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