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CortexNet

This repo contains the PyTorch implementation of CortexNet.
Check the project website for further information.

Project structure

The project consists of the following folders and files:

Dependencies

pip install sk-video
conda config --add channels conda-forge
conda update --all
conda install tqdm

IDE

This project has been realised with PyCharm by JetBrains and the Vim editor. Grip has been also fundamental for crafting decent documtation locally.

Initialise environment

Once you've determined where you'd like to save your experimental results — let's call this directory <my saving location> — run the following commands from the project's root directory:

ln -s <my saving location> results  # replace <my saving location>
mkdir results/000 && touch results/000/train.log  # init. placeholder
ln -s results/000 last  # create pointer to the most recent result

Setup new experiment

Ready to run your first experiment? Type the following:

./new_experiment.sh

GPU selection

Let's say your machine has N GPUs. You can choose to use any of these, by specifying the index n = 0, ..., N-1. Therefore, type CUDA_VISIBLE_DEVICES=n just before python ... in the following sections.

Train MatchNet

python -u main.py --mode MatchNet <CLI arguments> | tee last/train.log

Train TempoNet

python -u main.py --mode TempoNet --pre-trained <path> <CLI args> | tee last/train.log

GPU selection

To run on a specific GPU, say n, type CUDA_VISIBLE_DEVICES=n just before python ....