NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.
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How to parse any arch in NAS-Bench-101 as pytorch class to train. #121
Hi,
I'm trying to sample few architectures from NAS-Bench-101, and then query the socre, and also get the torch-model according to the architecutre.
I wanna use the model to do some customer training or testing.
In core/graph.py, it shows we should us the following code to parse the model to pytorch module
**Use as pytorch module**
If you want to learn the weights of the operations or any
other parameters of the graph you have to parse it first.
>>> graph = getFancySearchSpace()
>>> graph.parse()
>>> logits = graph(data)
>>> optimizer.min(loss(logits, target))
But the graph.parse() will fail if i use NasBench101SearchSpace as graph instance ,
Hi, I'm trying to sample few architectures from NAS-Bench-101, and then query the socre, and also get the torch-model according to the architecutre.
I wanna use the model to do some customer training or testing.
In core/graph.py, it shows we should us the following code to parse the model to pytorch module
But the graph.parse() will fail if i use NasBench101SearchSpace as graph instance ,