mindspore.ops.ResizeBilinear

class mindspore.ops.ResizeBilinear(size, align_corners=False, half_pixel_centers=False)[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.

For general resize, refer to mindspore.ops.interpolate() for more details.

Warning

This interface does not support dynamic shape and is subject to change or deletion, use mindspore.ops.interpolate() instead.

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.

  • half_pixel_centers (bool) – Whether half pixel center. If set to True, align_corners should be False. Default: False.

Inputs:

  • x (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 \((batch, channels, new\_height, new\_width)\), with the same data type as input x.

Raises

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

  • TypeError – If align_corners is not a bool.

  • TypeError – If half_pixel_centers is not a bool.

  • TypeError – If align_corners and half_pixel_centers are all True.

  • TypeError – If half_pixel_centers is True and device_target not Ascend.

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

  • TypeError – If x is not a Tensor.

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

Supported Platforms:

Ascend CPU GPU

Examples

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