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Optimal channel selection with discrete QCQP

This is the code for reproducing the results of the paper Optimal channel selection with discrete QCQP accepted at AISTATS 2022.

Requirements

  • Python 3.7.9
  • PyTorch 1.4.0
  • TorchVision 0.5.0
  • matplotlib 3.3.2
  • cplex 12.10.0 => You need to install academic version (restricted usage for community version).

Download Pretrained Network

  • Download 'pretrained_networks.zip' from google drive.
wget --no-check-certificate -r 'https://docs.google.com/uc?export=download&id=1ZRL84HmoRG28c7IOSDu5gKNwxGcLIsPU' -O pretrained_networks.zip
  • Unzip to generate './pretrained_networks/'.
unzip pretrained_networks.zip

Training ResNet on Cifar-10

Run Pruning Training ResNet (depth 56,32,20) on Cifar10:

python exp/cifar_exp/main.py --mode (mode) --timelimit (timelimit) --baseseed (baseseed) --target_c (target) --arch (arch)
e.g.) python exp/cifar_exp/main.py --mode flops_2D --timelimit 5000 --baseseed 4 --target_c 0.46 --arch resnet20 --use_cplex True
e.g.2) python exp/cifar_exp/main.py --mode flops_2D --timelimit 5000 --baseseed 4 --target_c 0.46 --arch resnet20 --use_cplex False
e.g.3.) python exp/cifar_exp/main.py --mode flops_4D --timelimit 500 --baseseed 4 --target_c 0.46 --gamma 0.5 --arch resnet20 --use_cplex True
e.g.4.) python exp/cifar_exp/main.py --mode flops_4D --timelimit 500 --baseseed 4 --target_c 0.46 --arch resnet20 --use_cplex False
  • (mode) - 'flops_2D', 'flops_4D', 'mem_2D', 'mem_4D'
  • (timelimit) - timelimit for CPLEX. for resnet20 - 5000, resnet32 - 20000, resnet56 - 80000.
  • (baseseed) - (4,5,6) for resnet20, (1,2,3) for resnet32, resnet56
  • (target_c) - ratio of target constraints and max constraints for conv layers. 0~1
  • (gamma) - gamma for 4D. target_c ~ 1
  • (arch) - 'resnet20', 'resnet32', 'resnet56'
  • (use_cplex) - True or False

Greedy solve for the discrete QCQP

We implemented another fast greedy approach to solve discrete QCQP. Please set '(use_cplex)' to be False.