mindspore.ops.affine_grid

mindspore.ops.affine_grid(theta, size, align_corners=False)[source]

Returns a 2D or 3D flow field (sampling grid) based on theta, a batch of affine matrices.

Parameters

theta (Tensor) – The input tensor of flow field whose dtype is float16, float32. Input batch of affine matrices with shape $(N, 2, 3)$ for 2D grid or $(N, 3, 4)$ for 3D grid.
size (tuple[int]) – The target output image size. The value of target output with format $(N, C, H, W)$ for 2D grid or $(N, C, D, H, W)$ for 3D grid.
align_corners (bool, optional) – Geometrically, each pixel of input is viewed as a squqre instead of dot. If True , consider extremum -1 and 1 referring to the centers of the pixels rather than pixel corners. The default value is False , extremum -1 and 1 refer to the corners of the pixels, so that sampling is irrelevant to resolution of the image. Default: False .

Returns

Tensor, a tensor whose data type is same as 'theta', and the shape is $(N, H, W, 2)$ for 2D grid or $(N, D, H, W, 3)$ for 3D grid.

Raises

TypeError – If theta is not a Tensor or size is not a tuple.
ValueError – If the shape of theta is not $(N, 2, 3)$ or $(N, 3, 4)$ .
ValueError – If the size of size is not 4 or 5.
ValueError – If the shape of theta is $(N, 2, 3)$ , the size of size is not 4; If the shape of theta is $(N, 3, 4)$ , the size of size is not 5.
ValueError – If the size[0] is not equal to the shape[0] of theta.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> theta = Tensor([[[0.8, 0.5, 0],[-0.5, 0.8, 0]]], mindspore.float32)
>>> out_size = (1, 3, 2, 3)
>>> output = ops.affine_grid(theta, out_size, False)
>>> print(output)
[[[[-0.78333336 -0.06666666]
[-0.25       -0.4       ]
[ 0.28333336 -0.73333335]]
[[-0.28333336  0.73333335]
[ 0.25        0.4       ]
[ 0.78333336  0.06666666]]]]