140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
""" Module for LabelTensor """
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import torch
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class LabelTensor(torch.Tensor):
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"""Torch tensor with a label for any column."""
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@staticmethod
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def __new__(cls, x, labels, *args, **kwargs):
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return super().__new__(cls, x, *args, **kwargs)
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def __init__(self, x, labels):
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'''
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Construct a `LabelTensor` by passing a tensor and a list of column
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labels. Such labels uniquely identify the columns of the tensor,
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allowing for an easier manipulation.
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:param torch.Tensor x: the data tensor.
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:param labels: the labels of the columns.
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:type labels: str or iterable(str)
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:Example:
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>>> from pina import LabelTensor
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>>> tensor = LabelTensor(torch.rand((2000, 3)), ['a', 'b', 'c'])
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>>> tensor
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tensor([[6.7116e-02, 4.8892e-01, 8.9452e-01],
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[9.2392e-01, 8.2065e-01, 4.1986e-04],
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[8.9266e-01, 5.5446e-01, 6.3500e-01],
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...,
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[5.8194e-01, 9.4268e-01, 4.1841e-01],
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[1.0246e-01, 9.5179e-01, 3.7043e-02],
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[9.6150e-01, 8.0656e-01, 8.3824e-01]])
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>>> tensor.extract('a')
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tensor([[0.0671],
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[0.9239],
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[0.8927],
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...,
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[0.5819],
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[0.1025],
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[0.9615]])
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>>> tensor.extract(['a', 'b'])
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tensor([[0.0671, 0.4889],
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[0.9239, 0.8207],
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[0.8927, 0.5545],
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...,
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[0.5819, 0.9427],
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[0.1025, 0.9518],
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[0.9615, 0.8066]])
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>>> tensor.extract(['b', 'a'])
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tensor([[0.4889, 0.0671],
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[0.8207, 0.9239],
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[0.5545, 0.8927],
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...,
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[0.9427, 0.5819],
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[0.9518, 0.1025],
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[0.8066, 0.9615]])
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'''
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if x.ndim == 1:
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x = x.reshape(-1, 1)
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if isinstance(labels, str):
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labels = [labels]
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if len(labels) != x.shape[1]:
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raise ValueError(
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'the tensor has not the same number of columns of '
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'the passed labels.'
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)
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self.labels = labels
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def clone(self, *args, **kwargs):
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"""
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Clone the LabelTensor. For more details, see
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:meth:`torch.Tensor.clone`.
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:return: a copy of the tensor
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:rtype: LabelTensor
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"""
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return LabelTensor(super().clone(*args, **kwargs), self.labels)
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def to(self, *args, **kwargs):
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"""
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Performs Tensor dtype and/or device conversion. For more details, see
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:meth:`torch.Tensor.to`.
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"""
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tmp = super().to(*args, **kwargs)
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new = self.__class__.clone(self)
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new.data = tmp.data
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return new
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def extract(self, label_to_extract):
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"""
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Extract the subset of the original tensor by returning all the columns
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corresponding to the passed `label_to_extract`.
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:param label_to_extract: the label(s) to extract.
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:type label_to_extract: str or iterable(str)
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:raises TypeError: labels are not str
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:raises ValueError: label to extract is not in the labels list
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"""
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if isinstance(label_to_extract, str):
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label_to_extract = [label_to_extract]
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elif isinstance(label_to_extract, (tuple, list)): # TODO
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pass
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else:
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raise TypeError(
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'`label_to_extract` should be a str, or a str iterator')
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try:
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indeces = [self.labels.index(f) for f in label_to_extract]
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except ValueError:
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raise ValueError('`label_to_extract` not in the labels list')
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new_data = self[:, indeces].float()
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new_labels = [self.labels[idx] for idx in indeces]
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extracted_tensor = LabelTensor(new_data, new_labels)
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return extracted_tensor
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def append(self, lt):
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"""
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Return a copy of the merged tensors.
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:param LabelTensor lt: the tensor to merge.
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:return: the merged tensors
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:rtype: LabelTensor
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"""
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if set(self.labels).intersection(lt.labels):
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raise RuntimeError('The tensors to merge have common labels')
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new_labels = self.labels + lt.labels
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new_tensor = torch.cat((self, lt), dim=1)
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return LabelTensor(new_tensor, new_labels)
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def __str__(self):
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s = f'labels({str(self.labels)})\n'
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s += super().__str__()
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return s
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