454 lines
16 KiB
Python
454 lines
16 KiB
Python
""" Module for LabelTensor """
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from copy import deepcopy, copy
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import torch
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from torch import Tensor
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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 __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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self.dim_names = None
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self.labels = 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[self.tensor.ndim - 1]['dof']
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@property
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def full_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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""""
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Set properly the parameter _labels
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:param labels: Labels to assign to the class variable _labels.
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:type: labels: str | list(str) | dict
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"""
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if hasattr(self, 'labels') is False:
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self.init_labels()
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if isinstance(labels, dict):
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self.update_labels_from_dict(labels)
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elif isinstance(labels, list):
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self.update_labels_from_list(labels)
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elif isinstance(labels, str):
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labels = [labels]
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self.update_labels_from_list(labels)
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else:
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raise ValueError("labels must be list, dict or string.")
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self.set_names()
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def set_names(self):
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labels = self.full_labels
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self.dim_names = {}
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for dim in range(self.tensor.ndim):
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self.dim_names[labels[dim]['name']] = dim
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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, int)):
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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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return self._extract_from_list(label_to_extract)
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if isinstance(label_to_extract, dict):
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return self._extract_from_dict(label_to_extract)
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raise ValueError('labels_to_extract must be str or list or dict')
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def _extract_from_list(self, labels_to_extract):
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# Store locally all necessary obj/variables
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ndim = self.tensor.ndim
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labels = self.full_labels
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tensor = self.tensor
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last_dim_label = self.labels
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# Verify if all the labels in labels_to_extract are in last dimension
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if set(labels_to_extract).issubset(last_dim_label) is False:
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raise ValueError(
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'Cannot extract a dof which is not in the original LabelTensor')
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# Extract index to extract
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idx_to_extract = [last_dim_label.index(i) for i in labels_to_extract]
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# Perform extraction
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new_tensor = tensor[..., idx_to_extract]
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# Manage labels
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new_labels = copy(labels)
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last_dim_new_label = {ndim - 1: {
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'dof': list(labels_to_extract),
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'name': labels[ndim - 1]['name']
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}}
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new_labels.update(last_dim_new_label)
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return LabelTensor(new_tensor, new_labels)
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def _extract_from_dict(self, labels_to_extract):
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labels = self.full_labels
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tensor = self.tensor
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ndim = tensor.ndim
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new_labels = deepcopy(labels)
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new_tensor = tensor
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for k, _ in labels_to_extract.items():
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idx_dim = self.dim_names[k]
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dim_labels = labels[idx_dim]['dof']
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if isinstance(labels_to_extract[k], (int, str)):
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labels_to_extract[k] = [labels_to_extract[k]]
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if set(labels_to_extract[k]).issubset(dim_labels) is False:
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raise ValueError(
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'Cannot extract a dof which is not in the original '
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'LabelTensor')
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idx_to_extract = [dim_labels.index(i) for i in labels_to_extract[k]]
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indexer = [slice(None)] * idx_dim + [idx_to_extract] + [
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slice(None)] * (ndim - idx_dim - 1)
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new_tensor = new_tensor[indexer]
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dim_new_label = {idx_dim: {
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'dof': labels_to_extract[k],
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'name': labels[idx_dim]['name']
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}}
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new_labels.update(dim_new_label)
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return LabelTensor(new_tensor, new_labels)
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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 cat(tensors, dim=0):
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"""
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Stack a list of tensors. For example, given a tensor `a` of shape
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`(n,m,dof)` and a tensor `b` of dimension `(n',m,dof)`
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the resulting tensor is of shape `(n+n',m,dof)`
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:param tensors: tensors to concatenate
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:type tensors: list(LabelTensor)
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:param dim: dimensions on which you want to perform the operation (default 0)
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:type dim: int
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:rtype: LabelTensor
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:raises ValueError: either number dof or dimensions names differ
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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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new_labels_cat_dim = LabelTensor._check_validity_before_cat(tensors,
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dim)
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# Perform cat on tensors
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new_tensor = torch.cat(tensors, dim=dim)
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# Update labels
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labels = tensors[0].full_labels
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labels.pop(dim)
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new_labels_cat_dim = new_labels_cat_dim if len(
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set(new_labels_cat_dim)) == len(new_labels_cat_dim) \
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else range(new_tensor.shape[dim])
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labels[dim] = {'dof': new_labels_cat_dim,
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'name': tensors[1].full_labels[dim]['name']}
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return LabelTensor(new_tensor, labels)
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@staticmethod
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def _check_validity_before_cat(tensors, dim):
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n_dims = tensors[0].ndim
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new_labels_cat_dim = []
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# Check if names and dof of the labels are the same in all dimensions
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# except in dim
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for i in range(n_dims):
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name = tensors[0].full_labels[i]['name']
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if i != dim:
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dof = tensors[0].full_labels[i]['dof']
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for tensor in tensors:
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dof_to_check = tensor.full_labels[i]['dof']
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name_to_check = tensor.full_labels[i]['name']
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if dof != dof_to_check or name != name_to_check:
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raise ValueError(
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'dimensions must have the same dof and name')
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else:
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for tensor in tensors:
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new_labels_cat_dim += tensor.full_labels[i]['dof']
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name_to_check = tensor.full_labels[i]['name']
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if name != name_to_check:
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raise ValueError(
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'Dimensions to concatenate must have the same name')
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return new_labels_cat_dim
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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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out = LabelTensor(super().clone(*args, **kwargs), self._labels)
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return out
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def init_labels(self):
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self._labels = {
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idx_: {
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'dof': range(self.tensor.shape[idx_]),
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'name': idx_
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} for idx_ in range(self.tensor.ndim)
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}
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def update_labels_from_dict(self, labels):
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"""
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Update the internal label representation according to the values passed
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as input.
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:param labels: The label(s) to update.
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:type labels: dict
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:raises ValueError: dof list contain duplicates or number of dof does
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not match with tensor shape
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"""
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tensor_shape = self.tensor.shape
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# Check dimensionality
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for k, v in labels.items():
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if len(v['dof']) != len(set(v['dof'])):
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raise ValueError("dof must be unique")
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if len(v['dof']) != tensor_shape[k]:
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raise ValueError(
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'Number of dof does not match with tensor dimension')
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# Perform update
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self._labels.update(labels)
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def update_labels_from_list(self, labels):
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"""
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Given a list of dof, this method update the internal label
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representation
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:param labels: The label(s) to update.
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:type labels: list
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"""
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# Create a dict with labels
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last_dim_labels = {
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self.tensor.ndim - 1: {'dof': labels, 'name': self.tensor.ndim - 1}}
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self.update_labels_from_dict(last_dim_labels)
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@staticmethod
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def summation(tensors):
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if len(tensors) == 0:
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raise ValueError('tensors list must not be empty')
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if len(tensors) == 1:
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return tensors[0]
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# Collect all labels
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labels = tensors[0].full_labels
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# Check labels of all the tensors in each dimension
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for j in range(tensors[0].ndim):
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for i in range(1, len(tensors)):
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if labels[j] != tensors[i].full_labels[j]:
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labels.pop(j)
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break
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# Sum tensors
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data = torch.zeros(tensors[0].tensor.shape)
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for tensor in tensors:
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data += tensor.tensor
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new_tensor = LabelTensor(data, labels)
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return new_tensor
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def append(self, tensor, mode='std'):
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if mode == 'std':
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# Call cat on last dimension
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new_label_tensor = LabelTensor.cat([self, tensor],
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dim=self.tensor.ndim - 1)
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elif mode == 'cross':
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# Crete tensor and call cat on last dimension
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tensor1 = self
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tensor2 = tensor
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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(tensor2.repeat_interleave(n1, dim=0),
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labels=tensor2.labels)
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new_label_tensor = LabelTensor.cat([tensor1, tensor2],
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dim=self.tensor.ndim - 1)
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else:
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raise ValueError('mode must be either "std" or "cross"')
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return new_label_tensor
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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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return LabelTensor.cat(label_tensors, dim=0)
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def __getitem__(self, index):
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"""
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TODO: Complete docstring
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:param index:
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:return:
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"""
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if isinstance(index, str) or (isinstance(index, (tuple, list)) and all(
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isinstance(a, str) for a in index)):
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return self.extract(index)
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selected_lt = super().__getitem__(index)
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if isinstance(index, (int, slice)):
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return self._getitem_int_slice(index, selected_lt)
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if len(index) == self.tensor.ndim:
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return self._getitem_full_dim_indexing(index, selected_lt)
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if isinstance(index, torch.Tensor) or (
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isinstance(index, (tuple, list)) and all(
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isinstance(x, int) for x in index)):
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return self._getitem_permutation(index, selected_lt)
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raise ValueError('Not recognized index type')
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def _getitem_int_slice(self, index, selected_lt):
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"""
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:param index:
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:param selected_lt:
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:return:
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"""
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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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new_labels = deepcopy(self.full_labels)
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to_update_dof = new_labels[0]['dof'][index]
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to_update_dof = to_update_dof if isinstance(to_update_dof, (
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tuple, list, range)) else [to_update_dof]
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new_labels.update(
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{0: {'dof': to_update_dof, 'name': new_labels[0]['name']}}
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)
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selected_lt.labels = new_labels
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return selected_lt
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def _getitem_full_dim_indexing(self, index, selected_lt):
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new_labels = {}
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old_labels = self.full_labels
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if selected_lt.ndim == 1:
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selected_lt = selected_lt.reshape(-1, 1)
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new_labels = deepcopy(old_labels)
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new_labels[1].update({'dof': old_labels[1]['dof'][index[1]],
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'name': old_labels[1]['name']})
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idx = 0
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for j in range(selected_lt.ndim):
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if not isinstance(index[j], int):
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if hasattr(self, "labels"):
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new_labels.update(
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self._update_label_for_dim(old_labels, index[j], idx))
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idx += 1
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selected_lt.labels = new_labels
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return selected_lt
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def _getitem_permutation(self, index, selected_lt):
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new_labels = deepcopy(self.full_labels)
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new_labels.update(self._update_label_for_dim(self.full_labels, index,
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0))
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selected_lt.labels = self.labels
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return selected_lt
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@staticmethod
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def _update_label_for_dim(old_labels, index, dim):
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"""
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TODO
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:param old_labels:
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:param index:
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:param dim:
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:return:
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"""
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if isinstance(index, torch.Tensor):
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index = index.nonzero()
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if isinstance(index, list):
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return {dim: {'dof': [old_labels[dim]['dof'][i] for i in index],
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'name': old_labels[dim]['name']}}
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else:
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return {dim: {'dof': old_labels[dim]['dof'][index],
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'name': old_labels[dim]['name']}}
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def sort_labels(self, dim=None):
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def argsort(lst):
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return sorted(range(len(lst)), key=lambda x: lst[x])
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if dim is None:
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dim = self.tensor.ndim - 1
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labels = self.full_labels[dim]['dof']
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sorted_index = argsort(labels)
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indexer = [slice(None)] * self.tensor.ndim
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indexer[dim] = sorted_index
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new_labels = deepcopy(self.full_labels)
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new_labels[dim] = {'dof': sorted(labels),
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'name': new_labels[dim]['name']}
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return LabelTensor(self.tensor[indexer], new_labels)
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