PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

Related tags

Deep Learning pytorch-deep-generative-replay
Overview

pytorch-deep-generative-replay

PyTorch implementation of Continual Learning with Deep Generative Replay, NIPS 2017

model

Results

Continual Learning on Permutated MNISTs

  • Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

Continual Learning on MNIST-SVHN

  • Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

  • Generated samples from the scholar trained without any replay (left) and with Deep Generative Replay (right).

  • Training scholar's generator without replay (left) and with Deep Generative Replay (right).

Continual Learning on SVHN-MNIST

  • Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

  • Generated samples from the scholar trained without replay (left) and with Deep Generative Replay (right).

  • Training scholar's generator without replay (left) and with Deep Generative Replay (right).

Installation

$ git clone https://github.com/kuc2477/pytorch-deep-generative-replay
$ pip install -r pytorch-deep-generative-replay/requirements.txt

Commands

Usage

$ ./main.py --help
$ usage: PyTorch implementation of Deep Generative Replay [-h]
                                                          [--experiment {permutated-mnist,svhn-mnist,mnist-svhn}]
                                                          [--mnist-permutation-number MNIST_PERMUTATION_NUMBER]
                                                          [--mnist-permutation-seed MNIST_PERMUTATION_SEED]
                                                          --replay-mode
                                                          {exact-replay,generative-replay,none}
                                                          [--generator-z-size GENERATOR_Z_SIZE]
                                                          [--generator-c-channel-size GENERATOR_C_CHANNEL_SIZE]
                                                          [--generator-g-channel-size GENERATOR_G_CHANNEL_SIZE]
                                                          [--solver-depth SOLVER_DEPTH]
                                                          [--solver-reducing-layers SOLVER_REDUCING_LAYERS]
                                                          [--solver-channel-size SOLVER_CHANNEL_SIZE]
                                                          [--generator-c-updates-per-g-update GENERATOR_C_UPDATES_PER_G_UPDATE]
                                                          [--generator-iterations GENERATOR_ITERATIONS]
                                                          [--solver-iterations SOLVER_ITERATIONS]
                                                          [--importance-of-new-task IMPORTANCE_OF_NEW_TASK]
                                                          [--lr LR]
                                                          [--weight-decay WEIGHT_DECAY]
                                                          [--batch-size BATCH_SIZE]
                                                          [--test-size TEST_SIZE]
                                                          [--sample-size SAMPLE_SIZE]
                                                          [--image-log-interval IMAGE_LOG_INTERVAL]
                                                          [--eval-log-interval EVAL_LOG_INTERVAL]
                                                          [--loss-log-interval LOSS_LOG_INTERVAL]
                                                          [--checkpoint-dir CHECKPOINT_DIR]
                                                          [--sample-dir SAMPLE_DIR]
                                                          [--no-gpus]
                                                          (--train | --test)

To Run Full Experiments

# Run a visdom server and conduct full experiments
$ python -m visdom.server &
$ ./run_full_experiments

To Run a Single Experiment

# Run a visdom server and conduct a desired experiment
$ python -m visdom.server &
$ ./main.py --train --experiment=[permutated-mnist|svhn-mnist|mnist-svhn] --replay-mode=[exact-replay|generative-replay|none]

To Generate Images from the learned Scholar

$ # Run the command below and visit the "samples" directory
$ ./main.py --test --experiment=[permutated-mnist|svhn-mnist|mnist-svhn] --replay-mode=[exact-replay|generative-replay|none]

Note

  • I failed to find the supplementary materials that the authors mentioned in the paper to contain the experimental details. Thus, I arbitrarily chose a 4-convolutional-layer CNN as a solver in this project. If you know where I can find the additional materials, please let me know through the project's Github issue.

Reference

Author

Ha Junsoo / @kuc2477 / MIT License

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Comments
  • Some errors while running and my solution

    Some errors while running and my solution

    I had problems running the code initially. The first problem that I encountered was "ImportError: cannot import name 'ImageOps' ". I solved it by changing it to "from PIL import ImageOps". The second problem I had has something to do with visdom which I solved it by removing all related codes. Hopefully the above information help someone who would like to run this code.

    If anyone can provide codes that are compatible with newer PyTorch (either 0.4.1 or 1.0), Please share it !

    opened by AlanChou 0
  • TypeError: len() of a 0-d tensor

    TypeError: len() of a 0-d tensor

    Hi, I am facing the foll. issue using the pytorch, torchvision, and PIL versions (because of compatibility requirement with cuda 10.1) as: torch: 1.4.0 torchvision: 0.5.0 PIL: 5.2.0

    Traceback (most recent call last): File "./main.py", line 186, in cuda=cuda File "/home/js/DGR_pytorch/train.py", line 102, in train collate_fn=collate_fn, File "/home/js/DGR_pytorch/dgr.py", line 130, in train_with_replay collate_fn=collate_fn, File "/home/js/DGR_pytorch/dgr.py", line 205, in _train_batch_trainable_with_replay callback(trainable, progress, batch_index, result) File "/home/js/DGR_pytorch/train.py", line 157, in cb result['g_loss'], 'generator g loss', iteration, env=env File "/home/js/DGR_pytorch/visual.py", line 87, in visualize_scalar [name], name, iteration, env=env File "/home/js/DGR_pytorch/visual.py", line 92, in visualize_scalars assert len(scalars) == len(names) File "/home/js/anaconda3/envs/env_con/lib/python3.5/site-packages/torch/tensor.py", line 445, in len raise TypeError("len() of a 0-d tensor") TypeError: len() of a 0-d tensor

    Kindly suggest an alternative. Thank you.

    opened by JsPassion 0
  • Incremental Learning

    Incremental Learning

    Can incremental learning be achieved, that is to say, if a model can recognize a picture as a cat or a dog, it is necessary to add new categories on this basis and only train new categories without retraining all data?

    opened by LIMr1209 0
Owner
Junsoo Ha
A graduate student @SNUVL
Junsoo Ha
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