Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

The PyTorch implementation for our IJCAI 2018 paper. This implementation is based on ResNeXt-DenseNet.

Requirements

• Python 3.6
• PyTorch 0.3.1
• TorchVision 0.3.0

Training ImageNet

Usage of Pruning Training:

We train each model from scratch by default. If you wish to train the model with pre-trained models, please use the options --use_pretrain --lr 0.01.

Run Pruning Training ResNet (depth 152,101,50,34,18) on Imagenet: (the layer_begin and layer_end is the index of the first and last conv layer, layer_inter choose the conv layer instead of BN layer):

python pruning_train.py -a resnet152 --save_dir ./snapshots/resnet152-rate-0.7 --rate 0.7 --layer_begin 0 --layer_end 462 --layer_inter 3  /path/to/Imagenet2012

python pruning_train.py -a resnet101 --save_dir ./snapshots/resnet101-rate-0.7 --rate 0.7 --layer_begin 0 --layer_end 309 --layer_inter 3  /path/to/Imagenet2012

python pruning_train.py -a resnet50  --save_dir ./snapshots/resnet50-rate-0.7 --rate 0.7 --layer_begin 0 --layer_end 156 --layer_inter 3  /path/to/Imagenet2012

python pruning_train.py -a resnet34  --save_dir ./snapshots/resnet34-rate-0.7 --rate 0.7 --layer_begin 0 --layer_end 105 --layer_inter 3  /path/to/Imagenet2012

python pruning_train.py -a resnet18  --save_dir ./snapshots/resnet18-rate-0.7 --rate 0.7 --layer_begin 0 --layer_end 57 --layer_inter 3  /path/to/Imagenet2012

Usage of Initial with Pruned Model:

We use unpruned model as initial model by default. If you wish to initial with pruned model, please use the options --use_sparse --sparse path_to_pruned_model.

Usage of Normal Training:

Run resnet(100 epochs):

python original_train.py -a resnet50 --save_dir ./snapshots/resnet50-baseline  /path/to/Imagenet2012 --workers 36

Scripts to reproduce the results in our paper

To train the ImageNet model with / without pruning, see the directory scripts (we use 8 GPUs for training).

Inference the pruned model

sh scripts/inference_resnet.sh

The trained models with log files can be found in Google Drive

Notes

Torchvision Version

We use the torchvision of 0.3.0. If the version of your torchvision is 0.2.0, then the transforms.RandomResizedCrop should be transforms.RandomSizedCrop and the transforms.Resize should be transforms.Scale.

Why use 100 epochs for training

This can improve the accuracy slightly.

Citation

@inproceedings{he2018soft,
  title     = {Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks},
  author    = {He, Yang and Kang, Guoliang and Dong, Xuanyi and Fu, Yanwei and Yang, Yi},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  pages     = {2234--2240},
  year      = {2018}
}