue Type: question

it is a special requirement, there is no deepstream sample with the same inputs.
you need custom preprocess, please refer to the similar sample deepstream-3d-action-recognition, which has speicial input. here is App introducetion: DeepStream 3D Action Recognition App — DeepStream 6.1 Release documentation

There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.

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