/IRI-DL

IRI deep learning project example

Primary LanguagePython

IRI-DL

IRI deep learning project example. This repo contains the code structure I use for all my research. I designed it with the purpose of having a generic framework in which new research ideas could be quickly evaluated. For this particular repo, as an example, the framework has been configured to solve object classification.

If you have any doubt do not hesitate to contact me at apumarola@iri.upc.edu.

0. System

Upgrade system:

sudo apt-get update
sudo apt-get upgrade

1. Nvidia Driver

sudo apt install nvidia-415*
sudo reboot

2. MiniConda

  1. Download miniconda from the oficial website. (Recommended: Python 3.* , 64-bits)
  2. Install miniconda. (Recommended: use predefined paths and answer yes whenever you are asked yes/no) 
    bash ~/Downloads/Miniconda3-latest-Linux-x86_64.sh

3. Dependencies

  1. Create and activate conda environment for the project 
    conda create -n IRI-DL
source activate IRI-DL
  2. Install Pytorch 
    conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
  3. Install other rand dependencies 
    conda install matplotlib opencv pillow scikit-learn scikit-image cython tqdm
pip install tensorboardX
  4. Deactivate environment 
    conda deactivate

4. Tensorboard

  1. Create and activate conda environment for tensorboard: 
    conda create -n tensorboard python=3.6
source activate tensorboard
  2. Install Tensorflow CPU 
    pip install tensorflow
  3. Deactivate environment 
    conda deactivate

Set code

Simply clone the repo:

cd /path/to/desired/folder/
git clone https://github.com/albertpumarola/IRI-DL.git

Set IDE

  1. Install PyCharm Professional from the official website using your academic email.
  2. Open Project: Open->path/to/repo/
  3. Create desktop entry: Tools->Create Desktop Entry...
  4. Set interpreter. In File->Settings->Project: IRI-DL->Project Interpreter->gear->Add->Conda Environment->Existing environment: set path to the created environment ~/miniconda3/envs/IRI-DL/bin/python.

Run train

  1. Add configuration. In the top right Add Configuration...->+->Python. Introduce:

    • Name: train
    • Script Path: path/to/repo/src/train.py
    • Parameters: --exp_dir experiments/model1/basic_settings
    • Environment Variables:
      • PYTHONUNBUFFERED 1
      • OMP_NUM_THREADS 4
      • CUDA_VISIBLE_DEVICES 0
    • Python Interpreter: Python 3.7(IRI-DL)
    • Working Directory: path/to/repo/

  2. To run train simply press play button. If you prefer running in terminal you can launch

    . experiments/prepare_session.sh 0

    to set environment variables and then run

    python src/train.py --exp_dir experiments/model1/basic_settings

  3. To visualize. In a new terminal run:

    source activate tensorboard
tensorboard --logdir path/to/repo/experiments/model1/basic_settings/

  4. To run other experiments simply change the experiment dir (e.g. experiments/model1/with_vgg_lower_lr)

Run test

  1. Add configuration. In the top right Add Configuration...->+->Python. Introduce:
    • Name: test
    • Script Path: path/to/repo/src/test.py
    • Parameters: --exp_dir experiments/model1/basic_settings
    • Environment Variables:
      • PYTHONUNBUFFERED 1
      • OMP_NUM_THREADS 1
      • CUDA_VISIBLE_DEVICES 0
    • Python Interpreter: Python 3.7(IRI-DL)
    • Working Directory: path/to/repo/
  2. To run test simply press play button. If you prefer running in terminal you can launch 
    . experiments/prepare_session.sh 0
    to set environment variables and then run 
    python src/test.py --exp_dir experiments/model1/basic_settings
    results will be store in experiments/model1/basic_settings/test
  3. To test other experiments simply change the experiment dir (e.g. experiments/model1/with_vgg_lower_lr)