Unofficial PyTorch implementation of pruning VGG on CIFAR-10 Data set

Reference: Pruning Filters For Efficient ConvNets, ICLR2017

• torch (version: 1.2.0)
• torchvision (version: 0.4.0)
• Pillow (version: 6.1.0)
• matplotlib (version: 3.1.1)
• numpy (version: 1.16.5)

• --train-flag: Train VGG on CIFAR Data set
• --save-path: Path to save results, ex) trained_models/
• --load-path: Path to load checkpoint, add 'checkpoint.pht' with save_path, ex) trained_models/checkpoint.pth
• --resume-flag: Resume the training from checkpoint loaded with load-path
• --prune-flag: Prune VGG
• --prune-layers: List of target convolution layers for pruning, ex) conv1 conv2
• --prune-channels: List of number of channels for pruning the prune-layers, ex) 4 14
• --independent-prune-flag: Prune multiple layers by independent strategy
• --retrain-flag: Retrain the pruned nework
• --retrain-epoch: Number of epoch for retraining pruned network
• --retrain-lr: Number of epoch for retraining pruned network

You can follow the procedure of prune filter in the .ipynb file but switch the network to the corresponding network file: drop_out_network.py and VGG_19_network.py

quantization.py file apply quantization to the original VGG-16 model. we created a new network trying to fit the pruned and retrained network into the original model which unfortunately failed.

https://drive.google.com/drive/folders/1vxF0YL90rF_19sQyHhwGfC_WtgVl7NmW?usp=drive_link