Ex-model CL

Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them without access to the original data.

NOTE: This repository is a work in progress. It's a heavy refactoring of the code from our internal repository to make it easier to understand and reuse by other researchers. At some point we plan to integrate the strategies and benchmarks directly in Avalanche.

The module exmodel follows Avalanche's structure:

• benchmarks: ExModelScenario adds an attribute trained_models to the benchmarks. The original train_stream and test_stream are available for evaluation purposes (they are assumed private by the scenario).
• models: custom nn.Modules and baseline architectures used in the experiments.
• evaluation: loggers and metrics.
• training: training algorithms. The ex-model distillation strategy is here.

The folder experiments contains the code to run the experiments. The main is in launcher.py, while the training function is in train_ex_model.py.

Install Dependencies

conda env create -f environment.yml

avalanche must be installed separately. This repository used Avalanche pre-release, aligned with the master branch. You can use commit a299bd4.

Run Experiments

to launch an experiment run:

python experiments/launcher.py --config CONFIGS/debug.yaml

The directory CONFIGS contains the configuration already setup for you. To run the experiments you may need to change the logs and data folders in the CONFIGS yaml files.

Known issues

The table printed by the rich-based logger sometimes misalign metric values (when there are missing values). The textual logger and json files are all correct.