einops.repeat

einops.repeat allows reordering elements and repeating them in arbitrary combinations. This operation includes functionality of repeat, tile, and broadcast functions.

Examples for repeat operation:

# a grayscale image (of shape height x width)
>>> image = np.random.randn(30, 40)

# change it to RGB format by repeating in each channel
>>> repeat(image, 'h w -> h w c', c=3).shape
(30, 40, 3)

# repeat image 2 times along height (vertical axis)
>>> repeat(image, 'h w -> (repeat h) w', repeat=2).shape
(60, 40)

# repeat image 2 time along height and 3 times along width
>>> repeat(image, 'h w -> (h2 h) (w3 w)', h2=2, w3=3).shape
(60, 120)

# convert each pixel to a small square 2x2, i.e. upsample an image by 2x
>>> repeat(image, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape
(60, 80)

# 'pixelate' an image first by downsampling by 2x, then upsampling
>>> downsampled = reduce(image, '(h h2) (w w2) -> h w', 'mean', h2=2, w2=2)
>>> repeat(downsampled, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape
(30, 40)

When composing axes, C-order enumeration used (consecutive elements have different last axis). Find more examples in einops tutorial.

Parameters:

Name	Type	Description	Default
tensor	Union[Tensor, List[Tensor]]	tensor of any supported library (e.g. numpy.ndarray, tensorflow, pytorch). list of tensors is also accepted, those should be of the same type and shape	required
pattern	str	string, rearrangement pattern	required
axes_lengths	Size	any additional specifications for dimensions	{}

Returns:

Type	Description
Tensor	Tensor of the same type as input. If possible, a view to the original tensor is returned.

Source code in einops/einops.py

def repeat(tensor: Union[Tensor, List[Tensor]], pattern: str, **axes_lengths: Size) -> Tensor:
    """
    einops.repeat allows reordering elements and repeating them in arbitrary combinations.
    This operation includes functionality of repeat, tile, and broadcast functions.

    Examples for repeat operation:

    ```python
    # a grayscale image (of shape height x width)
    >>> image = np.random.randn(30, 40)

    # change it to RGB format by repeating in each channel
    >>> repeat(image, 'h w -> h w c', c=3).shape
    (30, 40, 3)

    # repeat image 2 times along height (vertical axis)
    >>> repeat(image, 'h w -> (repeat h) w', repeat=2).shape
    (60, 40)

    # repeat image 2 time along height and 3 times along width
    >>> repeat(image, 'h w -> (h2 h) (w3 w)', h2=2, w3=3).shape
    (60, 120)

    # convert each pixel to a small square 2x2, i.e. upsample an image by 2x
    >>> repeat(image, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape
    (60, 80)

    # 'pixelate' an image first by downsampling by 2x, then upsampling
    >>> downsampled = reduce(image, '(h h2) (w w2) -> h w', 'mean', h2=2, w2=2)
    >>> repeat(downsampled, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape
    (30, 40)

    ```

    When composing axes, C-order enumeration used (consecutive elements have different last axis).
    Find more examples in einops tutorial.

    Parameters:
        tensor: tensor of any supported library (e.g. numpy.ndarray, tensorflow, pytorch).
            list of tensors is also accepted, those should be of the same type and shape
        pattern: string, rearrangement pattern
        axes_lengths: any additional specifications for dimensions

    Returns:
        Tensor of the same type as input. If possible, a view to the original tensor is returned.

    """
    return reduce(tensor, pattern, reduction="repeat", **axes_lengths)