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https://github.com/facebookresearch/pytorch3d.git
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34f648ede0
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from pytorch3d.implicitron.models.renderer.ray_point_refiner import RayPointRefiner
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from pytorch3d.renderer import RayBundle
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from tests.common_testing import TestCaseMixin
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class TestRayPointRefiner(TestCaseMixin, unittest.TestCase):
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def test_simple(self):
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length = 15
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n_pts_per_ray = 10
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for add_input_samples in [False, True]:
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ray_point_refiner = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=False,
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add_input_samples=add_input_samples,
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)
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lengths = torch.arange(length, dtype=torch.float32).expand(3, 25, length)
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bundle = RayBundle(lengths=lengths, origins=None, directions=None, xys=None)
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weights = torch.ones(3, 25, length)
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refined = ray_point_refiner(bundle, weights)
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self.assertIsNone(refined.directions)
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self.assertIsNone(refined.origins)
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self.assertIsNone(refined.xys)
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expected = torch.linspace(0.5, length - 1.5, n_pts_per_ray)
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expected = expected.expand(3, 25, n_pts_per_ray)
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if add_input_samples:
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full_expected = torch.cat((lengths, expected), dim=-1).sort()[0]
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else:
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full_expected = expected
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self.assertClose(refined.lengths, full_expected)
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ray_point_refiner_random = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=True,
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add_input_samples=add_input_samples,
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)
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refined_random = ray_point_refiner_random(bundle, weights)
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lengths_random = refined_random.lengths
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self.assertEqual(lengths_random.shape, full_expected.shape)
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if not add_input_samples:
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self.assertGreater(lengths_random.min().item(), 0.5)
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self.assertLess(lengths_random.max().item(), length - 1.5)
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# Check sorted
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self.assertTrue(
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(lengths_random[..., 1:] - lengths_random[..., :-1] > 0).all()
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)
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