66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import pickle
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from collections import defaultdict
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from abc import ABC
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from pathlib import Path
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from torch.utils.data import Dataset
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from ml_lib.point_toolset.sampling import FarthestpointSampling
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import numpy as np
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class _Point_Dataset(ABC, Dataset):
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@property
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def sample_shape(self):
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# FixMe: This does not work when more then x/y tuples are returned
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return self[0][0].shape
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@property
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def setting(self) -> str:
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raise NotImplementedError
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headers = ['x', 'y', 'z', 'xn', 'yn', 'zn', 'label', 'cl_idx']
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def __init__(self, root=Path('data'), sampling_k=2048, transforms=None, load_preprocessed=True, *args, **kwargs):
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super(_Point_Dataset, self).__init__()
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self.load_preprocessed = load_preprocessed
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self.transforms = transforms if transforms else lambda x: x
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self.sampling_k = sampling_k
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self.sampling = FarthestpointSampling(K=self.sampling_k)
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self.root = Path(root)
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self.raw = self.root / 'raw'
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self.processed_ext = '.pik'
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self.raw_ext = '.xyz'
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self.processed = self.root / self.setting
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self.processed.mkdir(parents=True, exist_ok=True)
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self._files = list(self.raw.glob(f'*{self.setting}*'))
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def _read_or_load(self, item):
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raw_file_path = self._files[item]
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processed_file_path = self.processed / raw_file_path.name.replace(self.raw_ext, self.processed_ext)
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if not self.load_preprocessed:
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processed_file_path.unlink(missing_ok=True)
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if not processed_file_path.exists():
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pointcloud = defaultdict(list)
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with raw_file_path.open('r') as raw_file:
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for row in raw_file:
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values = [float(x) for x in row.strip().split(' ')]
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for header, value in zip(self.headers, values):
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pointcloud[header].append(value)
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for key in pointcloud.keys():
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pointcloud[key] = np.asarray(pointcloud[key])
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with processed_file_path.open('wb') as processed_file:
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pickle.dump(pointcloud, processed_file)
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return processed_file_path
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def __len__(self):
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raise NotImplementedError
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def __getitem__(self, item):
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raise NotImplementedError
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