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---
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]}')
``` |