This is unofficial implementation of NetAdapt(https://arxiv.org/abs/1804.03230).
- python netadapt.py pretrain ... Training and saving the MobileNet will start.
- python netadapt.py maketable ... Calculating and saving the lookup-table will start.
- python netadapt.py search ... The main pruning procedure will start.
The adapted model will be outputted to "./adapted_model.pickle".
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GTX1080Ti MobileNet CIFAR-10 compression-rate=0.9 layer2, numfilter=34 layer3, numfilter=49 layer4, numfilter=91 layer5, numfilter=144 layer6, numfilter=256 layer7, numfilter=447 layer8, numfilter=482 layer9, numfilter=468 layer10, numfilter=436 layer11, numfilter=471 layer12, numfilter=512 layer13, numfilter=1024 reduction = 0.105497025671 test accuracy = 0.811116
base_time = 0.13710842136667933
adapted_time = 0.0943378491526019
(both batchsize = 128)
exact-latency = 0.6880529161684906%
lookup-table is so poor approximation -
Core i5 MobileNet CIFAR-10 compression-rate=0.2 layer2, numfilter=1 layer3, numfilter=1 layer4, numfilter=1 layer5, numfilter=94 layer6, numfilter=76 layer7, numfilter=249 layer8, numfilter=280 layer9, numfilter=251 layer10, numfilter=324 layer11, numfilter=280 layer12, numfilter=1 layer13, numfilter=1024 reduction = 0.781648723048 test accuracy = 0.698279
base_time=9.089213041232654
exact time=2.1972459169957546
exact reduction percentage=0.24174215160631443