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lottery-jackpots

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Pytorch implementation of our paper accepted by TPAMI 2023 — Lottery Jackpots Exist in Pre-trained Models

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// repository documentation

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# Lottery Jackpots Exist in Pre-trained Models  ([Paper Link](https://arxiv.org/abs/2104.08700))

## 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](https://drive.google.com/drive/folders/13et0J5S2iJK9oS-twrKXVqC-tk0AO9Gn?usp=sharing) 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:

| Model     | Dataset   | Sparsity | Epoch       | Top-1 Acc. | Link                                                         |
| --------- | --------- | -------- | ----------- | ---------- | ------------------------------------------------------------ |
| VGGNet-19 | CIFAR-10  | 90%      | 30(S)       | 93.88%     | [link](https://drive.google.com/drive/folders/1Ok8Yn6hdPy6GYKruHFWt4gjuYbjRnnGR?usp=sharing) |
| VGGNet-19 | CIFAR-10  | 90%      | 160(S)      | 93.94%     | [link](https://drive.google.com/drive/folders/1oT-QXUPWxiXqsrxmq2sfyi_BQCb_dVaM?usp=sharing) |
| VGGNet-19 | CIFAR-10  | 95%      | 30(S)       | 93.49%     | [link](https://drive.google.com/drive/folders/1gnmM04kbbzJogq3sFnuDb4P46dg4Fdng?usp=sharing) |
| VGGNet-19 | CIFAR-10  | 95%      | 160(S)      | 93.74%     | [link](https://drive.google.com/drive/folders/1N_VDQjiozY1YfLqOfg_AbatqF2W1BDua?usp=sharing) |
| VGGNet-19 | CIFAR-100 | 90%      | 30(S)       | 72.59%     | [link](https://drive.google.com/drive/folders/1lele-SwnPLiUnoUS9ckdrJU5yKCw-B4w?usp=sharing) |
| VGGNet-19 | CIFAR-100 | 90%      | 160(S)      | 74.61%     | [link](https://drive.google.com/drive/folders/1_vt2ELABH0uc1szWDCPt14SUme6TqXyU?usp=sharing) |
| VGGNet-19 | CIFAR-100 | 95%      | 30(S)       | 71.76%     | [link](https://drive.google.com/drive/folders/1bHuRm2hQTJYIuoOokEdy_qh9zU2EIRcj?usp=sharing) |
| VGGNet-19 | CIFAR-100 | 95%      | 160(S)      | 73.35%     | [link](https://drive.google.com/drive/folders/1ofF9Bxlq1OI1n3rBOZFbOvJHnCo573cd?usp=sharing) |
| ResNet-32 | CIFAR-10  | 90%      | 30(S)       | 93.70%     | [link](https://drive.google.com/drive/folders/128ns-f4Ei3sh-j2IaUgSH2wpxwZ5JdW4?usp=sharing) |
| ResNet-32 | CIFAR-10  | 90%      | 160(S)      | 94.39%     | [link](https://drive.google.com/drive/folders/1SKWtpmFfwFzQmAnJyES-sBgw6SIpHO8w?usp=sharing) |
| ResNet-32 | CIFAR-10  | 95%      | 30(S)       | 92.90%     | [link](https://drive.google.com/drive/folders/12377BFszAKAVlL3NW2LTlFkolmBmPBTC?usp=sharing) |
| ResNet-32 | CIFAR-10  | 95%      | 160(S)      | 93.41%     | [link](https://drive.google.com/drive/folders/1v51xuTPujFejhTueNHwxq9qMwe0HRniu?usp=sharing) |
| ResNet-32 | CIFAR-100 | 90%      | 30(S)       | 72.22%     | [link](https://drive.google.com/drive/folders/1Pu3HbZ80dLNdkqc32oVJIMJN4grgdajC?usp=sharing) |
| ResNet-32 | CIFAR-100 | 90%      | 160(S)      | 73.43%     | [link](https://drive.google.com/drive/folders/16fNdDoAn7dDXN7qKDaosPQn1T1aWCaUF?usp=sharing) |
| ResNet-32 | CIFAR-100 | 95%      | 30(S)       | 69.38%     | [link](https://drive.google.com/drive/folders/1ygB_uEYed_27M34jX7Uxh7aOnnBfbLqj?usp=sharing) |
| ResNet-32 | CIFAR-100 | 95%      | 160(S)      | 70.31%     | [link](https://drive.google.com/drive/folders/1Sz6pbt9RhCPlwmGIaScbFLvt2fXcZ3WM?usp=sharing) |
| ResNet-50 | ImageNet  | 80%      | 30(S)       | 75.19%     | [link](https://drive.google.com/drive/folders/1s0Ar5VamRndGj06hrMzPLz5WHaqehqTf?usp=sharing) |
| ResNet-50 | ImageNet  | 90%      | 30(S)       | 72.43%     | [link](https://drive.google.com/drive/folders/1Ws5qjQrSeEYeOf2UhIdEmgQO04O5LwUa?usp=sharing) |

To test 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`

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