imgraph 0.0.9

Graph Neural Network Library Built On Top Of PyTorch and PyTorch Geometric

IMGRAPH

Used for converting image to graph, uses superpixel method for node creation, extract features from CNN models.

Example Usage:

    from imgraph.pipeline import create_graph_pipleline

    path = "path/to/image"

    create_graph_pipleline(path, 'classification', 'rag', 'resnet18', 10)

Above code will create a graph from the image and save it in the directory .~/cache/imgraph or directory specified by the user in enviornment variable IMGRAPH_HOME.

Expected input folder structure:

    image_folder
    ├── test
    │   ├── class1
    │   └── class2
    ├── train
    │   ├── class1
    │   └── class2
    └── val
        ├── class1
        └── class2

The graph will be saved in the PyG Data format or pickle format.

To install pytorch geometric dependencies, please follow the instructions here: PyG installation or use the following code snippet:

To install full dependeciens install using setup.py with full-dependencies flag (its slow, but will install all dependencies)

    import torch

    def format_pytorch_version(version):
    return version.split('+')[0]

    TORCH_version = torch.__version__
    TORCH = format_pytorch_version(TORCH_version)

    def format_cuda_version(version):
    return 'cu' + version.replace('.', '')

    CUDA_version = torch.version.cuda
    CUDA = format_cuda_version(CUDA_version)

    !pip install torch-scatter     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-sparse      -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-cluster     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html
    !pip install torch-geometric