TensorRT frozen graphs - integration with NiftyNet

[carlpe]

I have a question concerning TensorRT and use of frozen graphs.

"TensorRT performs several important transformations and optimizations to the neural network graph. First, layers with unused output are eliminated to avoid unnecessary computation. Next, where possible, convolution, bias, and ReLU layers are fused to form a single layer. Another transformation is horizontal layer fusion, or layer aggregation, along with the required division of aggregated layers to their respective output. Horizontal layer fusion improves performance by combining layers that take the same source tensor and apply the same operations with similar parameters."

https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html?fbclid=IwAR18Vlgy-ekaC3u-q9gQXoEMgBDeEyREB9FnTNBnZNmstrulr07H-5Jj-J0

Is this something that is possible to implement in NiftyNey? Is it possible to convert the checkpoints from niftynet and convert them into such frozen graphs? And would it significantly improve the inference time?