142 lines
5.0 KiB
Python
142 lines
5.0 KiB
Python
# taken from pytorch3d.loss.chamfer, but only one directional
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from typing import Union
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import torch
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import torch.nn.functional as F
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from pytorch3d.ops.knn import knn_gather, knn_points
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from pytorch3d.loss.chamfer import _handle_pointcloud_input, _validate_chamfer_reduction_inputs
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def chamfer_distance(
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x,
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y,
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x_lengths=None,
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y_lengths=None,
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x_normals=None,
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y_normals=None,
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weights=None,
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batch_reduction: Union[str, None] = "mean",
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point_reduction: str = "mean",
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norm: int = 2,
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):
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"""
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Chamfer distance between two pointclouds x and y.
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Args:
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x: FloatTensor of shape (N, P1, D) or a Pointclouds object representing
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a batch of point clouds with at most P1 points in each batch element,
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batch size N and feature dimension D.
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y: FloatTensor of shape (N, P2, D) or a Pointclouds object representing
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a batch of point clouds with at most P2 points in each batch element,
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batch size N and feature dimension D.
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x_lengths: Optional LongTensor of shape (N,) giving the number of points in each
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cloud in x.
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y_lengths: Optional LongTensor of shape (N,) giving the number of points in each
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cloud in y.
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x_normals: Optional FloatTensor of shape (N, P1, D).
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y_normals: Optional FloatTensor of shape (N, P2, D).
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weights: Optional FloatTensor of shape (N,) giving weights for
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batch elements for reduction operation.
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batch_reduction: Reduction operation to apply for the loss across the
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batch, can be one of ["mean", "sum"] or None.
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point_reduction: Reduction operation to apply for the loss across the
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points, can be one of ["mean", "sum"].
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norm: int indicates the norm used for the distance. Supports 1 for L1 and 2 for L2.
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Returns:
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2-element tuple containing
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- **loss**: Tensor giving the reduced distance between the pointclouds
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in x and the pointclouds in y.
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- **loss_normals**: Tensor giving the reduced cosine distance of normals
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between pointclouds in x and pointclouds in y. Returns None if
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x_normals and y_normals are None.
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"""
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_validate_chamfer_reduction_inputs(batch_reduction, point_reduction)
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if not ((norm == 1) or (norm == 2)):
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raise ValueError("Support for 1 or 2 norm.")
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x, x_lengths, x_normals = _handle_pointcloud_input(x, x_lengths, x_normals)
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y, y_lengths, y_normals = _handle_pointcloud_input(y, y_lengths, y_normals)
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return_normals = x_normals is not None and y_normals is not None
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N, P1, D = x.shape
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P2 = y.shape[1]
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# Check if inputs are heterogeneous and create a lengths mask.
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is_x_heterogeneous = (x_lengths != P1).any()
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x_mask = (
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torch.arange(P1, device=x.device)[None] >= x_lengths[:, None]
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) # shape [N, P1]
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if y.shape[0] != N or y.shape[2] != D:
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raise ValueError("y does not have the correct shape.")
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if weights is not None:
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if weights.size(0) != N:
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raise ValueError("weights must be of shape (N,).")
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if not (weights >= 0).all():
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raise ValueError("weights cannot be negative.")
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if weights.sum() == 0.0:
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weights = weights.view(N, 1)
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if batch_reduction in ["mean", "sum"]:
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return (
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(x.sum((1, 2)) * weights).sum() * 0.0,
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(x.sum((1, 2)) * weights).sum() * 0.0,
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)
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return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)
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cham_norm_x = x.new_zeros(())
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x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, norm=norm, K=1)
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cham_x = x_nn.dists[..., 0] # (N, P1)
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if is_x_heterogeneous:
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cham_x[x_mask] = 0.0
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if weights is not None:
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cham_x *= weights.view(N, 1)
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if return_normals:
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# Gather the normals using the indices and keep only value for k=0
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x_normals_near = knn_gather(y_normals, x_nn.idx, y_lengths)[..., 0, :]
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cham_norm_x = 1 - torch.abs(
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F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)
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)
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if is_x_heterogeneous:
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cham_norm_x[x_mask] = 0.0
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if weights is not None:
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cham_norm_x *= weights.view(N, 1)
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# Apply point reduction
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cham_x = cham_x.sum(1) # (N,)
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if return_normals:
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cham_norm_x = cham_norm_x.sum(1) # (N,)
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if point_reduction == "mean":
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x_lengths_clamped = x_lengths.clamp(min=1)
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cham_x /= x_lengths_clamped
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if return_normals:
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cham_norm_x /= x_lengths_clamped
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if batch_reduction is not None:
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# batch_reduction == "sum"
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cham_x = cham_x.sum()
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if return_normals:
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cham_norm_x = cham_norm_x.sum()
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if batch_reduction == "mean":
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div = weights.sum() if weights is not None else max(N, 1)
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cham_x /= div
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if return_normals:
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cham_norm_x /= div
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cham_dist = cham_x
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cham_normals = cham_norm_x if return_normals else None
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return cham_dist, cham_normals
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