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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
This commit is contained in:
Norman Mueller
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Facebook GitHub Bot
Facebook GitHub Bot
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573a42cd5f
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5ffeb4d580
@@ -88,7 +88,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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)
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@staticmethod
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def chamfer_distance_naive_pointclouds(p1, p2, norm: int = 2, device="cpu"):
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def chamfer_distance_naive_pointclouds(
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p1, p2, norm: int = 2, device="cpu", abs_cosine=True
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):
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"""
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Naive iterative implementation of nearest neighbor and chamfer distance.
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x and y are assumed to be pointclouds objects with points and optionally normals.
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@@ -146,17 +148,20 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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if return_normals:
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x_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
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y_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
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lnorm1 = 1 - torch.abs(
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F.cosine_similarity(
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x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
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)
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cosine_sim1 = F.cosine_similarity(
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x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
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)
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lnorm2 = 1 - torch.abs(
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F.cosine_similarity(
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y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
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)
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cosine_sim2 = F.cosine_similarity(
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y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
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)
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if abs_cosine:
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lnorm1 = 1 - torch.abs(cosine_sim1)
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lnorm2 = 1 - torch.abs(cosine_sim2)
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else:
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lnorm1 = 1 - cosine_sim1
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lnorm2 = 1 - cosine_sim2
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if is_x_heterogeneous:
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lnorm1[x_mask] = 0.0
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if is_y_heterogeneous:
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@@ -167,7 +172,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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return loss, lnorm
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@staticmethod
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def chamfer_distance_naive(x, y, x_normals=None, y_normals=None, norm: int = 2):
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def chamfer_distance_naive(
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x, y, x_normals=None, y_normals=None, norm: int = 2, abs_cosine=True
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):
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"""
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Naive iterative implementation of nearest neighbor and chamfer distance.
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Returns lists of the unreduced loss and loss_normals. This naive
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@@ -200,16 +207,21 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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if return_normals:
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x_index = dist.argmin(2).view(N, P1, 1).expand(N, P1, 3)
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y_index = dist.argmin(1).view(N, P2, 1).expand(N, P2, 3)
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lnorm1 = 1 - torch.abs(
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F.cosine_similarity(
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x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
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)
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cosine_sim1 = F.cosine_similarity(
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x_normals, y_normals.gather(1, x_index), dim=2, eps=1e-6
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)
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lnorm2 = 1 - torch.abs(
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F.cosine_similarity(
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y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
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)
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cosine_sim2 = F.cosine_similarity(
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y_normals, x_normals.gather(1, y_index), dim=2, eps=1e-6
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)
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if abs_cosine:
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lnorm1 = 1 - torch.abs(cosine_sim1)
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lnorm2 = 1 - torch.abs(cosine_sim2)
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else:
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lnorm1 = 1 - cosine_sim1
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lnorm2 = 1 - cosine_sim2
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lnorm = [lnorm1, lnorm2] # [(N, P1), (N, P2)]
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return loss, lnorm
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@@ -323,6 +335,80 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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y_lengths,
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)
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def test_single_directional_chamfer_vs_naive_pointcloud(self):
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"""
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Test the single directional settings for chamfer_distance
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(point reduction = "mean" and batch_reduction="mean") but with heterogeneous
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pointclouds as input. Compare with the naive implementation of chamfer
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which supports heterogeneous pointcloud objects.
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"""
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N, max_P1, max_P2 = 3, 70, 70
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device = get_random_cuda_device()
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for norm in [1, 2]:
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for abs_cosine in [True, False]:
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points_normals = TestChamfer.init_pointclouds(N, max_P1, max_P2, device)
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weights = points_normals.weights
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x_lengths = points_normals.p1_lengths
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y_lengths = points_normals.p2_lengths
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# Chamfer with tensors as input for heterogeneous pointclouds.
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cham_tensor, norm_tensor = chamfer_distance(
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points_normals.p1,
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points_normals.p2,
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x_normals=points_normals.n1,
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y_normals=points_normals.n2,
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x_lengths=points_normals.p1_lengths,
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y_lengths=points_normals.p2_lengths,
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weights=weights,
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norm=norm,
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single_directional=True,
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abs_cosine=abs_cosine,
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)
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# Chamfer with pointclouds as input.
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(
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pred_loss,
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pred_norm_loss,
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) = TestChamfer.chamfer_distance_naive_pointclouds(
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points_normals.cloud1,
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points_normals.cloud2,
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norm=norm,
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device=device,
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abs_cosine=abs_cosine,
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)
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# Mean reduction point loss.
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pred_loss[0] *= weights.view(N, 1)
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pred_loss_mean = pred_loss[0].sum(1) / x_lengths
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pred_loss_mean = pred_loss_mean.sum()
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pred_loss_mean /= weights.sum()
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# Mean reduction norm loss.
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pred_norm_loss[0] *= weights.view(N, 1)
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pred_norm_loss_mean = pred_norm_loss[0].sum(1) / x_lengths
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pred_norm_loss_mean = pred_norm_loss_mean.sum() / weights.sum()
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self.assertClose(pred_loss_mean, cham_tensor)
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self.assertClose(pred_norm_loss_mean, norm_tensor)
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self._check_gradients(
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cham_tensor,
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norm_tensor,
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pred_loss_mean,
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pred_norm_loss_mean,
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points_normals.cloud1.points_list(),
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points_normals.p1,
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points_normals.cloud2.points_list(),
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points_normals.p2,
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points_normals.cloud1.normals_list(),
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points_normals.n1,
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points_normals.cloud2.normals_list(),
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points_normals.n2,
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x_lengths,
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y_lengths,
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)
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def test_chamfer_pointcloud_object_withnormals(self):
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N = 5
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P1, P2 = 100, 100
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@@ -485,6 +571,53 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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loss, loss_norm, pred_loss_mean, pred_loss_norm_mean, p1, p11, p2, p22
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)
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def test_single_direction_chamfer_point_reduction_mean(self):
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"""
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Compare output of vectorized chamfer loss with naive implementation
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for point_reduction = "mean" and batch_reduction = None.
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"""
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N, max_P1, max_P2 = 7, 10, 18
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device = get_random_cuda_device()
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points_normals = TestChamfer.init_pointclouds(N, max_P1, max_P2, device)
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p1 = points_normals.p1
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p2 = points_normals.p2
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p1_normals = points_normals.n1
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p2_normals = points_normals.n2
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weights = points_normals.weights
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p11 = p1.detach().clone()
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p22 = p2.detach().clone()
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p11.requires_grad = True
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p22.requires_grad = True
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P1 = p1.shape[1]
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pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
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p1, p2, x_normals=p1_normals, y_normals=p2_normals
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)
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# point_reduction = "mean".
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loss, loss_norm = chamfer_distance(
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p11,
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p22,
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x_normals=p1_normals,
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y_normals=p2_normals,
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weights=weights,
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batch_reduction=None,
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point_reduction="mean",
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single_directional=True,
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)
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pred_loss_mean = pred_loss[0].sum(1) / P1
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pred_loss_mean *= weights
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self.assertClose(loss, pred_loss_mean)
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pred_loss_norm_mean = pred_loss_norm[0].sum(1) / P1
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pred_loss_norm_mean *= weights
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self.assertClose(loss_norm, pred_loss_norm_mean)
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# Check gradients
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self._check_gradients(
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loss, loss_norm, pred_loss_mean, pred_loss_norm_mean, p1, p11, p2, p22
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)
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def test_chamfer_point_reduction_sum(self):
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"""
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Compare output of vectorized chamfer loss with naive implementation
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@@ -529,6 +662,51 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
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loss, loss_norm, pred_loss_sum, pred_loss_norm_sum, p1, p11, p2, p22
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)
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def test_single_directional_chamfer_point_reduction_sum(self):
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"""
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Compare output of vectorized single directional chamfer loss with naive implementation
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for point_reduction = "sum" and batch_reduction = None.
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"""
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N, P1, P2 = 7, 10, 18
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device = get_random_cuda_device()
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points_normals = TestChamfer.init_pointclouds(N, P1, P2, device)
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p1 = points_normals.p1
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p2 = points_normals.p2
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p1_normals = points_normals.n1
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p2_normals = points_normals.n2
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weights = points_normals.weights
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p11 = p1.detach().clone()
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p22 = p2.detach().clone()
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p11.requires_grad = True
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p22.requires_grad = True
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pred_loss, pred_loss_norm = TestChamfer.chamfer_distance_naive(
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p1, p2, x_normals=p1_normals, y_normals=p2_normals
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)
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loss, loss_norm = chamfer_distance(
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p11,
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p22,
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x_normals=p1_normals,
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y_normals=p2_normals,
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weights=weights,
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batch_reduction=None,
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point_reduction="sum",
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single_directional=True,
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)
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pred_loss_sum = pred_loss[0].sum(1)
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pred_loss_sum *= weights
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self.assertClose(loss, pred_loss_sum)
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pred_loss_norm_sum = pred_loss_norm[0].sum(1)
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pred_loss_norm_sum *= weights
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self.assertClose(loss_norm, pred_loss_norm_sum)
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# Check gradients
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self._check_gradients(
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loss, loss_norm, pred_loss_sum, pred_loss_norm_sum, p1, p11, p2, p22
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)
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def _check_gradients(
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self,
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loss,
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