Single directional chamfer distance and non-absolute cosine similarity

Summary: Single directional chamfer distance and option to use non-absolute cosine similarity

Reviewed By: bottler

Differential Revision: D46593980

fbshipit-source-id: b2e591706a0cdde1c2d361614cecebb84a581433

pytorch3d/loss/chamfer.py

@@ -68,74 +68,28 @@ def _handle_pointcloud_input(
     return X, lengths, normals
 
 
-def chamfer_distance(
+def _chamfer_distance_single_direction(
     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,
+    x_lengths,
+    y_lengths,
+    x_normals,
+    y_normals,
+    weights,
+    batch_reduction: Union[str, None],
+    point_reduction: str,
+    norm: int,
+    abs_cosine: bool,
 ):
-    """
-    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()
-    is_y_heterogeneous = (y_lengths != P2).any()
     x_mask = (
         torch.arange(P1, device=x.device)[None] >= x_lengths[:, None]
     )  # shape [N, P1]
-    y_mask = (
-        torch.arange(P2, device=y.device)[None] >= y_lengths[:, None]
-    )  # shape [N, P2]
-
     if y.shape[0] != N or y.shape[2] != D:
         raise ValueError("y does not have the correct shape.")
     if weights is not None:
@@ -153,75 +107,148 @@ def chamfer_distance(
             return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)
 
     cham_norm_x = x.new_zeros(())
-    cham_norm_y = x.new_zeros(())
 
     x_nn = knn_points(x, y, lengths1=x_lengths, lengths2=y_lengths, norm=norm, K=1)
-    y_nn = knn_points(y, x, lengths1=y_lengths, lengths2=x_lengths, norm=norm, K=1)
-
     cham_x = x_nn.dists[..., 0]  # (N, P1)
-    cham_y = y_nn.dists[..., 0]  # (N, P2)
 
     if is_x_heterogeneous:
         cham_x[x_mask] = 0.0
-    if is_y_heterogeneous:
-        cham_y[y_mask] = 0.0
 
     if weights is not None:
         cham_x *= weights.view(N, 1)
-        cham_y *= 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, :]
-        y_normals_near = knn_gather(x_normals, y_nn.idx, x_lengths)[..., 0, :]
 
-        cham_norm_x = 1 - torch.abs(
-            F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)
-        )
-        cham_norm_y = 1 - torch.abs(
-            F.cosine_similarity(y_normals, y_normals_near, dim=2, eps=1e-6)
-        )
+        cosine_sim = F.cosine_similarity(x_normals, x_normals_near, dim=2, eps=1e-6)
+        # If abs_cosine, ignore orientation and take the absolute value of the cosine sim.
+        cham_norm_x = 1 - (torch.abs(cosine_sim) if abs_cosine else cosine_sim)
 
         if is_x_heterogeneous:
             cham_norm_x[x_mask] = 0.0
-        if is_y_heterogeneous:
-            cham_norm_y[y_mask] = 0.0
 
         if weights is not None:
             cham_norm_x *= weights.view(N, 1)
-            cham_norm_y *= weights.view(N, 1)
+        cham_norm_x = cham_norm_x.sum(1)  # (N,)
 
     # Apply point reduction
     cham_x = cham_x.sum(1)  # (N,)
-    cham_y = cham_y.sum(1)  # (N,)
-    if return_normals:
-        cham_norm_x = cham_norm_x.sum(1)  # (N,)
-        cham_norm_y = cham_norm_y.sum(1)  # (N,)
     if point_reduction == "mean":
         x_lengths_clamped = x_lengths.clamp(min=1)
-        y_lengths_clamped = y_lengths.clamp(min=1)
         cham_x /= x_lengths_clamped
-        cham_y /= y_lengths_clamped
         if return_normals:
             cham_norm_x /= x_lengths_clamped
-            cham_norm_y /= y_lengths_clamped
 
     if batch_reduction is not None:
         # batch_reduction == "sum"
         cham_x = cham_x.sum()
-        cham_y = cham_y.sum()
         if return_normals:
             cham_norm_x = cham_norm_x.sum()
-            cham_norm_y = cham_norm_y.sum()
         if batch_reduction == "mean":
             div = weights.sum() if weights is not None else max(N, 1)
             cham_x /= div
-            cham_y /= div
             if return_normals:
                 cham_norm_x /= div
-                cham_norm_y /= div
-
-    cham_dist = cham_x + cham_y
-    cham_normals = cham_norm_x + cham_norm_y if return_normals else None
 
+    cham_dist = cham_x
+    cham_normals = cham_norm_x if return_normals else None
     return cham_dist, cham_normals
+
+
+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,
+    single_directional: bool = False,
+    abs_cosine: bool = True,
+):
+    """
+    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.
+        single_directional: If False (default), loss comes from both the distance between
+            each point in x and its nearest neighbor in y and each point in y and its nearest
+            neighbor in x. If True, loss is the distance between each point in x and its
+            nearest neighbor in y.
+        abs_cosine: If False, loss_normals is from one minus the cosine similarity.
+            If True (default), loss_normals is from one minus the absolute value of the
+            cosine similarity, which means that exactly opposite normals are considered
+            equivalent to exactly matching normals, i.e. sign does not matter.
+
+    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)
+
+    cham_x, cham_norm_x = _chamfer_distance_single_direction(
+        x,
+        y,
+        x_lengths,
+        y_lengths,
+        x_normals,
+        y_normals,
+        weights,
+        batch_reduction,
+        point_reduction,
+        norm,
+        abs_cosine,
+    )
+    if single_directional:
+        return cham_x, cham_norm_x
+    else:
+        cham_y, cham_norm_y = _chamfer_distance_single_direction(
+            y,
+            x,
+            y_lengths,
+            x_lengths,
+            y_normals,
+            x_normals,
+            weights,
+            batch_reduction,
+            point_reduction,
+            norm,
+            abs_cosine,
+        )
+        return (
+            cham_x + cham_y,
+            (cham_norm_x + cham_norm_y) if cham_norm_x is not None else None,
+        )

tests/test_chamfer.py

@@ -88,7 +88,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         )
 
     @staticmethod
-    def chamfer_distance_naive_pointclouds(p1, p2, norm: int = 2, device="cpu"):
+    def chamfer_distance_naive_pointclouds(
+        p1, p2, norm: int = 2, device="cpu", abs_cosine=True
+    ):
         """
         Naive iterative implementation of nearest neighbor and chamfer distance.
         x and y are assumed to be pointclouds objects with points and optionally normals.
@@ -146,17 +148,20 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         if return_normals:
             x_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
             y_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
-            lnorm1 = 1 - torch.abs(
-                F.cosine_similarity(
-                    x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
-                )
+            cosine_sim1 = F.cosine_similarity(
+                x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
             )
-            lnorm2 = 1 - torch.abs(
-                F.cosine_similarity(
-                    y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
-                )
+            cosine_sim2 = F.cosine_similarity(
+                y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
             )
 
+            if abs_cosine:
+                lnorm1 = 1 - torch.abs(cosine_sim1)
+                lnorm2 = 1 - torch.abs(cosine_sim2)
+            else:
+                lnorm1 = 1 - cosine_sim1
+                lnorm2 = 1 - cosine_sim2
+
             if is_x_heterogeneous:
                 lnorm1[x_mask] = 0.0
             if is_y_heterogeneous:
@@ -167,7 +172,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         return loss, lnorm
 
     @staticmethod
-    def chamfer_distance_naive(x, y, x_normals=None, y_normals=None, norm: int = 2):
+    def chamfer_distance_naive(
+        x, y, x_normals=None, y_normals=None, norm: int = 2, abs_cosine=True
+    ):
         """
         Naive iterative implementation of nearest neighbor and chamfer distance.
         Returns lists of the unreduced loss and loss_normals. This naive
@@ -200,16 +207,21 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
         if return_normals:
             x_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
             y_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
-            lnorm1 = 1 - torch.abs(
-                F.cosine_similarity(
-                    x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
-                )
+
+            cosine_sim1 = F.cosine_similarity(
+                x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
             )
-            lnorm2 = 1 - torch.abs(
-                F.cosine_similarity(
-                    y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
-                )
+            cosine_sim2 = F.cosine_similarity(
+                y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
             )
+
+            if abs_cosine:
+                lnorm1 = 1 - torch.abs(cosine_sim1)
+                lnorm2 = 1 - torch.abs(cosine_sim2)
+            else:
+                lnorm1 = 1 - cosine_sim1
+                lnorm2 = 1 - cosine_sim2
+
             lnorm = [lnorm1, lnorm2]  # [(N, P1), (N, P2)]
 
         return loss, lnorm
@@ -323,6 +335,80 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
                 y_lengths,
             )
 
+    def test_single_directional_chamfer_vs_naive_pointcloud(self):
+        """
+        Test the single directional settings for chamfer_distance
+        (point reduction = "mean" and batch_reduction="mean") but with heterogeneous
+        pointclouds as input. Compare with the naive implementation of chamfer
+        which supports heterogeneous pointcloud objects.
+        """
+        N, max_P1, max_P2 = 3, 70, 70
+        device = get_random_cuda_device()
+
+        for norm in [1, 2]:
+            for abs_cosine in [True, False]:
+                points_normals = TestChamfer.init_pointclouds(N, max_P1, max_P2, device)
+                weights = points_normals.weights
+                x_lengths = points_normals.p1_lengths
+                y_lengths = points_normals.p2_lengths
+
+                # Chamfer with tensors as input for heterogeneous pointclouds.
+                cham_tensor, norm_tensor = chamfer_distance(
+                    points_normals.p1,
+                    points_normals.p2,
+                    x_normals=points_normals.n1,
+                    y_normals=points_normals.n2,
+                    x_lengths=points_normals.p1_lengths,
+                    y_lengths=points_normals.p2_lengths,
+                    weights=weights,
+                    norm=norm,
+                    single_directional=True,
+                    abs_cosine=abs_cosine,
+                )
+
+                # Chamfer with pointclouds as input.
+                (
+                    pred_loss,
+                    pred_norm_loss,
+                ) = TestChamfer.chamfer_distance_naive_pointclouds(
+                    points_normals.cloud1,
+                    points_normals.cloud2,
+                    norm=norm,
+                    device=device,
+                    abs_cosine=abs_cosine,
+                )
+
+                # Mean reduction point loss.
+                pred_loss[0] *= weights.view(N, 1)
+                pred_loss_mean = pred_loss[0].sum(1) / x_lengths
+                pred_loss_mean = pred_loss_mean.sum()
+                pred_loss_mean /= weights.sum()
+
+                # Mean reduction norm loss.
+                pred_norm_loss[0] *= weights.view(N, 1)
+                pred_norm_loss_mean = pred_norm_loss[0].sum(1) / x_lengths
+                pred_norm_loss_mean = pred_norm_loss_mean.sum() / weights.sum()
+
+                self.assertClose(pred_loss_mean, cham_tensor)
+                self.assertClose(pred_norm_loss_mean, norm_tensor)
+
+                self._check_gradients(
+                    cham_tensor,
+                    norm_tensor,
+                    pred_loss_mean,
+                    pred_norm_loss_mean,
+                    points_normals.cloud1.points_list(),
+                    points_normals.p1,
+                    points_normals.cloud2.points_list(),
+                    points_normals.p2,
+                    points_normals.cloud1.normals_list(),
+                    points_normals.n1,
+                    points_normals.cloud2.normals_list(),
+                    points_normals.n2,
+                    x_lengths,
+                    y_lengths,
+                )
+
     def test_chamfer_pointcloud_object_withnormals(self):
         N = 5
         P1, P2 = 100, 100
@@ -485,6 +571,53 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
             loss, loss_norm, pred_loss_mean, pred_loss_norm_mean, p1, p11, p2, p22
         )
 
+    def test_single_direction_chamfer_point_reduction_mean(self):
+        """
+        Compare output of vectorized chamfer loss with naive implementation
+        for point_reduction = "mean" and batch_reduction = None.
+        """
+        N, max_P1, max_P2 = 7, 10, 18
+        device = get_random_cuda_device()
+        points_normals = TestChamfer.init_pointclouds(N, max_P1, max_P2, device)
+        p1 = points_normals.p1
+        p2 = points_normals.p2
+        p1_normals = points_normals.n1
+        p2_normals = points_normals.n2
+        weights = points_normals.weights
+        p11 = p1.detach().clone()
+        p22 = p2.detach().clone()
+        p11.requires_grad = True
+        p22.requires_grad = True
+        P1 = p1.shape[1]
+
+        pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, x_normals=p1_normals, y_normals=p2_normals
+        )
+
+        # point_reduction = "mean".
+        loss, loss_norm = chamfer_distance(
+            p11,
+            p22,
+            x_normals=p1_normals,
+            y_normals=p2_normals,
+            weights=weights,
+            batch_reduction=None,
+            point_reduction="mean",
+            single_directional=True,
+        )
+        pred_loss_mean = pred_loss[0].sum(1) / P1
+        pred_loss_mean *= weights
+        self.assertClose(loss, pred_loss_mean)
+
+        pred_loss_norm_mean = pred_loss_norm[0].sum(1) / P1
+        pred_loss_norm_mean *= weights
+        self.assertClose(loss_norm, pred_loss_norm_mean)
+
+        # Check gradients
+        self._check_gradients(
+            loss, loss_norm, pred_loss_mean, pred_loss_norm_mean, p1, p11, p2, p22
+        )
+
     def test_chamfer_point_reduction_sum(self):
         """
         Compare output of vectorized chamfer loss with naive implementation
@@ -529,6 +662,51 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
             loss, loss_norm, pred_loss_sum, pred_loss_norm_sum, p1, p11, p2, p22
         )
 
+    def test_single_directional_chamfer_point_reduction_sum(self):
+        """
+        Compare output of vectorized single directional chamfer loss with naive implementation
+        for point_reduction = "sum" and batch_reduction = None.
+        """
+        N, P1, P2 = 7, 10, 18
+        device = get_random_cuda_device()
+        points_normals = TestChamfer.init_pointclouds(N, P1, P2, device)
+        p1 = points_normals.p1
+        p2 = points_normals.p2
+        p1_normals = points_normals.n1
+        p2_normals = points_normals.n2
+        weights = points_normals.weights
+        p11 = p1.detach().clone()
+        p22 = p2.detach().clone()
+        p11.requires_grad = True
+        p22.requires_grad = True
+
+        pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, x_normals=p1_normals, y_normals=p2_normals
+        )
+
+        loss, loss_norm = chamfer_distance(
+            p11,
+            p22,
+            x_normals=p1_normals,
+            y_normals=p2_normals,
+            weights=weights,
+            batch_reduction=None,
+            point_reduction="sum",
+            single_directional=True,
+        )
+        pred_loss_sum = pred_loss[0].sum(1)
+        pred_loss_sum *= weights
+        self.assertClose(loss, pred_loss_sum)
+
+        pred_loss_norm_sum = pred_loss_norm[0].sum(1)
+        pred_loss_norm_sum *= weights
+        self.assertClose(loss_norm, pred_loss_norm_sum)
+
+        # Check gradients
+        self._check_gradients(
+            loss, loss_norm, pred_loss_sum, pred_loss_norm_sum, p1, p11, p2, p22
+        )
+
     def _check_gradients(
         self,
         loss,