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A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)The original paper:Learning both Weights and Connections for Efficient Neural Networks
Thelibs package contains utilities needed,andcompressor.py defines aCompressor class that allows pruning a network layer-by-layer.
The fileiterative_pruning.py contains functioniter_prune which achieves iterative pruning.
An example use of the function is described in the main function in the same file.Please devise your own script and do
fromiterative_pruningimport*
to import all necessary modules and run your script as follows.
python your_script.py [-h] [--data DIR] [--arch ARCH] [-j N] [-b N] [-o O] [-m E] [-c I] [--lr LR] [--momentum M] [--weight_decay W] [--resume PATH] [--pretrained] [-t T [T ...]] [--cuda]
-h, --help show thishelp message andexit --data DIR, -d DIR path to dataset --arch ARCH, -a ARCH model architecture: alexnet| densenet121| densenet161| densenet169| densenet201| inception_v3| resnet101| resnet152| resnet18| resnet34| resnet50| squeezenet1_0| squeezenet1_1| vgg11| vgg11_bn| vgg13| vgg13_bn| vgg16| vgg16_bn| vgg19| vgg19_bn -j N, --workers N number of data loading workers (default: 4) -b N, --batch-size N mini-batch size (default: 256) -o O, --optimizer O optimizers: ASGD| Adadelta| Adagrad| Adam| Adamax| LBFGS| Optimizer| RMSprop| Rprop| SGD| SparseAdam (default: SGD) -m E, --max_epochs E max number of epochswhile training -c I, --interval I checkpointing interval --lr LR, --learning-rate LR initial learning rate --momentum M momentum --weight_decay W, --wd W weight decay --resume PATH path to latest checkpoint (default: none) --pretrained use pre-trained model -t T [T ...], --topk T [T ...] Top k precision metrics --cuda
(other architectures in torch.vision package can also be chosen, but have not been experimented on). DATA_LOCATION should be replaced with the location of the ImageNet dataset on your machine.
| Model | Top-1 | Top-5 | Compression Rate |
|---|---|---|---|
| LeNet-300-100 | 92% | N/A | 92% |
| LeNet-5 | 98.8% | N/A | 92% |
| AlexNet | 39% | 63% | 85.99% |
Note: To achieve better results, try to tweak the alpha hyper-parameter in functionprune() to change the pruning rate of each layer.
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A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)
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