Speed up inference or/and training of a 3D deep neural network (U-net) for a regression task
4 vues (au cours des 30 derniers jours)
I am working on a (volumetric) regression task using a 3D deep neural network.
Its architecture is based on the 3D U-net model provided by the output of Matlab's function unet3dLayers(). I modified its architecture by switching the upscaling transposedConv3dLayer layers with resize3dLayer layers. Furthermore, I removed the PixelClassificationLayer and defined a custom deep learning loop and loss function as described here: https://de.mathworks.com/help/deeplearning/ug/train-network-using-custom-training-loop.html.
The training and inference work well, but their duration is too long for the later use case. Thus, I tried to use the "Deep Network Quantizer" to speed up the inference time, but the toolbox does not support 3D layers. Also, other optimisation strategies for inference/training do not seem to be supported for 3D layers.
So my questions is: Is there any other technique to speed up the inference and/or the training of this type of network architecture?
Many thanks in advance!