def test_scale_double(self, device, dtype): scale_factor = torch.tensor(0.5) camera_matrix = torch.tensor( [[[100.0, 0.0, 50.0], [0.0, 100.0, 50.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype) camera_matrix_expected = torch.tensor( [[[50.0, 0.0, 25.0], [0.0, 50.0, 25.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype) camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor) assert_allclose(camera_matrix_scale, camera_matrix_expected, atol=1e-4, rtol=1e-4)
def test_shape(self, batch_size, device, dtype): B: int = batch_size scale_factor = torch.rand(B, device=device, dtype=dtype) camera_matrix = torch.rand(B, 3, 3, device=device, dtype=dtype) camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor) assert camera_matrix_scale.shape == (B, 3, 3)
def test_smoke_tensor(self, device, dtype): scale_factor = torch.tensor(1.0) camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype) camera_matrix_scale = epi.scale_intrinsics(camera_matrix, scale_factor) assert camera_matrix_scale.shape == (1, 3, 3)