two questions about once for all about gan-compression HOT 3 CLOSED

For the first question, you could use the latency on your platform instead of computation as the criterion of the sub-network searching. You could refer to our searching code and latency code for more details.

I do not quite understand your second question, could you elaborate more on that?

from gan-compression.

Sorry, I did not describe the problem clearly, let me re-clarify the two problems:
For problem1: Different hardware platforms have different preferred ops, but the parent network structure of once for all is fixed, and the ops inside are fixed, and type selection is not supported. For example, when doing upsampling, on some platform deconv is more efficient than others, but on other platform depth2space is more efficient than others. In this case, how to search for network structures that are best to different hardware platforms?
For problem2: In the camera pipeline, different hardware platforms have different input data due to different sensors. Each hardware platform has corresponding training data to train the corresponding platform model. In this case, if once for all is used, what data should be used to train once for all, and then the subnet from this supernet can be used on different hardware platforms?

from gan-compression.

For the first question, currently our searching space only supports different channels. You could select the best performed one one according to the latency on your specified device. If you need to expand the searching space to support different modules or ops, you could refer to ProxylessNAS.

from gan-compression.