View source on GitHub
|
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.
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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).