There have been many papers quoting FLOP to quote the performance of a specific approach in machine learning. For example,
We trained two models with different capacities: BlazePose Full (6.9 MFlop, 3.5M Params) and BlazePose Lite (2.7 MFlop, 1.3M Params).
I assume they measured the number of Mega FLOP needed to run the model on input. But they did not explain how.
This may be specific to neural networks, but probably isn't. This is not the same question as how to calculate the floating point operations per second a machine is capable of (which there are plenty of answers to).