noether.data.pipeline.sample_processors.point_sampling¶

Classes¶

PointSamplingSampleProcessor

Randomly subsamples points from a tensor.

Module Contents¶

class noether.data.pipeline.sample_processors.point_sampling.PointSamplingSampleProcessor(items, num_points, seed=None)¶

Bases: noether.data.pipeline.sample_processor.SampleProcessor

Randomly subsamples points from a tensor.

# dummy example
processor = PointSamplingSampleProcessor(
    items={"input_position", "output_position"},
    num_points=1024,
    seed=42,
)
input_sample = {
    "input_position": torch.randn(5000, 3),
    "output_position": torch.randn(5000, 3),
    "input_features": torch.randn(5000, 6),
}
output_sample = processor(input_sample)
# output_sample['input_position'] will be a tensor of shape (1024, 3)
# output_sample['output_position'] will be a tensor of shape (1024, 3)
# output_sample['input_features'] will be unchanged.
# If input features is also added to items, it will be of shape (1024, 6)

Parameters:

items (set[str]) – Which pointcloud items should be subsampled (e.g., input_position, output_position, …). If multiple
present (items are)
(e.g. (the subsampling will use identical indices for all items)
downsample (to)
subsampling). (output_position and output_pressure with the same)
num_points (int) – Number of points to sample.
seed (int | None) – Random seed for deterministic sampling for evaluation. Default None (i.e., no seed). If not None, requires sample index to be present in batch.

items¶

num_points¶

seed = None¶