def test_funcional_shapes(self, device, dtype, shapes):
input_shape = shapes + (3, )
t = torch.rand(*input_shape, device=device, dtype=dtype)
# Feed batches
cross_product_matrices = []
for i in range(t.shape[1]):
cross_product_matrices.append(
epi.cross_product_matrix(t[:, i, ...]))
cross_product_matrix_parts = torch.stack(cross_product_matrices, dim=1)
# Feed one-shot
cross_product_matrix_whole = epi.cross_product_matrix(t)
assert_close(cross_product_matrix_parts, cross_product_matrix_whole)
def test_mean_std(self, device, dtype):
vec = torch.tensor([[1., 2., 3.]], device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert_allclose(cross_product_matrix[..., 0, 1],
-cross_product_matrix[..., 1, 0])
assert_allclose(cross_product_matrix[..., 0, 2],
-cross_product_matrix[..., 2, 0])
assert_allclose(cross_product_matrix[..., 1, 2],
-cross_product_matrix[..., 2, 1])
def test_shape(self, batch_size, device, dtype):
B = batch_size
vec = torch.rand(B, 3, device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert cross_product_matrix.shape == (B, 3, 3)
def test_smoke(self, device, dtype):
vec = torch.rand(1, 3, device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert cross_product_matrix.shape == (1, 3, 3)
def test_shapes(self, device, dtype, shapes):
input_shape = shapes + (3, )
output_shape = shapes + (3, 3)
t = torch.rand(*input_shape, device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(t)
assert cross_product_matrix.shape == output_shape
