https://github.com/gmvandeven/progressive-learning-pytorch
https://github.com/gmvandeven/progressive-learning-pytorch
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/gmvandeven/progressive-learning-pytorch
- Owner: GMvandeVen
- License: mit
- Created: 2020-08-27T13:01:31.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-04T10:24:01.000Z (about 5 years ago)
- Last Synced: 2025-04-06T21:09:51.816Z (6 months ago)
- Language: Python
- Size: 1.47 MB
- Stars: 6
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Task-incremental learning on CIFAR-100
A PyTorch implementation for task-incremental learning with the Split CIFAR-100 protocol.
## Installation & requirements
The current version of the code has been tested with `Python 3.5.2` on several Linux operating systems with the following versions of PyTorch and Torchvision:
* `pytorch 1.1.0`
* `torchvision 0.2.2`
Assuming Python and pip are set up, the Python-packages used by this code can be installed using:
```bash
pip install -r requirements.txt
```
However, you might want to install pytorch and torchvision in a slightly different way to ensure compatability with your version of CUDA (see https://pytorch.org/).
Finally, the code in this repository itself does not need to be installed, but two scripts should be made executable:
```bash
chmod +x main_cl.py main_pretrain.py
```
## Running experiments
Use `main_cl.py` to run individual continual learning experiments. The main options for this script are:
- `--experiment`: which task protocol? (`splitMNIST`|`permMNIST`|`CIFAR100`)
- `--tasks`: how many tasks?
To run specific methods, use the following:
- Context-dependent-Gating (XdG): `./main_cl.py --xdg --xdg-prop=0.8`
- Elastic Weight Consolidation (EWC): `./main_cl.py --ewc --lambda=5000`
- Online EWC: `./main_cl.py --ewc --online --lambda=5000 --gamma=1`
- Synaptic Intelligenc (SI): `./main_cl.py --si --c=0.1`
- Learning without Forgetting (LwF): `./main_cl.py --replay=current --distill`
- Experience Replay (ER): `./main_cl.py --replay=exemplars --budget=1000`
- Averaged Gradient Episodic Memory (A-GEM): `./main_cl.py --replay=exemplars --agem --budget=1000`
For information on further options: `./main_cl.py -h`.
## On-the-fly plots during training
With this code it is possible to track progress during training with on-the-fly plots. This feature requires `visdom`.
Before running the experiments, the visdom server should be started from the command line:
```bash
python -m visdom.server
```
The visdom server is now alive and can be accessed at `http://localhost:8097` in your browser (the plots will appear
there). The flag `--visdom` should then be added when calling `./main_cl.py` to run the experiments with on-the-fly plots.
For more information on `visdom` see .