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Implement definition of LabelTensor from list, implement cat method (previously stack) and re-implement extract

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FilippoOlivo
2024-09-28 12:23:16 +02:00
committed by Nicola Demo
parent a779007b36
commit c53c3d5b84
2 changed files with 105 additions and 473 deletions
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pina/label_tensor.py
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@@ -1,311 +1,8 @@
""" Module for LabelTensor """
from copy import deepcopy
import torch
from torch import Tensor
# class LabelTensor(torch.Tensor):
# """Torch tensor with a label for any column."""
# @staticmethod
# def __new__(cls, x, labels, *args, **kwargs):
# return super().__new__(cls, x, *args, **kwargs)
# def __init__(self, x, labels):
# """
# Construct a `LabelTensor` by passing a tensor and a list of column
# labels. Such labels uniquely identify the columns of the tensor,
# allowing for an easier manipulation.
# :param torch.Tensor x: The data tensor.
# :param labels: The labels of the columns.
# :type labels: str | list(str) | tuple(str)
# :Example:
# >>> from pina import LabelTensor
# >>> tensor = LabelTensor(torch.rand((2000, 3)), ['a', 'b', 'c'])
# >>> tensor
# tensor([[6.7116e-02, 4.8892e-01, 8.9452e-01],
# [9.2392e-01, 8.2065e-01, 4.1986e-04],
# [8.9266e-01, 5.5446e-01, 6.3500e-01],
# ...,
# [5.8194e-01, 9.4268e-01, 4.1841e-01],
# [1.0246e-01, 9.5179e-01, 3.7043e-02],
# [9.6150e-01, 8.0656e-01, 8.3824e-01]])
# >>> tensor.extract('a')
# tensor([[0.0671],
# [0.9239],
# [0.8927],
# ...,
# [0.5819],
# [0.1025],
# [0.9615]])
# >>> tensor['a']
# tensor([[0.0671],
# [0.9239],
# [0.8927],
# ...,
# [0.5819],
# [0.1025],
# [0.9615]])
# >>> tensor.extract(['a', 'b'])
# tensor([[0.0671, 0.4889],
# [0.9239, 0.8207],
# [0.8927, 0.5545],
# ...,
# [0.5819, 0.9427],
# [0.1025, 0.9518],
# [0.9615, 0.8066]])
# >>> tensor.extract(['b', 'a'])
# tensor([[0.4889, 0.0671],
# [0.8207, 0.9239],
# [0.5545, 0.8927],
# ...,
# [0.9427, 0.5819],
# [0.9518, 0.1025],
# [0.8066, 0.9615]])
# """
# if x.ndim == 1:
# x = x.reshape(-1, 1)
# if isinstance(labels, str):
# labels = [labels]
# if len(labels) != x.shape[-1]:
# raise ValueError(
# "the tensor has not the same number of columns of "
# "the passed labels."
# )
# self._labels = labels
# def __deepcopy__(self, __):
# """
# Implements deepcopy for label tensor. By default it stores the
# current labels and use the :meth:`~torch._tensor.Tensor.__deepcopy__`
# method for creating a new :class:`pina.label_tensor.LabelTensor`.
# :param __: Placeholder parameter.
# :type __: None
# :return: The deep copy of the :class:`pina.label_tensor.LabelTensor`.
# :rtype: LabelTensor
# """
# labels = self.labels
# copy_tensor = deepcopy(self.tensor)
# return LabelTensor(copy_tensor, labels)
# @property
# def labels(self):
# """Property decorator for labels
# :return: labels of self
# :rtype: list
# """
# return self._labels
# @labels.setter
# def labels(self, labels):
# if len(labels) != self.shape[self.ndim - 1]: # small check
# raise ValueError(
# "The tensor has not the same number of columns of "
# "the passed labels."
# )
# self._labels = labels # assign the label
# @staticmethod
# def vstack(label_tensors):
# """
# Stack tensors vertically. For more details, see
# :meth:`torch.vstack`.
# :param list(LabelTensor) label_tensors: the tensors to stack. They need
# to have equal labels.
# :return: the stacked tensor
# :rtype: LabelTensor
# """
# if len(label_tensors) == 0:
# return []
# all_labels = [label for lt in label_tensors for label in lt.labels]
# if set(all_labels) != set(label_tensors[0].labels):
# raise RuntimeError("The tensors to stack have different labels")
# labels = label_tensors[0].labels
# tensors = [lt.extract(labels) for lt in label_tensors]
# return LabelTensor(torch.vstack(tensors), labels)
# def clone(self, *args, **kwargs):
# """
# Clone the LabelTensor. For more details, see
# :meth:`torch.Tensor.clone`.
# :return: A copy of the tensor.
# :rtype: LabelTensor
# """
# # # used before merging
# # try:
# # out = LabelTensor(super().clone(*args, **kwargs), self.labels)
# # except:
# # out = super().clone(*args, **kwargs)
# out = LabelTensor(super().clone(*args, **kwargs), self.labels)
# return out
# def to(self, *args, **kwargs):
# """
# Performs Tensor dtype and/or device conversion. For more details, see
# :meth:`torch.Tensor.to`.
# """
# tmp = super().to(*args, **kwargs)
# new = self.__class__.clone(self)
# new.data = tmp.data
# return new
# def select(self, *args, **kwargs):
# """
# Performs Tensor selection. For more details, see :meth:`torch.Tensor.select`.
# """
# tmp = super().select(*args, **kwargs)
# tmp._labels = self._labels
# return tmp
# def cuda(self, *args, **kwargs):
# """
# Send Tensor to cuda. For more details, see :meth:`torch.Tensor.cuda`.
# """
# tmp = super().cuda(*args, **kwargs)
# new = self.__class__.clone(self)
# new.data = tmp.data
# return new
# def cpu(self, *args, **kwargs):
# """
# Send Tensor to cpu. For more details, see :meth:`torch.Tensor.cpu`.
# """
# tmp = super().cpu(*args, **kwargs)
# new = self.__class__.clone(self)
# new.data = tmp.data
# return new
# def extract(self, label_to_extract):
# """
# Extract the subset of the original tensor by returning all the columns
# corresponding to the passed ``label_to_extract``.
# :param label_to_extract: The label(s) to extract.
# :type label_to_extract: str | list(str) | tuple(str)
# :raises TypeError: Labels are not ``str``.
# :raises ValueError: Label to extract is not in the labels ``list``.
# """
# if isinstance(label_to_extract, str):
# label_to_extract = [label_to_extract]
# elif isinstance(label_to_extract, (tuple, list)): # TODO
# pass
# else:
# raise TypeError(
# "`label_to_extract` should be a str, or a str iterator"
# )
# indeces = []
# for f in label_to_extract:
# try:
# indeces.append(self.labels.index(f))
# except ValueError:
# raise ValueError(f"`{f}` not in the labels list")
# new_data = super(Tensor, self.T).__getitem__(indeces).T
# new_labels = [self.labels[idx] for idx in indeces]
# extracted_tensor = new_data.as_subclass(LabelTensor)
# extracted_tensor.labels = new_labels
# return extracted_tensor
# def detach(self):
# detached = super().detach()
# if hasattr(self, "_labels"):
# detached._labels = self._labels
# return detached
# def append(self, lt, mode="std"):
# """
# Return a copy of the merged tensors.
# :param LabelTensor lt: The tensor to merge.
# :param str mode: {'std', 'first', 'cross'}
# :return: The merged tensors.
# :rtype: LabelTensor
# """
# if set(self.labels).intersection(lt.labels):
# raise RuntimeError("The tensors to merge have common labels")
# new_labels = self.labels + lt.labels
# if mode == "std":
# new_tensor = torch.cat((self, lt), dim=1)
# elif mode == "first":
# raise NotImplementedError
# elif mode == "cross":
# tensor1 = self
# tensor2 = lt
# n1 = tensor1.shape[0]
# n2 = tensor2.shape[0]
# tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
# tensor2 = LabelTensor(
# tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels
# )
# new_tensor = torch.cat((tensor1, tensor2), dim=1)
# new_tensor = new_tensor.as_subclass(LabelTensor)
# new_tensor.labels = new_labels
# return new_tensor
# def __getitem__(self, index):
# """
# Return a copy of the selected tensor.
# """
# if isinstance(index, str) or (
# isinstance(index, (tuple, list))
# and all(isinstance(a, str) for a in index)
# ):
# return self.extract(index)
# selected_lt = super(Tensor, self).__getitem__(index)
# try:
# len_index = len(index)
# except TypeError:
# len_index = 1
# if isinstance(index, int) or len_index == 1:
# if selected_lt.ndim == 1:
# selected_lt = selected_lt.reshape(1, -1)
# if hasattr(self, "labels"):
# selected_lt.labels = self.labels
# elif len_index == 2:
# if selected_lt.ndim == 1:
# selected_lt = selected_lt.reshape(-1, 1)
# if hasattr(self, "labels"):
# if isinstance(index[1], list):
# selected_lt.labels = [self.labels[i] for i in index[1]]
# else:
# selected_lt.labels = self.labels[index[1]]
# else:
# selected_lt.labels = self.labels
# return selected_lt
# def __str__(self):
# if hasattr(self, "labels"):
# s = f"labels({str(self.labels)})\n"
# else:
# s = "no labels\n"
# s += super().__str__()
# return s
def issubset(a, b):
"""
Check if a is a subset of b.
@@ -334,21 +31,19 @@ class LabelTensor(torch.Tensor):
:Example:
>>> from pina import LabelTensor
>>> tensor = LabelTensor(
>>> torch.rand((2000, 3)),
>>> torch.rand((2000, 3)),
{1: {"name": "space"['a', 'b', 'c'])
"""
from .utils import check_consistency
check_consistency(labels, dict)
self.labels = {
idx_: {
'dof': range(x.shape[idx_]),
'name': idx_
} for idx_ in range(x.ndim)
}
self.labels.update(labels)
if isinstance(labels, dict):
# check_consistency(labels, dict)
self.update_labels(labels)
elif isinstance(labels, list):
self.init_labels_from_list(labels)
elif isinstance(labels, str):
labels = [labels]
raise ValueError(f"labels must be list, dict or string.")
def extract(self, label_to_extract):
"""
@@ -360,47 +55,48 @@ class LabelTensor(torch.Tensor):
:raises TypeError: Labels are not ``str``.
:raises ValueError: Label to extract is not in the labels ``list``.
"""
if isinstance(label_to_extract, (int, str)):
from copy import deepcopy
if isinstance(label_to_extract, (str, int)):
label_to_extract = [label_to_extract]
if isinstance(label_to_extract, (tuple, list)):
for k, v in self.labels.items():
if issubset(label_to_extract, v['dof']):
break
last_dim_label = self.labels[self.tensor.ndim - 1]['dof']
if set(label_to_extract).issubset(last_dim_label) is False:
raise ValueError('Cannot extract a dof which is not in the original LabelTensor')
idx_to_extract = [last_dim_label.index(i) for i in label_to_extract]
new_tensor = deepcopy(self.tensor)
new_tensor = new_tensor[..., idx_to_extract]
new_labels = deepcopy(self.labels)
last_dim_new_label = {self.tensor.ndim - 1: {
'dof': label_to_extract,
'name': self.labels[self.tensor.ndim - 1]['name']
}}
new_labels.update(last_dim_new_label)
elif isinstance(label_to_extract, dict):
new_labels = (deepcopy(self.labels))
new_tensor = deepcopy(self.tensor)
for k, v in label_to_extract.items():
idx_dim = None
for kl, vl in self.labels.items():
if vl['name'] == k:
idx_dim = kl
break
dim_labels = self.labels[idx_dim]['dof']
if isinstance(label_to_extract[k], (int, str)):
label_to_extract[k] = [label_to_extract[k]]
if set(label_to_extract[k]).issubset(dim_labels) is False:
raise ValueError('Cannot extract a dof which is not in the original labeltensor')
idx_to_extract = [dim_labels.index(i) for i in label_to_extract[k]]
indexer = [slice(None)] * idx_dim + [idx_to_extract] + [slice(None)] * (self.tensor.ndim - idx_dim - 1)
new_tensor = new_tensor[indexer]
dim_new_label = {idx_dim: {
'dof': label_to_extract[k],
'name': self.labels[idx_dim]['name']
}}
new_labels.update(dim_new_label)
else:
raise ValueError('labels_to_extract must be str or list or dict')
return LabelTensor(new_tensor, new_labels)
label_to_extract = {v['name']: label_to_extract}
for k, v in label_to_extract.items():
if isinstance(v, (int, str)):
label_to_extract[k] = [v]
indeces = []
for dim in range(self.ndim):
boolean_idx = [True] * self.shape[dim]
for dim_to_extract, dof_to_extract in label_to_extract.items():
if dim_to_extract == self.labels[dim]['name']:
boolean_idx = [False] * self.shape[dim]
for label in dof_to_extract:
idx_to_keep = self.labels[dim]['dof'].index(label)
boolean_idx[idx_to_keep] = True
boolean_idx = torch.Tensor(boolean_idx).bool()
indeces.append(boolean_idx)
final_shapes = [sum(idx) for idx in indeces]
grids = torch.meshgrid(*indeces)
ii = grids[0]
for grid in grids[1:]:
ii = torch.logical_and(ii, grid)
new_tensor = self.tensor[ii].reshape(*final_shapes)
return LabelTensor(new_tensor, label_to_extract)
def __str__(self):
"""
returns a string with the representation of the class
@@ -409,23 +105,45 @@ class LabelTensor(torch.Tensor):
s = ''
for key, value in self.labels.items():
s += f"{key}: {value}\n"
s += '\n'
s += '\n'
s += super().__str__()
return s
@staticmethod
def stack(tensors):
"""
def cat(tensors, dim=0):
"""
Stack a list of tensors. For example, given a tensor `a` of shape `(n,m,dof)` and a tensor `b` of dimension `(n',m,dof)`
the resulting tensor is of shape `(n+n',m,dof)`
"""
if len(tensors) == 0:
return []
if len(tensors) == 1:
return tensors[0]
raise NotImplementedError
labels = [tensor.labels for tensor in tensors]
n_dims = tensors[0].ndim
new_labels_cat_dim = []
for i in range(n_dims):
name = tensors[0].labels[i]['name']
if i != dim:
dof = tensors[0].labels[i]['dof']
for tensor in tensors:
dof_to_check = tensor.labels[i]['dof']
name_to_check = tensor.labels[i]['name']
if dof != dof_to_check or name != name_to_check:
raise ValueError('dimensions must have the same dof and name')
else:
for tensor in tensors:
new_labels_cat_dim += tensor.labels[i]['dof']
name_to_check = tensor.labels[i]['name']
if name != name_to_check:
raise ValueError('dimensions must have the same dof and name')
new_tensor = torch.cat(tensors, dim=dim)
labels = tensors[0].labels
labels.pop(dim)
new_labels_cat_dim = new_labels_cat_dim if len(set(new_labels_cat_dim)) == len(new_labels_cat_dim) \
else range(new_tensor.shape[dim])
labels[dim] = {'dof': new_labels_cat_dim,
'name': tensors[1].labels[dim]['name']}
return LabelTensor(new_tensor, labels)
def requires_grad_(self, mode=True):
lt = super().requires_grad_(mode)
@@ -454,10 +172,25 @@ class LabelTensor(torch.Tensor):
:return: A copy of the tensor.
:rtype: LabelTensor
"""
# # used before merging
# try:
# out = LabelTensor(super().clone(*args, **kwargs), self.labels)
# except:
# out = super().clone(*args, **kwargs)
out = LabelTensor(super().clone(*args, **kwargs), self.labels)
return out
return out
def update_labels(self, labels):
self.labels = {
idx_: {
'dof': range(self.tensor.shape[idx_]),
'name': idx_
} for idx_ in range(self.tensor.ndim)
}
tensor_shape = self.tensor.shape
for k, v in labels.items():
if len(v['dof']) != len(set(v['dof'])):
raise ValueError("dof must be unique")
if len(v['dof']) != tensor_shape[k]:
raise ValueError('Number of dof does not match with tensor dimension')
self.labels.update(labels)
def init_labels_from_list(self, labels):
last_dim_labels = {self.tensor.ndim - 1: {'dof': labels, 'name': self.tensor.ndim - 1}}
self.update_labels(last_dim_labels)