affine_grid¶

paddle.nn.functional. affine_grid ( theta, out_shape, align_corners=True, name=None ) [source]

It generates a grid of (x,y) or (x,y,z) coordinates using the parameters of the affine transformation that correspond to a set of points where the input feature map should be sampled to produce the transformed output feature map.

Parameters

• theta (Tensor) – The data type can be float32 or float64.

• out_shape (Tensor | list | tuple) – Type can be a 1-D Tensor, list, or tuple. It is used to represent the shape of the output in an affine transformation, in the format [N, C, H, W] or [N, C, D, H, W]. When the format is [N, C, H, W], it represents the batch size, number of channels, height and width. When the format is [N, C, D, H, W], it represents the batch size, number of channels, depth, height and width. The data type must be int32.

• align_corners (bool, optional) – if True, aligns the centers of the 4 (4D) or 8 (5D) corner pixels of the input and output tensors, and preserves the value of the corner pixels. Default: True

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.

Returns

Tensor, A Tensor with shape [batch_size, H, W, 2] or [batch, D, H, W, 3] while (‘D’)’H’, ‘W’ are the (depth)height, width of feature map in affine transformation. The data type is the same as theta.

Examples

import paddle
import paddle.nn.functional as F
# theta shape = [1, 2, 3]
theta = paddle.to_tensor([[[-0.7, -0.4, 0.3],
                           [ 0.6,  0.5, 1.5]]], dtype="float32")
y_t = F.affine_grid(
        theta,
        [1, 2, 3, 3],
        align_corners=False)
print(y_t)

#[[[[ 1.0333333   0.76666665]
#   [ 0.76666665  1.0999999 ]
#   [ 0.5         1.4333333 ]]
#
#  [[ 0.5666667   1.1666666 ]
#   [ 0.3         1.5       ]
#   [ 0.03333333  1.8333334 ]]
#
#  [[ 0.10000002  1.5666667 ]
#   [-0.16666666  1.9000001 ]
#   [-0.43333334  2.2333333 ]]]]