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I found the dataset generation method in paper:
For each model (e.g., AlexNet), we generate 2,000 variants
(e.g., AlexNets) by re-sampling the output channel number and kernel size for each layer. Specifically, the new output channel number is randomly selected from [0.2 × 𝐶𝑜𝑢𝑡, 1.8 × 𝐶𝑜𝑢𝑡], and the kernel size is sampled from {1, 3, 5, 7, 9}
and the generated models in the datasets.zip, but how can I get the models to execute them by myself?
I noticed that these models are ir graphs, how can I transfer them back to pytorch/tensorflow models to do the inference by myself
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