tfg.geometry.representation.grid.generate

Generates a M-D uniform axis-aligned grid.

tfg.geometry.representation.grid.generate(
    starts, stops, nums, name='grid_generate'
)

Warning
This op is not differentiable. Indeed, the gradient of tf.linspace and tf.meshgrid are currently not defined.

Note
In the following, B is an optional batch dimension.

Args
starts	A tensor of shape [M] or [B, M], where the last dimension represents a M-D start point.
stops	A tensor of shape [M] or [B, M], where the last dimension represents a M-D end point.
nums	A tensor of shape [M] representing the number of subdivisions for each dimension.
name	A name for this op. Defaults to "grid_generate".

Returns
A tensor of shape [nums[0], ..., nums[M-1], M] containing an M-D uniform grid or a tensor of shape [B, nums[0], ..., nums[M-1], M] containing B M-D uniform grids. Please refer to the example below for more details.

Raises
ValueError	If the shape of starts, stops, or nums is not supported.

Examples
print(generate((-1.0, -2.0), (1.0, 2.0), (3, 5))) [[[-1. -2.] [-1. -1.] [-1. 0.] [-1. 1.] [-1. 2.]] [[ 0. -2.] [ 0. -1.] [ 0. 0.] [ 0. 1.] [ 0. 2.]] [[ 1. -2.] [ 1. -1.] [ 1. 0.] [ 1. 1.] [ 1. 2.]]] Generates a 3x5 2d grid from -1.0 to 1.0 with 3 subdivisions for the x axis and from -2.0 to 2.0 with 5 subdivisions for the y axis. This lead to a tensor of shape (3, 5, 2).