AIROBEST

Repository for AIROBEST project

Getting started

Make sure to install all the requirements, run: pip install -r requirements.txt

The following instruction assumes your current directory is $PATH_TO_AIROBEST_RPOJECT/source/cnn

Data preprocessing

• Specify paths to hyperspectral data and forest data in spreprocess.sh script, for more options, run: python preprocess.py -h
• Run sbash spreprocess.sh to submit preprocessing job on Taito
• This process will generate the following files and save them under ./data directory:
  • hyper_image.pt: hyperspectral image saved as pytorch Tensor. We use this instead of spectral format to increase GPU utilization.
  • hyperspectral_src_l2norm_along_channel.pt: tensor contains the corresponding norm of each pixel from hyperspectral image, norms are computed along color channel.
  • hyperspectral_tgt_normalized.pt: tensor of target labels, labels for regression tasks has been normalized to [0, 1] scale.
  • metadata.pt: dictionary contains information about the input data, such as class values for each classification task

Training

If you want to see real-time visualization, you must start visdom server first.

How to keep visom server running while waiting for submitted batch job to start?

• Log in to taito using local forwarding: ssh -L 8097:127.0.0.1:8097 username@taito-gpu.csc.fi
• Open tmux session by running: tmux
• cd to project folder: cd $path_to_project_folder/source/cnn
• Start visdom server: bash server.sh
• Now one can detach the tmux session and end the ssh session without interrupting visom server
• To attach again to tmux session, ssh to remote server and run tmux attach

Training configurations:

• hyper_data_path: path to hyperspectral image saved in preprocessing stage.
• src_norm_multiplier: path to file contains pixel norm (corresponding to hyperspectral_src_l2norm_along_channel.pt file in preprocessing stage)
• tgt_path: path to target label

For other options, run: python train.py -h for detailed explanation.

Visualization can be seen by opening http://localhost:8097/ from web browser. You first need to login on Taito with local forwarding: ssh -L 8097:127.0.0.1:8097 username@taito-gpu.csc.fi

Project structure

• preprocess.py: processes and prepares the data for neural network training.
• train.py: constructs and builds the CNN models which are defined in models/model.py
• input folder: contains the implementations of data loader, utility functions, and focal loss function
• inference.py: runs the inference on test data
• tools folder: includes code for working with hyperspectral data