465 lines
15 KiB
Python
465 lines
15 KiB
Python
""" Module for LabelTensor """
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from copy import deepcopy
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import torch
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from torch import Tensor
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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 | list(str) | tuple(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['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 __deepcopy__(self, __):
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# """
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# Implements deepcopy for label tensor. By default it stores the
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# current labels and use the :meth:`~torch._tensor.Tensor.__deepcopy__`
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# method for creating a new :class:`pina.label_tensor.LabelTensor`.
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# :param __: Placeholder parameter.
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# :type __: None
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# :return: The deep copy of the :class:`pina.label_tensor.LabelTensor`.
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# :rtype: LabelTensor
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# """
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# labels = self.labels
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# copy_tensor = deepcopy(self.tensor)
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# return LabelTensor(copy_tensor, labels)
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# @property
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# def labels(self):
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# """Property decorator for labels
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# :return: labels of self
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# :rtype: list
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# """
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# return self._labels
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# @labels.setter
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# def labels(self, labels):
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# if len(labels) != self.shape[self.ndim - 1]: # small check
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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 # assign the label
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# @staticmethod
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# def vstack(label_tensors):
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# """
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# Stack tensors vertically. For more details, see
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# :meth:`torch.vstack`.
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# :param list(LabelTensor) label_tensors: the tensors to stack. They need
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# to have equal labels.
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# :return: the stacked tensor
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# :rtype: LabelTensor
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# """
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# if len(label_tensors) == 0:
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# return []
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# all_labels = [label for lt in label_tensors for label in lt.labels]
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# if set(all_labels) != set(label_tensors[0].labels):
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# raise RuntimeError("The tensors to stack have different labels")
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# labels = label_tensors[0].labels
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# tensors = [lt.extract(labels) for lt in label_tensors]
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# return LabelTensor(torch.vstack(tensors), 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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# # # used before merging
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# # try:
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# # out = LabelTensor(super().clone(*args, **kwargs), self.labels)
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# # except:
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# # out = super().clone(*args, **kwargs)
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# out = LabelTensor(super().clone(*args, **kwargs), self.labels)
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# return out
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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 select(self, *args, **kwargs):
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# """
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# Performs Tensor selection. For more details, see :meth:`torch.Tensor.select`.
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# """
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# tmp = super().select(*args, **kwargs)
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# tmp._labels = self._labels
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# return tmp
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# def cuda(self, *args, **kwargs):
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# """
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# Send Tensor to cuda. For more details, see :meth:`torch.Tensor.cuda`.
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# """
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# tmp = super().cuda(*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 cpu(self, *args, **kwargs):
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# """
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# Send Tensor to cpu. For more details, see :meth:`torch.Tensor.cpu`.
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# """
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# tmp = super().cpu(*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 | list(str) | tuple(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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# )
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# indeces = []
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# for f in label_to_extract:
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# try:
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# indeces.append(self.labels.index(f))
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# except ValueError:
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# raise ValueError(f"`{f}` not in the labels list")
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# new_data = super(Tensor, self.T).__getitem__(indeces).T
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# new_labels = [self.labels[idx] for idx in indeces]
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# extracted_tensor = new_data.as_subclass(LabelTensor)
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# extracted_tensor.labels = new_labels
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# return extracted_tensor
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# def detach(self):
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# detached = super().detach()
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# if hasattr(self, "_labels"):
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# detached._labels = self._labels
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# return detached
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# def append(self, lt, mode="std"):
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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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# :param str mode: {'std', 'first', 'cross'}
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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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# if mode == "std":
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# new_tensor = torch.cat((self, lt), dim=1)
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# elif mode == "first":
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# raise NotImplementedError
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# elif mode == "cross":
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# tensor1 = self
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# tensor2 = lt
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# n1 = tensor1.shape[0]
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# n2 = tensor2.shape[0]
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# tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
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# tensor2 = LabelTensor(
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# tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels
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# )
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# new_tensor = torch.cat((tensor1, tensor2), dim=1)
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# new_tensor = new_tensor.as_subclass(LabelTensor)
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# new_tensor.labels = new_labels
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# return new_tensor
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# def __getitem__(self, index):
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# """
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# Return a copy of the selected tensor.
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# """
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# if isinstance(index, str) or (
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# isinstance(index, (tuple, list))
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# and all(isinstance(a, str) for a in index)
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# ):
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# return self.extract(index)
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# selected_lt = super(Tensor, self).__getitem__(index)
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# try:
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# len_index = len(index)
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# except TypeError:
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# len_index = 1
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# if isinstance(index, int) or len_index == 1:
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# if selected_lt.ndim == 1:
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# selected_lt = selected_lt.reshape(1, -1)
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# if hasattr(self, "labels"):
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# selected_lt.labels = self.labels
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# elif len_index == 2:
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# if selected_lt.ndim == 1:
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# selected_lt = selected_lt.reshape(-1, 1)
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# if hasattr(self, "labels"):
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# if isinstance(index[1], list):
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# selected_lt.labels = [self.labels[i] for i in index[1]]
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# else:
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# selected_lt.labels = self.labels[index[1]]
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# else:
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# selected_lt.labels = self.labels
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# return selected_lt
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# def __str__(self):
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# if hasattr(self, "labels"):
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# s = f"labels({str(self.labels)})\n"
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# else:
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# s = "no labels\n"
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# s += super().__str__()
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# return s
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def issubset(a, b):
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"""
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Check if a is a subset of b.
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"""
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return set(a).issubset(set(b))
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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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@property
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def tensor(self):
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return self.as_subclass(Tensor)
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def __len__(self) -> int:
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return super().__len__()
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def __init__(self, x, labels):
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"""
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Construct a `LabelTensor` by passing a dict of the labels
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:Example:
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>>> from pina import LabelTensor
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>>> tensor = LabelTensor(
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>>> torch.rand((2000, 3)),
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{1: {"name": "space"['a', 'b', 'c'])
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"""
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from .utils import check_consistency
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check_consistency(labels, dict)
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self.labels = {
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idx_: {
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'dof': range(x.shape[idx_]),
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'name': idx_
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} for idx_ in range(x.ndim)
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}
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self.labels.update(labels)
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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 | list(str) | tuple(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, (int, str)):
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label_to_extract = [label_to_extract]
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if isinstance(label_to_extract, (tuple, list)):
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for k, v in self.labels.items():
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if issubset(label_to_extract, v['dof']):
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break
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label_to_extract = {v['name']: label_to_extract}
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for k, v in label_to_extract.items():
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if isinstance(v, (int, str)):
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label_to_extract[k] = [v]
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indeces = []
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for dim in range(self.ndim):
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boolean_idx = [True] * self.shape[dim]
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for dim_to_extract, dof_to_extract in label_to_extract.items():
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if dim_to_extract == self.labels[dim]['name']:
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boolean_idx = [False] * self.shape[dim]
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for label in dof_to_extract:
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idx_to_keep = self.labels[dim]['dof'].index(label)
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boolean_idx[idx_to_keep] = True
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boolean_idx = torch.Tensor(boolean_idx).bool()
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indeces.append(boolean_idx)
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final_shapes = [sum(idx) for idx in indeces]
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grids = torch.meshgrid(*indeces)
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ii = grids[0]
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for grid in grids[1:]:
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ii = torch.logical_and(ii, grid)
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new_tensor = self.tensor[ii].reshape(*final_shapes)
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return LabelTensor(new_tensor, label_to_extract)
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def __str__(self):
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"""
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returns a string with the representation of the class
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"""
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s = ''
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for key, value in self.labels.items():
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s += f"{key}: {value}\n"
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s += '\n'
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s += super().__str__()
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return s
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@staticmethod
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def stack(tensors):
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"""
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"""
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if len(tensors) == 0:
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return []
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if len(tensors) == 1:
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return tensors[0]
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raise NotImplementedError
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labels = [tensor.labels for tensor in tensors]
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print(labels)
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def requires_grad_(self, mode=True):
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lt = super().requires_grad_(mode)
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lt.labels = self.labels
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return lt
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@property
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def dtype(self):
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return super().dtype
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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 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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# # used before merging
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# try:
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# out = LabelTensor(super().clone(*args, **kwargs), self.labels)
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# except:
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# out = super().clone(*args, **kwargs)
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out = LabelTensor(super().clone(*args, **kwargs), self.labels)
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return out |