| --- |
| license: other |
| language: |
| - en |
| --- |
| # ScanObjectNN |
|
|
| `scanobjectnn_PB_T50_RS_h5.zip` contains h5 files for the hard variant of the ScanObjectNN benchmark. |
|
|
| Dataset can be loaded as follows: |
|
|
| ```python |
| import os.path as osp |
| import os |
| import torch |
| import h5py |
| import torch_geometric.transforms as T |
| from torch_geometric.datasets import ModelNet |
| from torch_geometric.data import InMemoryDataset, download_url, extract_zip, Data |
| |
| class ScanObjectNN(InMemoryDataset): |
| url = 'https://huggingface.co/datasets/cminst/ScanObjectNN/resolve/main/scanobjectnn_PB_T50_RS_h5.zip' |
| |
| def __init__(self, root, train=True, transform=None, pre_transform=None, pre_filter=None): |
| self.train = train |
| super().__init__(root, transform, pre_transform, pre_filter) |
| path = self.processed_paths[0] if train else self.processed_paths[1] |
| self.load(path) |
| |
| @property |
| def raw_file_names(self): |
| return [ |
| osp.join('main_split', 'training_objectdataset_augmentedrot_scale75.h5'), |
| osp.join('main_split', 'test_objectdataset_augmentedrot_scale75.h5') |
| ] |
| |
| @property |
| def processed_file_names(self): |
| return ['training.pt', 'test.pt'] |
| |
| def download(self): |
| path = download_url(self.url, self.raw_dir) |
| extract_zip(path, self.raw_dir) |
| os.unlink(path) |
| |
| def process(self): |
| self.save(self.process_set('training'), self.processed_paths[0]) |
| self.save(self.process_set('test'), self.processed_paths[1]) |
| |
| def process_set(self, split): |
| filename = f'{split}_objectdataset_augmentedrot_scale75.h5' |
| |
| h5_path = osp.join(self.raw_dir, 'main_split', filename) |
| |
| with h5py.File(h5_path, 'r') as f: |
| data = f['data'][:].astype('float32') |
| labels = f['label'][:].astype('int64') |
| |
| data_list = [] |
| for i in range(data.shape[0]): |
| pos = torch.from_numpy(data[i]) |
| y = torch.tensor(labels[i]).view(1) |
| |
| d = Data(pos=pos, y=y) |
| data_list.append(d) |
| |
| 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] |
| |
| return data_list |
| |
| if __name__ == '__main__': |
| dataset = ScanObjectNN(root='data/ScanObjectNN', train=True) |
| print(f'Dataset: {dataset}') |
| print(f'First graph: {dataset[0]}') |
| ``` |
