3.7 KiB
Network Pruning via Transformable Architecture Search
Network Pruning via Transformable Architecture Search is accepted by NeurIPS 2019. In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network. You could see the highlight of our Transformable Architecture Search (TAS) at our project page.
Requirements and Preparation
Please install Python>=3.6 and PyTorch>=1.2.0.
CIFAR and ImageNet should be downloaded and extracted into $TORCH_HOME.
The proposed method utilized knowledge distillation (KD), which require pre-trained models. Please download these models from Google Drive (or train by yourself) and save into .latent-data.
LOGS: We provide some logs at Google Drive. It includes (1) logs of training searched shape of ResNet-18 and ResNet-50 on ImageNet, (2) logs of searching and training for ResNet-164 on CIFAR, (3) logs of searching and training for ResNet56 on CIFAR-10, (4) logs of searching and training for ResNet110 on CIFAR-100.
Usage
Use bash ./scripts/TAS/prepare.sh to prepare data splits for CIFAR-10, CIFARR-100, and ILSVRC2012.
If you do not have ILSVRC2012 data, please comment L12 in ./scripts/prepare.sh.
args: cifar10 indicates the dataset name, ResNet56 indicates the basemodel name, CIFARX indicates the searching hyper-parameters, 0.47/0.57 indicates the expected FLOP ratio, -1 indicates the random seed.
Model Configuration
The searched shapes for ResNet-20/32/56/110/164 and ResNet-18/50 in Table 3/4 in the original paper are listed in configs/NeurIPS-2019.
Search for the depth configuration of ResNet
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-depth-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
Search for the width configuration of ResNet
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-width-gumbel.sh cifar10 ResNet110 CIFARX 0.57 -1
Search for both depth and width configuration of ResNet
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-shape-cifar.sh cifar10 ResNet56 CIFARX 0.47 -1
Training the searched shape config from TAS: If you want to directly train a model with searched configuration of TAS, try these:
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar10 C010-ResNet32 -1
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/tas-infer-train.sh cifar100 C100-ResNet32 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/tas-infer-train.sh imagenet-1k ImageNet-ResNet18V1 -1
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/tas-infer-train.sh imagenet-1k ImageNet-ResNet50V1 -1
Citation
If you find that this project helps your research, please consider citing the following paper:
@inproceedings{dong2019tas,
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2019}
}