sleap.nn.data.utils

Miscellaneous utility functions for data processing.

sleap.nn.data.utils.ensure_list(x: Any) → List[Any][source]

Convert the input into a list if it is not already.

sleap.nn.data.utils.expand_to_rank(x: tensorflow.python.framework.ops.Tensor, target_rank: int, prepend: bool = True) → tensorflow.python.framework.ops.Tensor[source]

Expand a tensor to a target rank by adding singleton dimensions.

Parameters

  • x – Any tf.Tensor with rank <= target_rank. If the rank is higher than target_rank, the tensor will be returned with the same shape.

  • target_rank – Rank to expand the input to.

  • prepend – If True, singleton dimensions are added before the first axis of the data. If False, singleton dimensions are added after the last axis.

Returns

The expanded tensor of the same dtype as the input, but with rank target_rank.

The output has the same exact data as the input tensor and will be identical if they are both flattened.

sleap.nn.data.utils.gaussian_pdf(x: tensorflow.python.framework.ops.Tensor, sigma: float) → tensorflow.python.framework.ops.Tensor[source]

Compute the PDF of an unnormalized 0-centered Gaussian distribution.

Parameters

x – Any tensor of dtype tf.float32 with values to compute the PDF for.

Returns

A tensor of the same shape as x, but with values of a PDF of an unnormalized Gaussian distribution. Values of 0 have an unnormalized PDF value of 1.0.

sleap.nn.data.utils.make_grid_vectors(image_height: int, image_width: int, output_stride: int = 1) → Tuple[tensorflow.python.framework.ops.Tensor, tensorflow.python.framework.ops.Tensor][source]

Make sampling grid vectors from image dimensions.

This is a useful function for creating the x- and y-vectors that define a sampling grid over an image space. These vectors can be used to generate a full meshgrid or for equivalent broadcasting operations.

Parameters

  • image_height – Height of the image grid that will be sampled, specified as a scalar integer.

  • image_width – width of the image grid that will be sampled, specified as a scalar integer.

  • output_stride – Sampling step size, specified as a scalar integer. This can be used to specify a sampling grid that has a smaller shape than the image grid but with values span the same range. This can be thought of as the reciprocal of the output scale, i.e., it will induce subsampling when set to values greater than 1.

Returns

Tuple of grid vectors (xv, yv). These are tensors of dtype tf.float32 with shapes (grid_width,) and (grid_height,) respectively.

The grid dimensions are calculated as:

grid_width = image_width // output_stride grid_height = image_height // output_stride