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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from pytorch3d.common.workaround import _safe_det_3x3
from .common_testing import TestCaseMixin
class TestSafeDet3x3(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(42)
np.random.seed(42)
def _test_det_3x3(self, batch_size, device):
t = torch.rand((batch_size, 3, 3), dtype=torch.float32, device=device)
actual_det = _safe_det_3x3(t)
expected_det = t.det()
self.assertClose(actual_det, expected_det, atol=1e-7)
def test_empty_batch(self):
self._test_det_3x3(0, torch.device("cpu"))
self._test_det_3x3(0, torch.device("cuda:0"))
def test_manual(self):
t = torch.Tensor(
[
[[1, 0, 0], [0, 1, 0], [0, 0, 1]],
[[2, -5, 3], [0, 7, -2], [-1, 4, 1]],
[[6, 1, 1], [4, -2, 5], [2, 8, 7]],
]
).to(dtype=torch.float32)
expected_det = torch.Tensor([1, 41, -306]).to(dtype=torch.float32)
self.assertClose(_safe_det_3x3(t), expected_det)
device_cuda = torch.device("cuda:0")
self.assertClose(
_safe_det_3x3(t.to(device=device_cuda)), expected_det.to(device=device_cuda)
)
def test_regression(self):
tries = 32
device_cpu = torch.device("cpu")
device_cuda = torch.device("cuda:0")
batch_sizes = np.random.randint(low=1, high=128, size=tries)
for batch_size in batch_sizes:
self._test_det_3x3(batch_size, device_cpu)
self._test_det_3x3(batch_size, device_cuda)
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