KD-Lib 0.0.17

A Pytorch Library to help extend all Knowledge Distillation works

KD_Lib

A PyTorch library to easily facilitate knowledge distillation for custom deep learning models

Installation :

Stable release

KD_Lib is compatible with Python 3.6 or later and also depends on pytorch. The easiest way to install KD_Lib is with pip, Python’s preferred package installer.

$ pip install KD-Lib

Note that KD_Lib is an active project and routinely publishes new releases. In order to upgrade KD_Lib to the latest version, use pip as follows.

$ pip install -U KD-Lib

Build from source

If you intend to install the latest unreleased version of the library (i.e from source), you can simply do:

$ git clone https://github.com/SforAiDl/KD_Lib.git
$ cd KD_Lib
$ python setup.py install

Usage

To implement the most basic version of knowledge distillation from Distilling the Knowledge in a Neural Network and plot losses

import torch
import torch.optim as optim
from torchvision import datasets, transforms
from KD_Lib.KD import VanillaKD

# This part is where you define your datasets, dataloaders, models and optimizers

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=False,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

teacher_model = <your model>
student_model = <your model>

teacher_optimizer = optim.SGD(teacher_model.parameters(), 0.01)
student_optimizer = optim.SGD(student_model.parameters(), 0.01)

# Now, this is where KD_Lib comes into the picture

distiller = VanillaKD(teacher_model, student_model, train_loader, test_loader,
                      teacher_optimizer, student_optimizer)
distiller.train_teacher(epochs=5, plot_losses=True, save_model=True)    # Train the teacher network
distiller.train_student(epochs=5, plot_losses=True, save_model=True)    # Train the student network
distiller.evaluate(teacher=False)                                       # Evaluate the student network
distiller.get_parameters()                                              # A utility function to get the number of parameters in the teacher and the student network

To train a collection of 3 models in an online fashion using the framework in Deep Mutual Learning and log training details to Tensorboard

import torch
import torch.optim as optim
from torchvision import datasets, transforms
from KD_Lib.KD import DML
from KD_Lib.models import ResNet18, ResNet50                                   # To use models packaged in KD_Lib

# This part is where you define your datasets, dataloaders, models and optimizers

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=False,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

student_params = [4, 4, 4, 4, 4]
student_model_1 = ResNet50(student_params, 1, 10)
student_model_2 = ResNet18(student_params, 1, 10)

student_cohort = [student_model_1, student_model_2]

student_optimizer_1 = optim.SGD(student_model_1.parameters(), 0.01)
student_optimizer_2 = optim.SGD(student_model_2.parameters(), 0.01)

student_optimizers = [student_optimizer_1, student_optimizer_2]

# Now, this is where KD_Lib comes into the picture

distiller = DML(student_cohort, train_loader, test_loader, student_optimizers, log=True, logdir="./Logs")

distiller.train_students(epochs=5)
distiller.evaluate()
distiller.get_parameters()

Currently implemented works

Some benchmark results can be found in the logs file.

Paper	Link	Repository (KD_Lib/)
Distilling the Knowledge in a Neural Network	https://arxiv.org/abs/1503.02531	KD/vision/vanilla
Improved Knowledge Distillation via Teacher Assistant	https://arxiv.org/abs/1902.03393	KD/vision/TAKD
Relational Knowledge Distillation	https://arxiv.org/abs/1904.05068	KD/vision/RKD
Distilling Knowledge from Noisy Teachers	https://arxiv.org/abs/1610.09650	KD/vision/noisy
Paying More Attention To The Attention	https://arxiv.org/abs/1612.03928	KD/vision/attention
Revisit Knowledge Distillation: a Teacher-free Framework	https://arxiv.org/abs/1909.11723	KD/vision/teacher_free
Mean Teachers are Better Role Models	https://arxiv.org/abs/1703.01780	KD/vision/mean_teacher
Knowledge Distillation via Route Constrained Optimization	https://arxiv.org/abs/1904.09149	KD/vision/RCO
Born Again Neural Networks	https://arxiv.org/abs/1805.04770	KD/vision/BANN
Preparing Lessons: Improve Knowledge Distillation with Better Supervision	https://arxiv.org/abs/1911.07471	KD/vision/KA
Improving Generalization Robustness with Noisy Collaboration in Knowledge Distillation	https://arxiv.org/abs/1910.05057	KD/vision/noisy
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks	https://arxiv.org/abs/1903.12136	KD/text/BERT2LSTM
Deep Mutual Learning	https://arxiv.org/abs/1706.00384	KD/vision/DML
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	https://arxiv.org/abs/1803.03635	Pruning/ lottery_tickets
Regularizing Class-wise Predictions via Self- knowledge Distillation.	https://arxiv.org/abs/2003.13964	KD/vision/CSDK

Please cite our pre-print if you find KD_Lib useful in any way :)

@misc{shah2020kdlib,
  title={KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization},
  author={Het Shah and Avishree Khare and Neelay Shah and Khizir Siddiqui},
  year={2020},
  eprint={2011.14691},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

History

0.0.1 (2020-05-11)

• First release on PyPI.

0.0.3 (2020-05-29)

• Add Base Class and some implementations