FujitsuResearch / automatic_pruning

Structured pruning with automatic pruning rate derivation for ICPR 2020

Automatic Pruning Rate Derivation for Structured Pruning of Deep Neural Networks

Requirements

Automatic Pruner requires:

• Python 3.6
• Pytorch >=1.6
• Torchvision >= 0.6.0+cu101
• Numpy >= 1.18.2
• tqdm >= 4.62.0

Quick start

1. Prepare the pre-trained model, and the dataset for re-training such as CIFAR-10 and ImageNet.
   Pre-trained models for example codes can be downloaded from the following links.

• https://zenodo.org/record/5900671#.Ye-tKOrP38A (for CIFAR-10)
• https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py (for ImageNet)

2. Move to sample code directory.

cd /examples/<sample>

3. Set the file path of the dataset and pre-trained model in run.sh.
   Example of /examples/resnet34_imagenet/run.sh

CUDA_VISIBLE_DEVICES='0' python3 main.py --data ../dataset/imagenet/ --pretrained_model_path ../pretrained_model/resnet34-b627a593.pth > log.log

• --data The file path for retraining dataset, e.g. CIFAR-10 and ImageNet.
• --pretrained_model_path The file path of pre-trained model.

4. Execute run.sh.

chmod +x run.sh && ./run.sh

Note: When running inference with pruned model by this code

The number of channels of pruned model by this code is changed from the model before pruning. So, when run inference with pruned model by this code, change the number of channels defined in model file (e.g. resnet34.py).

Results