/Data-Free_Network_Quantization_With_Adversarial_Knowledge_Distillation

Data-Free Network Quantization With Adversarial Knowledge Distillation PyTorch

Primary LanguagePython

Data-Free Network Quantization With Adversarial Knowledge Distillation

Data-Free Network Quantization With Adversarial Knowledge Distillation PyTorch (Reproduced)

Requirements

  • Pytorch 1.4.0
  • Python 3.6
  • Torchvision 0.5.0
  • tensorboard
  • tensorboardX

Running the code

cifar10 dataset

CUDA_VISIBLE_DEVICES=0 python main.py --dataset=cifar10 --alpha=0.01 --do_warmup=True --do_Ttrain=True

The generated images and a trained student network from Knowledge distillation will be saved in ./outputs (default) folder.

cifar100 dataset

if you did train the teacher network, let argument "do_Ttrain" be False like as belows:

CUDA_VISIBLE_DEVICES=0 python main.py --dataset=cifar10 --alpha=0.01 --do_warmup=True --do_Ttrain=False

Arguments:

  • dataset - Choose a dataset name
    • [cifar10, cifar100]
  • data - dataset path
  • teacher_dir - save path for teacher
  • n_epochs - Epochs
  • iter - Iterations
  • batch_size - Size of the batches
  • lr_G - learning rate for generator
  • lr_S - learning rate for student
  • alpha - Alpha value
  • latent_dim - Dimensionality of the latent space
  • img_size - Size of each image dimension
  • channels - Number of image channels
  • saved_img_path - Save path for generated images
  • saved_model_path - Save path for trained stduent
  • do_warmup - Do warm-up??
  • do_Ttrain - Do train teacher network??

Result examples

1

1

Reference

Choi, Yoojin, et al. "Data-free network quantization with adversarial knowledge distillation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.