PyCIL: A Python Toolbox for Class-Incremental Learning

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Introduction

Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process. However, real-world applications often face the incoming new classes, and a model should incorporate them continually. The learning paradigm is called Class-Incremental Learning (CIL). We propose a Python toolbox that implements several key algorithms for class-incremental learning to ease the burden of researchers in the machine learning community. The toolbox contains implementations of a number of founding works of CIL, such as EWC and iCaRL, but also provides current state-of-the-art algorithms that can be used for conducting novel fundamental research. This toolbox, named PyCIL for Python Class-Incremental Learning, is open source with an MIT license.

For more information about incremental learning, you can refer to these reading materials:

  • A brief introduction (in Chinese) about CIL is available here.

  • A PyTorch Tutorial to Class-Incremental Learning (with explicit codes and detailed explanations) is available here.

How To Use

Clone this GitHub repository:

git clone https://github.com/G-U-N/PyCIL.git
cd PyCIL

Dependencies

Running Experiments

  1. Edit the [MODEL NAME].json file for global settings.

  2. Edit the hyperparameters in the corresponding [MODEL NAME].py file (e.g., models/icarl.py).

  3. Run:

python main.py --config=./exps/[MODEL NAME].json

where [MODEL NAME] should be chosen from finetune, ewc, lwf, replay, gem, icarl, bic, wa, podnet, der, etc.

  1. Hyperparameters

When using PyCIL, you can edit the global parameters and algorithm-specific hyper-parameter in the corresponding json file.

These parameters include:

  • memory-size: The total exemplar number in the incremental learning process. Assuming there are \(K\) classes at the current stage, the model will preserve \(\left[\frac{memory-size}{K}\right]\) exemplar per class.

  • init-cls: The number of classes in the first incremental stage. Since there are different settings in CIL with a different number of classes in the first stage, our framework enables different choices to define the initial stage.

  • increment: The number of classes in each incremental stage \(i\), \(i\) > 1. By default, the number of classes per incremental stage is equivalent per stage.

  • convnet-type: The backbone network for the incremental model. According to the benchmark setting, ResNet32 is utilized for CIFAR100, and ResNet18 is used for ImageNet.

  • seed: The random seed adopted for shuffling the class order. According to the benchmark setting, it is set to 1993 by default.

Other parameters in terms of model optimization, e.g., batch size, optimization epoch, learning rate, learning rate decay, weight decay, milestone, and temperature, can be modified in the corresponding Python file.

Datasets

We have implemented the pre-processing of CIFAR100, imagenet100, and imagenet1000. When training on CIFAR100, this framework will automatically download it. When training on imagenet100/1000, you should specify the folder of your dataset in utils/data.py.

def download_data(self):
    assert 0, "You should specify the folder of your dataset"
    train_dir = '[DATA-PATH]/train/'
    test_dir = '[DATA-PATH]/val/'

Here is the file list of ImageNet100 (or say ImageNet-Sub).