knowledge-distillation-pytorch
- Exploring knowledge distillation of DNNs for efficient hardware solutions
- Author: Haitong Li
- Framework: PyTorch
- Dataset: CIFAR-10
Features
- A framework for exploring "shallow" and "deep" knowledge distillation (KD) experiments
- Hyperparameters defined by "params.json" universally (avoiding long argparser commands)
- Hyperparameter searching and result synthesizing (as a table)
- Progress bar, tensorboard support, and checkpoint saving/loading (utils.py)
- Pretrained teacher models available for download
Install
-
Clone the repo
git clone https://github.com/peterliht/knowledge-distillation-pytorch.git -
Install the dependencies (including Pytorch)
pip install -r requirements.txt
Organizatoin:
- ./train.py: main entrance for train/eval with or without KD on CIFAR-10
- ./experiments/: json files for each experiment; dir for hypersearch
- ./model/: teacher and student DNNs, knowledge distillation (KD) loss defination, dataloader
Key notes about usage for your experiments:
- Download the zip file for pretrained teacher model checkpoints from this Box folder
- Simply move the unzipped subfolders into 'knowledge-distillation-pytorch/experiments/' (replacing the existing ones if necessary; follow the default path naming)
- Call train.py to start training 5-layer CNN with ResNet-18's dark knowledge, or training ResNet-18 with state-of-the-art deeper models distilled
- Use search_hyperparams.py for hypersearch
- Hyperparameters are defined in params.json files universally. Refer to the header of search_hyperparams.py for details
Train (dataset: CIFAR-10)
Note: all the hyperparameters can be found and modified in 'params.json' under 'model_dir'
-- Train a 5-layer CNN with knowledge distilled from a pre-trained ResNet-18 model
python train.py --model_dir experiments/cnn_distill
-- Train a ResNet-18 model with knowledge distilled from a pre-trained ResNext-29 teacher
python train.py --model_dir experiments/resnet18_distill/resnext_teacher
-- Hyperparameter search for a specified experiment ('parent_dir/params.json')
python search_hyperparams.py --parent_dir experiments/cnn_distill_alpha_temp
--Synthesize results of the recent hypersearch experiments
python synthesize_results.py --parent_dir experiments/cnn_distill_alpha_temp
Results: "Shallow" and "Deep" Distillation
Quick takeaways (more details to be added):
- Knowledge distillation provides regularization for both shallow DNNs and state-of-the-art DNNs
- Having unlabeled or partial dataset can benefit from dark knowledge of teacher models
-Knowledge distillation from ResNet-18 to 5-layer CNN
| Model | Dropout = 0.5 | No Dropout |
|---|---|---|
| 5-layer CNN | 83.51% | 84.74% |
| 5-layer CNN w/ ResNet18 | 84.49% | 85.69% |
-Knowledge distillation from deeper models to ResNet-18
| Model | Test Accuracy |
|---|---|
| Baseline ResNet-18 | 94.175% |
| + KD WideResNet-28-10 | 94.333% |
| + KD PreResNet-110 | 94.531% |
| + KD DenseNet-100 | 94.729% |
| + KD ResNext-29-8 | 94.788% |
References
H. Li, "Exploring knowledge distillation of Deep neural nets for efficient hardware solutions," CS230 Report, 2018
Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015).
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014). Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550.