iceberg-penguins.search 0.2.7

Quality Metrics

Prerequisites

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

Software Dependencies

• scipy==1.2.1
• Pillow==4.3.0
• torch
• scikit-learn==0.19.1
• torchvision==0.2.0'
• opencv-python
• rasterio
• future

Installation

PSC Bridges

From source:

$ git clone https://github.com/iceberg-project/Penguins.git
$ module load cuda
$ module load python3
$ virtualenv iceberg_penguins
$ source iceberg_penguins/bin/activate
[iceberg_penguins] $ export PYTHONPATH=<path>/iceberg_penguins/lib/python3.5/site-packages
[iceberg_penguins] $ pip install . --upgrade

From PyPi:

$ module load cuda
$ module load python3
$ virtualenv iceberg_penguins
$ source iceberg_penguins/bin/activate
[iceberg_penguins] $ export PYTHONPATH=<path>/iceberg_penguins/lib/python3.5/site-packages
[iceberg_penguins] $ pip install iceberg_penguins.search

To test

[iceberg_penguins] $ iceberg_penguins.detect

Prediction

• Download a pre-trained model at:

https://drive.google.com/file/d/149j5rlynkO1jQTLOMpL5lextHY0ozw6N/view?usp=sharing

Please put the model file to: <checkpoints_dir>/<model_name>/

The one provided here is at the epoch 300 of the model named "v3weakly_unetr_bs96_main_model_ignore_bad"

• The script to run the testing for a single PNG image:

iceberg_penguins.detect [--params ...]

params:

• --name: name of the model used for testing
• --gpu_ids: the gpu used for testing
• --checkpoints_dir: path to the folder containing the trained models
• --epoch: which epoch we use to test the model
• --input_im: path to the input image
• --output: directory to save the outputs