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import unittest |
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import numpy as np |
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import torch |
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from pytorch3d.common.workaround import _safe_det_3x3 |
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from .common_testing import TestCaseMixin |
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class TestSafeDet3x3(TestCaseMixin, unittest.TestCase): |
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def setUp(self) -> None: |
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super().setUp() |
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torch.manual_seed(42) |
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np.random.seed(42) |
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def _test_det_3x3(self, batch_size, device): |
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t = torch.rand((batch_size, 3, 3), dtype=torch.float32, device=device) |
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actual_det = _safe_det_3x3(t) |
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expected_det = t.det() |
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self.assertClose(actual_det, expected_det, atol=1e-7) |
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def test_empty_batch(self): |
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self._test_det_3x3(0, torch.device("cpu")) |
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self._test_det_3x3(0, torch.device("cuda:0")) |
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def test_manual(self): |
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t = torch.Tensor( |
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[ |
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[[1, 0, 0], [0, 1, 0], [0, 0, 1]], |
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[[2, -5, 3], [0, 7, -2], [-1, 4, 1]], |
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[[6, 1, 1], [4, -2, 5], [2, 8, 7]], |
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] |
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).to(dtype=torch.float32) |
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expected_det = torch.Tensor([1, 41, -306]).to(dtype=torch.float32) |
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self.assertClose(_safe_det_3x3(t), expected_det) |
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device_cuda = torch.device("cuda:0") |
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self.assertClose( |
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_safe_det_3x3(t.to(device=device_cuda)), expected_det.to(device=device_cuda) |
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) |
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def test_regression(self): |
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tries = 32 |
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device_cpu = torch.device("cpu") |
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device_cuda = torch.device("cuda:0") |
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batch_sizes = np.random.randint(low=1, high=128, size=tries) |
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for batch_size in batch_sizes: |
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self._test_det_3x3(batch_size, device_cpu) |
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self._test_det_3x3(batch_size, device_cuda) |
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