I have a Deep learning Pytorch model with me to predict a certain activity from given input video. With respect to the model, I have converted that model to ONNX to TensorRT Engine file.
Now, To predict that particular activity, I am able to create a PyTorch pipeline with the model and three inputs with shape (1,3,12,640,640), (10,3,640,640) and (10,3,640,640).
The first input is basically 12 images combined with shape 3x640x640. The first input would be the set of continuous 12 images extracted from a single video stream. Other two inputs would be first 10 images and last 10 images from the first input. Iteratively, This pre-processing would be used to feed the three inputs to the model.
My objective is to replicate the same PyTorch pipeline in Deep stream using TensorRT ‘.engine’ file.
Can you please help me provide some light on this? What can we use from Deepstream or GStreamer or anything else, To Extract the set of continuous images from a single input video stream and process it further to feed it to the model in Deepstream pipeline.
Let me know if you need any other details or clarity from my end.
Any help/suggestions would be really appreciated.