Thank you all for feedback,
Interesting, I’ll definitely try that for Tensor → cv::Mat conversion (right now it’s not implemented 
). But when converting in the opposite direction (cv::Mat → Tensor), it seems that the data must be contiguous? I’ve actually borrowed code from PythonConversion.swift and doing the same check for cv::Mat using isContinuous() and making cv::Mat clone if it’s not. If I understand correctly, some operations on cv::Mat can make it non-continuous and making it continuous in order to convert to Tensor may result in additional overhead.
As for numpy to tensor conversion in TF & pytorch, I can’t quite follow when they copy memory and when alias.
There might be difficulties with running that notebook. Are your swift_dev notebooks supposed to be working in colab and locally using official swift-jupyter or Jeremy’s Harebrained installation procedures?
Wrapper expects that OpenCV is installed and so far I’ve only tested it with OpenCV 4.0.1 compiled from source.
Not sure how to better tackle that - perhaps for colab I could start notebook with executing commands to compile/install OpenCV (e.g. using shellCommand from 00_load_data). For local installation I could create additional prep script like your gist and check these scripts together?
Note that OpenCV compilation is rather time consuming 
I can check if wrapper works with binary distribution (i.e. from conda), but it probably won’t be compiled for max performance in that case.
So far I’ve only checked it with Ubuntu 18 and OpenCV 4.0.1 compiled/installed from source. Docker itself is not required (I just don’t have another option in Win10 )
You can see commands used to build OpenCV in this Dockerfile.