Quality Metrics

Prerequisites - all available on bridges via the commands below

• Linux
• Python 3
• CPU and NVIDIA GPU + CUDA CuDNN

Software Dependencies - these will be installed automatically with the installation below.

• numpy
• scipy
• pandas
• torch
• torchvision
• tensorboardX
• opencv-python
• rasterio
• affine
• geopandas
• pandas

Installation

####Preliminaries: These instructions are specific to XSEDE Bridges 2 but other resources can be used if cuda, python3, and a NVIDIA V100 GPU are present, in which case 'module load' instructions can be skipped, which are specific to Bridges 2.

####For Unix or Mac Users: Login to Bridges 2 via ssh using a Unix or Mac command line terminal. Login is available to Bridges 2 directly or through the XSEDE portal. Please see the Bridges 2 User's Guide.

####For Windows Users: Many tools are available for ssh access to Bridges 2. Please see Ubuntu, MobaXterm or PuTTY

PSC Bridges 2

Once you have logged into Bridges 2, you can follow one of two methods for installing iceberg-seals.

Method 1 (Recommended):

The lines below following a '$' are commands to enter (or cut and paste) into your terminal (note that all commands are case-sensitive, meaning capital and lowercase letters are differentiated.) Everything following '#' are comments to explain the reason for the command and should not be included in what you enter. Lines that do not start with '$' or '[seals_env] $' are output you should expect to see.

$ pwd
/home/username
$ cd $PROJECT                      # switch to your working space.
$ mkdir Seals                      # create a directory to work in.
$ cd Seals                         # move into your working directory.
$ module load cuda/10.2.0          # load parallel computing architecture.
$ module load anaconda3            # load correct python version.
$ conda create -p seals_env python=3.9 -y     # create a virtual environment to isolate your work from the default system.
$ source activate <path>/seals_env    # activate your environment. Notice the command line prompt changes to show your environment on the next line.
[seals_env] $ pwd
/ocean/projects/group/username/Seals
[seals_env] $ export PYTHONPATH=<path>/seals_env/lib/python3.9/site-packages # set a system variable to point python to your specific code. (Replace <path> with the results of pwd command above.
[seals_env] $ pip install iceberg_seals.search # pip is a python tool to extract the requested software (iceberg_seals.search in this case) from a repository. (this may take several minutes).

Method 2 (Installing from source; recommended for developers only):

$ git clone https://github.com/iceberg-project/Seals.git
$ module load cuda/10.2.0
$ module load anaconda3              # load correct python version.
$ conda create -p seals_env python=3.9 -y     # create a virtual environment to isolate your work from the default system.
$ source activate <path>/seals_env    # activate your environment. Notice the command line prompt changes to show your environment on the next line.
[seals_env] $ export PYTHONPATH=<path>/seals_env/lib/python3.9/site-packages # set a system variable to point python to your specific code. (Replace <path> with the results of pwd command above.
[seals_env] $ pip install . --upgrade

To test

[iceberg_seals] $ deactivate       # exit your virtual environment.
$ interact --gpu  # request a compute node.  This package has been tested on P100 GPUs on bridges, but that does not exclude any other resource that offers the same GPUs. (this may take a minute or two or more to receive an allocation).
$ cd $PROJECT/Seals                # make sure you are in the same directory where everything was set up before.
$ module load cuda/10.2.0          # load parallel computing architecture, as before.
$ module load anaconda3            # load correct python version, as before.
$ source activate <path>/seals_env    # activate your environment. Notice the command line prompt changes to show your environment on the next line.
[seals_env] $ export PYTHONPATH=<path>/seals_env/lib/python3.9/site-packages # set a system variable to point python to your specific code. (Replace <path> with the results of pwd command above.
[iceberg_seals] $ iceberg_seals.predicting --help  # this will display a help screen of available usage and parameters.

Prediction

• Download a pre-trained model at: https://github.com/iceberg-project/Seals/tree/master/models/Heatmap-Cnt/UnetCntWRN/UnetCntWRN_ts-vanilla.tar

You can download to your local machine and use scp, ftp, rsync, or Globus to transfer to Bridges 2.

Seals predicting is executed in two steps: First, follow the environment setup commands under 'To test' above. Then create tiles from an input GeoTiff image and write to the output_folder. The scale_bands parameter (in pixels) depends on the trained model being used. The default scale_bands is 224 for the pre-trained model downloaded above. If you use your own model the scale_bands may be different.

[iceberg_seals] $ iceberg_seals.tiling --input_image=<image_abspath> --output_folder=./test --bands='0' --stride=1.0 --patch_size=224 --geotiff=0

Then, detect seals on each tile and output counts and confidence for each tile.

[iceberg_seals] $ iceberg_seals.predicting --input_dir=./test/ --model_architecture=UnetCntWRN --hyperparameter_set=B --model_name=UnetCntWRN_ts-vanilla.tar --models_folder=saved_models --output_dir=./test_pred/