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Add "max" point reduction for chamfer distance

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Summary:
* Adds a "max" option for the point_reduction input to the
  chamfer_distance function.
* When combining the x and y directions, maxes the losses instead
  of summing them when point_reduction="max".
* Moves batch reduction to happen after the directions are
  combined.
* Adds test_chamfer_point_reduction_max and
  test_single_directional_chamfer_point_reduction_max tests.

Fixes  https://github.com/facebookresearch/pytorch3d/issues/1838

Reviewed By: bottler

Differential Revision: D60614661

fbshipit-source-id: 7879816acfda03e945bada951b931d2c522756eb
This commit is contained in:
Rebecca Chen (Python)
2024-08-02 10:46:07 -07:00
committed by Facebook GitHub Bot
parent 7edaee71a9
commit 44702fdb4b
2 changed files with 136 additions and 35 deletions
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83
tests/test_chamfer.py
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@@ -847,6 +847,85 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
loss, loss_norm, pred_loss[0], pred_loss_norm[0], p1, p11, p2, p22
)
def test_chamfer_point_reduction_max(self):
"""
Compare output of vectorized chamfer loss with naive implementation
for point_reduction = "max" 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
weights = points_normals.weights
p11 = p1.detach().clone()
p22 = p2.detach().clone()
p11.requires_grad = True
p22.requires_grad = True
pred_loss, unused_pred_loss_norm = TestChamfer.chamfer_distance_naive(
p1, p2, x_normals=None, y_normals=None
)
loss, loss_norm = chamfer_distance(
p11,
p22,
x_normals=None,
y_normals=None,
weights=weights,
batch_reduction=None,
point_reduction="max",
)
pred_loss_max = torch.maximum(
pred_loss[0].max(1).values, pred_loss[1].max(1).values
)
pred_loss_max *= weights
self.assertClose(loss, pred_loss_max)
self.assertIsNone(loss_norm)
# Check gradients
self._check_gradients(loss, loss_norm, pred_loss_max, None, p1, p11, p2, p22)
def test_single_directional_chamfer_point_reduction_max(self):
"""
Compare output of vectorized single directional chamfer loss with naive implementation
for point_reduction = "max" 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
weights = points_normals.weights
p11 = p1.detach().clone()
p22 = p2.detach().clone()
p11.requires_grad = True
p22.requires_grad = True
pred_loss, unused_pred_loss_norm = TestChamfer.chamfer_distance_naive(
p1, p2, x_normals=None, y_normals=None
)
loss, loss_norm = chamfer_distance(
p11,
p22,
x_normals=None,
y_normals=None,
weights=weights,
batch_reduction=None,
point_reduction="max",
single_directional=True,
)
pred_loss_max = pred_loss[0].max(1).values
pred_loss_max *= weights
self.assertClose(loss, pred_loss_max)
self.assertIsNone(loss_norm)
# Check gradients
self._check_gradients(loss, loss_norm, pred_loss_max, None, p1, p11, p2, p22)
def _check_gradients(
self,
loss,
@@ -1020,9 +1099,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
with self.assertRaisesRegex(ValueError, "batch_reduction must be one of"):
chamfer_distance(p1, p2, weights=weights, batch_reduction="max")
# Error when point_reduction is not in ["mean", "sum"] or None.
# Error when point_reduction is not in ["mean", "sum", "max"] or None.
with self.assertRaisesRegex(ValueError, "point_reduction must be one of"):
chamfer_distance(p1, p2, weights=weights, point_reduction="max")
chamfer_distance(p1, p2, weights=weights, point_reduction="min")
def test_incorrect_weights(self):
N, P1, P2 = 16, 64, 128