mirror of
https://github.com/facebookresearch/pytorch3d.git
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14f7fe4a65
Differential Revision: D29458533 fbshipit-source-id: d9ef216fdbb677e49371ad91ea5e9355146c1c52
220 lines
8.0 KiB
Python
220 lines
8.0 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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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.structures.pointclouds import Pointclouds
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def _validate_chamfer_reduction_inputs(
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batch_reduction: Union[str, None], point_reduction: str
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):
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"""Check the requested reductions are valid.
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Args:
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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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"""
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if batch_reduction is not None and batch_reduction not in ["mean", "sum"]:
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raise ValueError('batch_reduction must be one of ["mean", "sum"] or None')
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if point_reduction not in ["mean", "sum"]:
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raise ValueError('point_reduction must be one of ["mean", "sum"]')
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def _handle_pointcloud_input(
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points: Union[torch.Tensor, Pointclouds],
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lengths: Union[torch.Tensor, None],
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normals: Union[torch.Tensor, None],
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):
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"""
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If points is an instance of Pointclouds, retrieve the padded points tensor
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along with the number of points per batch and the padded normals.
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Otherwise, return the input points (and normals) with the number of points per cloud
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set to the size of the second dimension of `points`.
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"""
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if isinstance(points, Pointclouds):
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X = points.points_padded()
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lengths = points.num_points_per_cloud()
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normals = points.normals_padded() # either a tensor or None
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elif torch.is_tensor(points):
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if points.ndim != 3:
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raise ValueError("Expected points to be of shape (N, P, D)")
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X = points
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if lengths is not None and (
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lengths.ndim != 1 or lengths.shape[0] != X.shape[0]
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):
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raise ValueError("Expected lengths to be of shape (N,)")
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if lengths is None:
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lengths = torch.full(
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(X.shape[0],), X.shape[1], dtype=torch.int64, device=points.device
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)
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if normals is not None and normals.ndim != 3:
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raise ValueError("Expected normals to be of shape (N, P, 3")
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else:
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raise ValueError(
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"The input pointclouds should be either "
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+ "Pointclouds objects or torch.Tensor of shape "
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+ "(minibatch, num_points, 3)."
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)
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return X, lengths, normals
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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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):
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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 x.
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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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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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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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is_y_heterogeneous = (y_lengths != P2).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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y_mask = (
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torch.arange(P2, device=y.device)[None] >= y_lengths[:, None]
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) # shape [N, P2]
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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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cham_norm_y = x.new_zeros(())
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x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, K=1)
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y_nn = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, K=1)
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cham_x = x_nn.dists[..., 0] # (N, P1)
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cham_y = y_nn.dists[..., 0] # (N, P2)
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if is_x_heterogeneous:
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cham_x[x_mask] = 0.0
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if is_y_heterogeneous:
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cham_y[y_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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cham_y *= 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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y_normals_near = knn_gather(x_normals, y_nn.idx, x_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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cham_norm_y = 1 - torch.abs(
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F.cosine_similarity(y_normals, y_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 is_y_heterogeneous:
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cham_norm_y[y_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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cham_norm_y *= 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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cham_y = cham_y.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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cham_norm_y = cham_norm_y.sum(1) # (N,)
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if point_reduction == "mean":
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cham_x /= x_lengths
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cham_y /= y_lengths
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if return_normals:
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cham_norm_x /= x_lengths
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cham_norm_y /= y_lengths
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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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cham_y = cham_y.sum()
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if return_normals:
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cham_norm_x = cham_norm_x.sum()
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cham_norm_y = cham_norm_y.sum()
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if batch_reduction == "mean":
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div = weights.sum() if weights is not None else N
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cham_x /= div
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cham_y /= div
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if return_normals:
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cham_norm_x /= div
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cham_norm_y /= div
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cham_dist = cham_x + cham_y
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cham_normals = cham_norm_x + cham_norm_y if return_normals else None
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return cham_dist, cham_normals
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