AffineGrid

Description

Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta (https://pytorch.org/docs/stable/generated/torch.nn.functional.affine_grid.html).

An affine matrix theta is applied to a position tensor represented in its homogeneous expression. Here is an example in 3D :

[r00, r01, r02, t0]   [x]   [x']
[r10, r11, r12, t1] * [y] = [y']
[r20, r21, r22, t2]   [z]   [z']
[0,   0,   0,   1 ]   [1]   [1 ]

where (x, y, z) is the position in the original space, (x', y', z') is the position in the output space. The last row is always [0, 0, 0, 1] and is not stored in the affine matrix. Therefore we have theta of shape (N, 2, 3) for 2D or (N, 3, 4) for 3D.

Input size is used to define grid of positions evenly spaced in the original 2D or 3D space, with dimensions ranging from -1 to 1. The output grid contains positions in the output space.

When align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels (mark v in illustration).

v            v            v            v
|-------------------|------------------|
-1                  0                  1

When align_corners=0, consider -1 and 1 to refer to the outer edge of the corner pixels.

    v        v         v         v
|------------------|-------------------|
-1                 0                   1

Input parameters

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

Output parameters

grid (heterogeneous) – T1 : output tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.

Type Constraints

Example