Lottery Jackpots Exist in Pre-trained Models (Paper Link)

Requirements

• Python >= 3.7.4
• Pytorch >= 1.6.1
• Torchvision >= 0.4.1

Reproduce the Experiment Results

1. Download the pre-trained models from this link and place them in the pre-train folder.

2. Select a configuration file in configs to reproduce the experiment results reported in the paper. For example, to find a lottery jackpot with 30 epochs for pruning 95% parameters of ResNet-32 on CIFAR-10, run:

   python cifar.py --config configs/resnet32_cifar10/90sparsity30epoch.yaml --gpus 0

   To find a lottery jackpot with 30 epochs for pruning 90% parameters of ResNet-50 on ImageNet, run:

   python imagenet.py --config configs/resnet50_imagenet/90sparsity30epoch.yaml --gpus 0

   Note that the data_path in the yaml file should be changed to the data

Evaluate Our Pruned Models

We provide configuration, training logs, and pruned models reported in the paper. They can be downloaded from the provided links in the following table:

To test the our pruned models, download the pruned models and place them in the ckpt folder.

1. Select a configuration file in configs to test the pruned models. For example, to evaluate a lottery jackpot for pruning ResNet-32 on CIFAR-10, run:

   python evaluate.py --config configs/resnet32_cifar10/evaluate.yaml --gpus 0

   To evaluate a lottery jackpot for pruning ResNet-50 on ImageNet, run:

   python evaluate.py --config configs/resnet50_imagenet/evaluate.yaml --gpus 0