Hi, thanks for posting this code. Maybe I am missing something but it seems that at each layer the cells are not repeated N times as in the PNAS paper?

Would you mind to provide the CIFAR-10 model? I use the provided architecture https://github.com/chenxi116/PNASNet.pytorch/blob/master/genotypes.py#L5 and the code from https://github.com/quark0/darts/blob/master/cnn/model.py#L111, but obtain the model parameter size of 4.2 MB (I use initial channel of 48 and layers of 11). Is there anything wrong?

@chenxi116 This repository looks very useful thanks for putting it up!

Would you mind adding a license to this? Without a license it is impossible to legally clone or run this code. If you're not sure and would like a suggestion the Apache 2.0 license is a good option. A quick summary of apache 2.0 is available at tl;dr legal. This is the same license used by TensorFlow. Here is the license text:

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Hi, I just want to use your code to convert PNASNet_mobile models to PyTorch.

After modifiing your code with both PNASNet.pytorch and PNASNet.TF,

I find that the accuracy is slow on converted models with PNASNet.pytorch Test: [50000/50000] Prec@1 0.710 Prec@5 0.901 But In PNASNet.TF, the accuracy is 74.2

The shape and dict test is pass. But the Check 2 failed.

Could you give me some hints on converting the PNASNet-mobile models?

Thanks

I want to use your code to design an architecture based on my own dataset. Could you tell me which part in your code I need to modify? Thanks very much!