LabelTensor

class LabelTensor(x, labels, *args, **kwargs)

Bases: Tensor

Torch tensor with a label for any column.

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.

Parameters:

• x (torch.Tensor) – The data tensor.

• labels (str | list(str) | tuple(str)) – The labels of the columns.

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]])

property labels

Property decorator for labels

Returns:

labels of self

Return type:

list

static vstack(label_tensors)

Stack tensors vertically. For more details, see torch.vstack().

Parameters:

label_tensors (list(LabelTensor)) – the tensors to stack. They need to have equal labels.

Returns:

the stacked tensor

Return type:

LabelTensor

clone(*args, **kwargs)

Clone the LabelTensor. For more details, see torch.Tensor.clone().

Returns:

A copy of the tensor.

Return type:

LabelTensor

to(*args, **kwargs)

Performs Tensor dtype and/or device conversion. For more details, see torch.Tensor.to().

select(*args, **kwargs)

Performs Tensor selection. For more details, see torch.Tensor.select().

cuda(*args, **kwargs)

Send Tensor to cuda. For more details, see torch.Tensor.cuda().

cpu(*args, **kwargs)

Send Tensor to cpu. For more details, see torch.Tensor.cpu().

extract(label_to_extract)

Extract the subset of the original tensor by returning all the columns corresponding to the passed label_to_extract.

Parameters:

label_to_extract (str | list(str) | tuple(str)) – The label(s) to extract.

Raises:

• TypeError – Labels are not str.

• ValueError – Label to extract is not in the labels list.

detach()

Returns a new Tensor, detached from the current graph.

The result will never require gradient.

This method also affects forward mode AD gradients and the result will never have forward mode AD gradients.

Note

Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks.

requires_grad_(requires_grad=True) → Tensor

Change if autograd should record operations on this tensor: sets this tensor’s requires_grad attribute in-place. Returns this tensor.

requires_grad_()’s main use case is to tell autograd to begin recording operations on a Tensor tensor. If tensor has requires_grad=False (because it was obtained through a DataLoader, or required preprocessing or initialization), tensor.requires_grad_() makes it so that autograd will begin to record operations on tensor.

Parameters:

requires_grad (bool) – If autograd should record operations on this tensor. Default: True.

Example:

>>> # Let's say we want to preprocess some saved weights and use
>>> # the result as new weights.
>>> saved_weights = [0.1, 0.2, 0.3, 0.25]
>>> loaded_weights = torch.tensor(saved_weights)
>>> weights = preprocess(loaded_weights)  # some function
>>> weights
tensor([-0.5503,  0.4926, -2.1158, -0.8303])

>>> # Now, start to record operations done to weights
>>> weights.requires_grad_()
>>> out = weights.pow(2).sum()
>>> out.backward()
>>> weights.grad
tensor([-1.1007,  0.9853, -4.2316, -1.6606])

append(lt, mode='std')

Return a copy of the merged tensors.

Parameters:

• lt (LabelTensor) – The tensor to merge.

• mode (str) – {‘std’, ‘first’, ‘cross’}

Returns:

The merged tensors.

Return type:

LabelTensor