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
