mindspore.ops.ScaleAndTranslate

class mindspore.ops.ScaleAndTranslate(kernel_type='lanczos3', antialias=True)[source]

Scale And Translate the input image tensor.

Note

  • Input images must be a 4-D tensor.

  • Input size, scale and translation must be a 1-D tensor with two elements.

Parameters

  • kernel_type (str, optional) – Deciding which image filtering algorithm to choose. Valid options: [“lanczos1”, “lanczos3”, “lanczos5”, “gaussian”, “box”, “triangle”, “keyscubic”, “mitchellcubic”] Default: “lanczos3”.

  • antialias (bool, optional) – Deciding whether to use the antialias. Default: True.

Inputs:

  • images (Tensor) - A 4-D tensor of shape \((batch, image\_height, image\_width, channel)\).

  • size (Tensor) - The size of the output image after scale and translate operations. A 1-D tensor with two positive elements whose dtype is int32 and shape must be (2,).

  • scale (Tensor) - Indicates the zoom factor. A 1-D tensor with two positive elements whose dtype is float32 and shape must be (2,).

  • translation (Tensor) - Translate the pixel value. A 1-D tensor with two elements whose dtype is float32 and shape must be (2,).

Outputs:

A 4-D tensor with type: float32 and shape \((batch, size[0], size[1], channel)\).

Raises

  • TypeError – If kernel_type is not str.

  • TypeError – If antialias is not bool.

  • TypeError – If images is not tensor with valid dtype.

  • TypeError – If size is not a tensor of int32.

  • TypeError – If scale is not a tensor of float32.

  • TypeError – If translation is not a tensor of float32.

  • ValueError – If kernel_type is not in [“lanczos1”, “lanczos3”, “lanczos5”, “gaussian”, “box”, “triangle”, “keyscubic”, “mitchellcubic”].

  • ValueError – If the rank of images is not 4.

  • ValueError – If the shape of size is not \((2,)\).

  • ValueError – If the shape of scale is not \((2,)\).

  • ValueError – If the shape of translation is not \((2,)\).

Supported Platforms:

GPU CPU

Examples

>>> op = ops.ScaleAndTranslate()
>>> image = Tensor(np.array([[[[9.0], [5.0], [2.0], [1.0]],
...                           [[6.0], [1.0], [9.0], [7.0]]]]), mindspore.float32)
>>> size = Tensor(np.array([2, 2]).astype(np.int32))
>>> scale = Tensor(np.array([1, 1]).astype(np.float32))
>>> translation = Tensor(np.array([1, 1]).astype(np.float32))
>>> output = op(image, size, scale, translation)
>>> print(output)
[[[[0.]
   [0.]]
  [[0.]
   [9.]]]]