PyTorch Implementation: RotNet

PyTorch implementation of Unsupervised Representation Learning by Predicting Image Rotations
based on the official implementation.

The implementation supports the following datasets:

• CIFAR-10 / CIFAR-100
• SVHN
• Caltech101 / Caltech256
• STL10
• HAM10000
• ImageNet

Installation

Required python packages are listed in requirements.txt. All dependencies can be installed using pip

pip install -r requirements.txt

or using conda

conda install --file requirements.txt

Training

RotNet training is started by running the following command (--pbar to show progress bar during training):

python main.py --pbar

All commandline arguments, which can be used to adapt the configuration of the RotNet training are defined and described in arguments.py.
By default the following configuration is run:

model: 'NIN_4'
dataset: 'cifar10'
lr: 0.1
wd: 0.0005
epochs: 100
batch_size: 128
device: 'cuda'
out_dir: 'rotation_prediction'
momentum: 0.9

In addition to these, the following arguments can be used to further configure the RotNet training process:

• --device <cuda / cpu>: Specify whether training should be run on GPU (if available) or CPU
• --num-workers <num_workers>: Number of workers used by torch dataloader
• --resume <path to run_folder>: Resumes training of training run saved at specified path, e.g. 'out/rotnet_training/run_0'. Dataset splits, model state, optimizer state, etc.
  are loaded and training is resumed with specified arguments.
• see arguments.py for more

Alternatively, the polyaxon.yaml-file can be used to start the RotNet training on a polyaxon-cluster:

polyaxon run -f polyaxon.yaml -u

For a general introduction to polyaxon and its commandline client, please refer to the official documentation

Monitoring

The training progress (loss, accuracy, etc.) can be monitored using tensorboard as follows:

tensorboard --logdir <result_folder>

This starts a tensorboard instance at localhost:6006, which can be opened in any common browser.

Evaluation

A trained RotNet model can be evaluated by running:

 python3 eval.py --run-path out/rotnet_training/run_0 --pbar --device <cuda / cpu>

where --run-path specifies the path at which the run to be evaluated is saved. Alternatively, one can also check all
metrics over all epochs using the tensorboard file.

References

@inproceedings{
  gidaris2018unsupervised,
  title={Unsupervised Representation Learning by Predicting Image Rotations},
  author={Spyros Gidaris and Praveer Singh and Nikos Komodakis},
  booktitle={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=S1v4N2l0-},
}