Source code for torch_geometric.datasets.medshapenet

import os
import os.path as osp
from typing import Callable, List, Optional

import numpy as np
import torch

from torch_geometric.data import Data, InMemoryDataset


class MedShapeNet(InMemoryDataset):
    r"""The MedShapeNet datasets from the `"MedShapeNet -- A Large-Scale
    Dataset of 3D Medical Shapes for Computer Vision"
    <https://arxiv.org/abs/2308.16139>`_ paper,
    containing 8 different type of structures (classes).

    .. note::

        Data objects hold mesh faces instead of edge indices.
        To convert the mesh to a graph, use the
        :obj:`torch_geometric.transforms.FaceToEdge` as :obj:`pre_transform`.
        To convert the mesh to a point cloud, use the
        :obj:`torch_geometric.transforms.SamplePoints` as :obj:`transform` to
        sample a fixed number of points on the mesh faces according to their
        face area.

    Args:
        root (str): Root directory where the dataset should be saved.
        size (int): Number of invividual 3D structures to download per
            type (classes).
        transform (callable, optional): A function/transform that takes in an
            :obj:`torch_geometric.data.Data` object and returns a transformed
            version. The data object will be transformed before every access.
            (default: :obj:`None`)
        pre_transform (callable, optional): A function/transform that takes in
            an :obj:`torch_geometric.data.Data` object and returns a
            transformed version. The data object will be transformed before
            being saved to disk. (default: :obj:`None`)
        pre_filter (callable, optional): A function that takes in an
            :obj:`torch_geometric.data.Data` object and returns a boolean
            value, indicating whether the data object should be included in the
            final dataset. (default: :obj:`None`)
        force_reload (bool, optional): Whether to re-process the dataset.
            (default: :obj:`False`)
    """
    def __init__(
        self,
        root: str,
        size: int = 100,
        transform: Optional[Callable] = None,
        pre_transform: Optional[Callable] = None,
        pre_filter: Optional[Callable] = None,
        force_reload: bool = False,
    ) -> None:
        self.size = size
        super().__init__(root, transform, pre_transform, pre_filter,
                         force_reload=force_reload)

        path = self.processed_paths[0]
        self.load(path)

    @property
    def raw_file_names(self) -> List[str]:
        return [
            '3DTeethSeg', 'CoronaryArteries', 'FLARE', 'KITS', 'PULMONARY',
            'SurgicalInstruments', 'ThoracicAorta_Saitta', 'ToothFairy'
        ]

    @property
    def processed_file_names(self) -> List[str]:
        return ['dataset.pt']

    @property
    def raw_paths(self) -> List[str]:
        r"""The absolute filepaths that must be present in order to skip
        downloading.
        """
        return [osp.join(self.raw_dir, f) for f in self.raw_file_names]

    def process(self) -> None:
        import urllib3
        from MedShapeNet import MedShapeNet as msn

        msn_instance = msn(timeout=120)

        urllib3.HTTPConnectionPool("medshapenet.ddns.net", maxsize=50)

        list_of_datasets = msn_instance.datasets(False)
        list_of_datasets = list(
            filter(
                lambda x: x not in [
                    'medshapenetcore/ASOCA', 'medshapenetcore/AVT',
                    'medshapenetcore/AutoImplantCraniotomy',
                    'medshapenetcore/FaceVR'
                ], list_of_datasets))

        subset = []
        for dataset in list_of_datasets:
            self.newpath = self.root + '/' + dataset.split("/")[1]
            if not os.path.exists(self.newpath):
                os.makedirs(self.newpath)
            stl_files = msn_instance.dataset_files(dataset, '.stl')
            subset.extend(stl_files[:self.size])

            for stl_file in stl_files[:self.size]:
                msn_instance.download_stl_as_numpy(bucket_name=dataset,
                                                   stl_file=stl_file,
                                                   output_dir=self.newpath,
                                                   print_output=False)

        class_mapping = {
            '3DTeethSeg': 0,
            'CoronaryArteries': 1,
            'FLARE': 2,
            'KITS': 3,
            'PULMONARY': 4,
            'SurgicalInstruments': 5,
            'ThoracicAorta_Saitta': 6,
            'ToothFairy': 7
        }

        for dataset, path in zip([subset], self.processed_paths):
            data_list = []
            for item in dataset:
                class_name = item.split("/")[0]
                item = item.split("stl")[0]
                target = class_mapping[class_name]
                file = osp.join(self.root, item + 'npz')

                data = np.load(file)
                pre_data_list = Data(
                    pos=torch.tensor(data["vertices"], dtype=torch.float),
                    face=torch.tensor(data["faces"],
                                      dtype=torch.long).t().contiguous())
                pre_data_list.y = torch.tensor([target], dtype=torch.long)
                data_list.append(pre_data_list)

            if self.pre_filter is not None:
                data_list = [d for d in data_list if self.pre_filter(d)]

            if self.pre_transform is not None:
                data_list = [self.pre_transform(d) for d in data_list]

            self.save(data_list, path)