_transpose

transpose

Transpose the tensor.

This method can be used with Numpy data:

n = np.array([[0,1,2],[3,4,5],[6,7,8]])
b = fe.backend.transpose(n)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]

This method can be used with TensorFlow tensors:

t = tf.constant([[0,1,2],[3,4,5],[6,7,8]])
b = fe.backend.transpose(t)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]

This method can be used with PyTorch tensors:

p = torch.tensor([[0,1,2],[3,4,5],[6,7,8]])
b = fe.backend.transpose(p)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]

Parameters:

Name	Type	Description	Default
tensor	Tensor	The input value.	required

Returns:

Type	Description
Tensor	The transposed tensor.

Raises:

Type	Description
ValueError	If tensor is an unacceptable data type.

Source code in fastestimator/fastestimator/backend/_transpose.py

def transpose(tensor: Tensor) -> Tensor:
    """Transpose the `tensor`.

    This method can be used with Numpy data:
    ```python
    n = np.array([[0,1,2],[3,4,5],[6,7,8]])
    b = fe.backend.transpose(n)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]
    ```

    This method can be used with TensorFlow tensors:
    ```python
    t = tf.constant([[0,1,2],[3,4,5],[6,7,8]])
    b = fe.backend.transpose(t)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]
    ```

    This method can be used with PyTorch tensors:
    ```python
    p = torch.tensor([[0,1,2],[3,4,5],[6,7,8]])
    b = fe.backend.transpose(p)  # [[0, 3, 6], [1, 4, 7], [2, 5, 8]]
    ```

    Args:
        tensor: The input value.

    Returns:
        The transposed `tensor`.

    Raises:
        ValueError: If `tensor` is an unacceptable data type.
    """
    if tf.is_tensor(tensor):
        return tf.transpose(tensor)
    elif isinstance(tensor, torch.Tensor):
        return tensor.T
    elif isinstance(tensor, np.ndarray):
        return np.transpose(tensor)
    else:
        raise ValueError("Unrecognized tensor type {}".format(type(tensor)))