This is a fully working implementation of the iCaRL code that can be used to reproduce CIFAR-100 experiments.
This code currently reproduces the CIFAR-100 10 classes per batch experiment (figure 2.a, top right).
Beware that this is not the official iCaRL code. The original Theano-Lasagne code for the CIFAR-100 experiments can be found in the official repo.
Beware that this code won't work with the iILSVRC dataset as it uses a modified version version of the ResNet which is specific to CIFAR-100.
This implementation has been tested with Python 3.8.6 and PyTorch 1.7.1 (see the conda environment for more details).
The code was manually translated from the original Theano-Lasagne one. This includes the modified ResNet and the custom weight initialization procedure. The code was translated by analyzing the original code and the Lasagne documentation in order to correctly port the correct default values for optimizers, layers, weight initialization procedures, etc. Beware that discrepancies with the original code may still exist.
This implementation is far from being efficient. A more efficient, general and tested version is coming to the Avalanche Continual Learning library very soon!
The reference conda environment can be found in environment.yml
The entry point for the experiments is main_icarl.py. Some annotations have been left allowing for a mapping between the Theano-Lasagne and the PyTorch code.
Various values are hard coded (for instance, the replay buffer size), but it should be easy to adapt them at will.
The NCProtocol class is the grandfather of the NCScenario class found in Avalanche. Its role is to return batches of classes by splitting the CIFAR-100 dataset. Changing the dataset and the amount of classes allocated to each batch is very simple.
Tensorboard logging is not supported. Results are returned by using the same method used in the iCaRL original code, that is by saving tensors to disk (top1_acc_list_cumul_icarl_cl10 and top1_acc_list_ori_icarl_cl10).
Again, it is strongly encouraged to use the Avalanche implementation of iCaRL, which will be integrated very soon and will surely be more general and tested.