Re-Trained Pytorch Mask-RCNN inferencing on Jetson Nano

Hi Community,

I have trained a Custom Trained Pytorch Mask-RCNN network which takes image as an input and gives outputs the bounding box, masks with class and class labels. I have used Mask-RCNN model directly for the torchvision v0.4.0. The training and data preprocessing code is similar to https://github.com/pytorch/vision/tree/master/references/segmentation and to get mask-rcnn model I have just used from torchvision.models.detection import MaskRCNN with no changes and trained it for 2 classes.

I tried to test this trained model on Jetson Nano without any use of ONNX/ DeepStream/TensorRT conversion and the swap memory(4GB) and the main memory(4GB) got filled up just while loading the model. The model weights .pth file is of size 241MB(just for ref).
I just installed the pytorch 1.2.0 and torchvision 0.4.0 as per the ref: PyTorch for Jetson - version 1.6.0 now available

Can someone tell me what I should do or how I should optimize this model to run on Jetson Nano. Is it possible to run this model in DeepStream or TensorRT? How can I convert the model to run in such system?

I’m new on this hardware, so in need of some guidance. Any suggestions from the community would be really great!

Thanks in advance.
Regards,