add chamfer loss

2023-01-14 17:13:27 +00:00
parent 191f6b01e0
commit c315f79941
2 changed files with 203 additions and 8 deletions
@@ -0,0 +1,141 @@
+# taken from pytorch3d.loss.chamfer, but only one directional
+
+from typing import Union
+
+import torch
+import torch.nn.functional as F
+from pytorch3d.ops.knn import knn_gather, knn_points
+from pytorch3d.loss.chamfer import _handle_pointcloud_input, _validate_chamfer_reduction_inputs
+
+
+def chamfer_distance(
+    x,
+    y,
+    x_lengths=None,
+    y_lengths=None,
+    x_normals=None,
+    y_normals=None,
+    weights=None,
+    batch_reduction: Union[str, None] = "mean",
+    point_reduction: str = "mean",
+    norm: int = 2,
+):
+    """
+    Chamfer distance between two pointclouds x and y.
+
+    Args:
+        x: FloatTensor of shape (N, P1, D) or a Pointclouds object representing
+            a batch of point clouds with at most P1 points in each batch element,
+            batch size N and feature dimension D.
+        y: FloatTensor of shape (N, P2, D) or a Pointclouds object representing
+            a batch of point clouds with at most P2 points in each batch element,
+            batch size N and feature dimension D.
+        x_lengths: Optional LongTensor of shape (N,) giving the number of points in each
+            cloud in x.
+        y_lengths: Optional LongTensor of shape (N,) giving the number of points in each
+            cloud in y.
+        x_normals: Optional FloatTensor of shape (N, P1, D).
+        y_normals: Optional FloatTensor of shape (N, P2, D).
+        weights: Optional FloatTensor of shape (N,) giving weights for
+            batch elements for reduction operation.
+        batch_reduction: Reduction operation to apply for the loss across the
+            batch, can be one of ["mean", "sum"] or None.
+        point_reduction: Reduction operation to apply for the loss across the
+            points, can be one of ["mean", "sum"].
+        norm: int indicates the norm used for the distance. Supports 1 for L1 and 2 for L2.
+
+    Returns:
+        2-element tuple containing
+
+        - **loss**: Tensor giving the reduced distance between the pointclouds
+          in x and the pointclouds in y.
+        - **loss_normals**: Tensor giving the reduced cosine distance of normals
+          between pointclouds in x and pointclouds in y. Returns None if
+          x_normals and y_normals are None.
+    """
+    _validate_chamfer_reduction_inputs(batch_reduction, point_reduction)
+
+    if not ((norm == 1) or (norm == 2)):
+        raise ValueError("Support for 1 or 2 norm.")
+
+    x, x_lengths, x_normals = _handle_pointcloud_input(x, x_lengths, x_normals)
+    y, y_lengths, y_normals = _handle_pointcloud_input(y, y_lengths, y_normals)
+
+    return_normals = x_normals is not None and y_normals is not None
+
+    N, P1, D = x.shape
+    P2 = y.shape[1]
+
+    # Check if inputs are heterogeneous and create a lengths mask.
+    is_x_heterogeneous = (x_lengths != P1).any()
+    x_mask = (
+        torch.arange(P1, device=x.device)[None] >= x_lengths[:, None]
+    )  # shape [N, P1]
+
+    if y.shape[0] != N or y.shape[2] != D:
+        raise ValueError("y does not have the correct shape.")
+    if weights is not None:
+        if weights.size(0) != N:
+            raise ValueError("weights must be of shape (N,).")
+        if not (weights >= 0).all():
+            raise ValueError("weights cannot be negative.")
+        if weights.sum() == 0.0:
+            weights = weights.view(N, 1)
+            if batch_reduction in ["mean", "sum"]:
+                return (
+                    (x.sum((1, 2)) * weights).sum() * 0.0,
+                    (x.sum((1, 2)) * weights).sum() * 0.0,
+                )
+            return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)
+
+    cham_norm_x = x.new_zeros(())
+
+    x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, norm=norm, K=1)
+
+    cham_x = x_nn.dists[..., 0]  # (N, P1)
+
+    if is_x_heterogeneous:
+        cham_x[x_mask] = 0.0
+
+    if weights is not None:
+        cham_x *= weights.view(N, 1)
+
+    if return_normals:
+        # Gather the normals using the indices and keep only value for k=0
+        x_normals_near = knn_gather(y_normals, x_nn.idx, y_lengths)[..., 0, :]
+
+        cham_norm_x = 1 - torch.abs(
+            F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)
+        )
+
+        if is_x_heterogeneous:
+            cham_norm_x[x_mask] = 0.0
+
+        if weights is not None:
+            cham_norm_x *= weights.view(N, 1)
+
+    # Apply point reduction
+    cham_x = cham_x.sum(1)  # (N,)
+    if return_normals:
+        cham_norm_x = cham_norm_x.sum(1)  # (N,)
+    if point_reduction == "mean":
+        x_lengths_clamped = x_lengths.clamp(min=1)
+        cham_x /= x_lengths_clamped
+        if return_normals:
+            cham_norm_x /= x_lengths_clamped
+
+    if batch_reduction is not None:
+        # batch_reduction == "sum"
+        cham_x = cham_x.sum()
+        if return_normals:
+            cham_norm_x = cham_norm_x.sum()
+        if batch_reduction == "mean":
+            div = weights.sum() if weights is not None else max(N, 1)
+            cham_x /= div
+            if return_normals:
+                cham_norm_x /= div
+
+    cham_dist = cham_x 
+    cham_normals = cham_norm_x if return_normals else None
+
+    return cham_dist, cham_normals