mindspore.ops.ResizeBilinear

class mindspore.ops.ResizeBilinear(*args, **kwargs)[source]

Resizes an image to a certain size using the bilinear interpolation.

The resizing only affects the lower two dimensions which represent the height and width. The input images can be represented by different data types, but the data types of output images are always float32.

Parameters

  • size (Union[tuple[int], list[int]]) – A tuple or list of 2 int elements (new_height, new_width), the new size of the images.

  • align_corners (bool) – If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Default: False.

Inputs:

  • input (Tensor) - Image to be resized. Input images must be a 4-D tensor with shape \((batch, channels, height, width)\), with data type of float32 or float16.

Outputs:

Tensor, resized image. 4-D with shape [batch, channels, new_height, new_width] in float32.

Raises

  • TypeError – If size is neither a tuple nor list.

  • TypeError – If align_corners is not a bool.

  • TypeError – If dtype of input is neither float16 nor float32.

  • TypeError – If input is not a Tensor.

  • ValueError – If length of shape of input is not equal to 4.

Supported Platforms:

Ascend CPU GPU

Examples

>>> tensor = Tensor([[[[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]]], mindspore.float32)
>>> resize_bilinear = ops.ResizeBilinear((5, 5))
>>> output = resize_bilinear(tensor)
>>> print(output)
[[[[1. 2. 3. 4. 5.]
   [1. 2. 3. 4. 5.]
   [1. 2. 3. 4. 5.]
   [1. 2. 3. 4. 5.]
   [1. 2. 3. 4. 5.]]]]