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SCAN: A Scalabel Neural Networks Framework for Compact and Efficient Models

A pytorch implementation of paper SCAN: A Scalabel Neural Networks Framework for Compact and Efficient Models. An advanced version has been released in https://github.com/ArchipLab-LinfengZhang/pytorch-self-distillation-final.

Requirements

Install PyTorch>=1.0.0, torchvision>=0.2.0.

Download and process the CIFAR datasets by torchvision.

How to train

python train.py [--depth=18] [--class_num=100] [--epoch=200] [--lambda_KD=0.5]

depth indicates the number of layers in resnet.

class_num decides which dataset will be used (cifar10/100).

epoch indicates how many epoches will be utilized to train this model.

lambda_KD is a hyper-parameter for balancing distillation loss and cross entropy loss.

Dynamatic inference

python inference.py [--depth=18]

Only a pre-trained ResNet18 model is prepared now, stored in model folder. inference.py will use it to inference, and print its accuracy and acceleration ratio. By adjusting the thresholds in line30 in inference.py, you can get different accuracy and acceleration results.

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A pytorch implement of scalable neural netowrks.

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