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use assertClose

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Summary: use assertClose in some tests, which enforces shape equality. Fixes some small problems, including graph_conv on an empty graph.

Reviewed By: nikhilaravi

Differential Revision: D20556912

fbshipit-source-id: 60a61eafe3c03ce0f6c9c1a842685708fb10ac5b
This commit is contained in:
Jeremy Reizenstein
2020-03-23 11:33:10 -07:00
committed by Facebook GitHub Bot
parent 744ef0c2c8
commit 595aca27ea
13 changed files with 216 additions and 241 deletions
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19
tests/test_rasterize_points.py
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@@ -224,9 +224,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
self.assertEqual((zbuf1.cpu() == zbuf2.cpu()).all().item(), 1)
self.assertClose(dist1.cpu(), dist2.cpu())
if compare_grads:
self.assertTrue(
torch.allclose(grad_points1, grad_points2, atol=2e-6)
)
self.assertClose(grad_points1, grad_points2, atol=2e-6)
def _test_behind_camera(self, rasterize_points_fn, device, bin_size=None):
# Test case where all points are behind the camera -- nothing should
@@ -261,7 +259,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
self.assertTrue(idx_same)
self.assertTrue(zbuf_same)
self.assertTrue(torch.allclose(dists, dists_expected))
self.assertClose(dists, dists_expected)
def _simple_test_case(self, rasterize_points_fn, device, bin_size=0):
# Create two pointclouds with different numbers of points.
@@ -334,18 +332,18 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
], device=device)
# fmt: on
dists1_expected = torch.full(
(1, 5, 5, 2), fill_value=0.0, dtype=torch.float32, device=device
dists1_expected = torch.zeros(
(5, 5, 2), dtype=torch.float32, device=device
)
# fmt: off
dists1_expected[0, :, :, 0] = torch.tensor([
dists1_expected[:, :, 0] = torch.tensor([
[-1.00, -1.00, 0.16, -1.00, -1.00], # noqa: E241
[-1.00, 0.16, 0.16, 0.16, -1.00], # noqa: E241
[ 0.16, 0.16, 0.00, 0.16, -1.00], # noqa: E241 E201
[-1.00, 0.16, 0.16, -1.00, -1.00], # noqa: E241
[-1.00, -1.00, -1.00, -1.00, -1.00], # noqa: E241
], device=device)
dists1_expected[0, :, :, 1] = torch.tensor([
dists1_expected[:, :, 1] = torch.tensor([
[-1.00, -1.00, -1.00, -1.00, -1.00], # noqa: E241
[-1.00, 0.16, 0.00, -1.00, -1.00], # noqa: E241
[-1.00, 0.00, 0.16, -1.00, -1.00], # noqa: E241
@@ -370,10 +368,9 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
print(idx[0, :, :, 0])
print(idx[0, :, :, 1])
zbuf_same = (zbuf[0, ...] == zbuf1_expected).all().item() == 1
dist_same = torch.allclose(dists[0, ...], dists1_expected)
self.assertClose(dists[0, ...], dists1_expected)
self.assertTrue(idx_same)
self.assertTrue(zbuf_same)
self.assertTrue(dist_same)
# Check second point cloud - the indices in idx refer to points in the
# pointclouds.points_packed() tensor. In the second point cloud,
@@ -387,7 +384,7 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
zbuf_same = (zbuf[1, ...] == zbuf1_expected).all().item() == 1
self.assertTrue(idx_same)
self.assertTrue(zbuf_same)
self.assertTrue(torch.allclose(dists[1, ...], dists1_expected))
self.assertClose(dists[1, ...], dists1_expected)
def test_coarse_cpu(self):
return self._test_coarse_rasterize(torch.device("cpu"))