add det method and st test cases.

mindspore/python/mindspore/scipy/linalg.py

@@ -649,3 +649,74 @@ def lu_solve(lu_and_piv, b, trans=0, overwrite_b=False, check_finite=True):
     rhs_vector = m_lu.ndim == b.ndim + 1
     x = lu_solve_core(m_lu, permutation, b, trans)
     return x[..., 0] if rhs_vector else x
+
+
+def _det_2x2(a):
+    return (a[..., 0, 0] * a[..., 1, 1] -
+            a[..., 0, 1] * a[..., 1, 0])
+
+
+def _det_3x3(a):
+    return (a[..., 0, 0] * a[..., 1, 1] * a[..., 2, 2] +
+            a[..., 0, 1] * a[..., 1, 2] * a[..., 2, 0] +
+            a[..., 0, 2] * a[..., 1, 0] * a[..., 2, 1] -
+            a[..., 0, 2] * a[..., 1, 1] * a[..., 2, 0] -
+            a[..., 0, 0] * a[..., 1, 2] * a[..., 2, 1] -
+            a[..., 0, 1] * a[..., 1, 0] * a[..., 2, 2])
+
+
+def det(a, overwrite_a=False, check_finite=True):
+    """
+    Compute the determinant of a matrix
+
+    The determinant of a square matrix is a value derived arithmetically
+    from the coefficients of the matrix.
+
+    The determinant for a 3x3 matrix, for example, is computed as follows::
+
+        a    b    c
+        d    e    f = A
+        g    h    i
+
+        det(A) = a*e*i + b*f*g + c*d*h - c*e*g - b*d*i - a*f*h
+
+    Args:
+        a (Tensor): A square matrix to compute. Note that if the input tensor is not a `float`,
+            then it will be casted to :class:`mstype.float32`.
+        overwrite_a (bool, optional): Allow overwriting data in a (may enhance performance).
+        check_finite (bool, optional): Whether to check that the input matrix contains
+            only finite numbers.
+            Disabling may give a performance gain, but may result in problems
+            (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Raises:
+        ValueError: If :math:`a` is not square.
+
+    Returns:
+        Tensor, Determinant of `a`.
+
+    Examples:
+        >>> import numpy as onp
+        >>> from mindspore.common import Tensor
+        >>> from mindspore.scipy.linalg import det
+        >>> a = Tensor(onp.array([[0, 2, 3], [4, 5, 6], [7, 8, 9]])).astype(onp.float64)
+        >>> det(a)
+        3.0
+    """
+    # special case
+    if a.ndim >= 2 and a.shape[-1] == 2 and a.shape[-2] == 2:
+        return _det_2x2(a)
+    if a.ndim >= 2 and a.shape[-1] == 3 and a.shape[-2] == 3:
+        return _det_3x3(a)
+    if a.ndim < 2 or a.shape[-1] != a.shape[-2]:
+        _raise_value_error("Arguments to det must be [..., n, n], but got shape {}.".format(a.shape))
+
+    if F.dtype(a) not in float_types:
+        a = F.cast(a, mstype.float32)
+
+    lu_matrix, pivot = lu_factor(a)
+    diag = lu_matrix.diagonal(axis1=-2, axis2=-1)
+    pivot_not_equal = (pivot != mnp.arange(a.shape[-1])).astype(mstype.int64)
+    pivot_sign = mnp.count_nonzero(pivot_not_equal, axis=-1)
+    sign = -2. * (pivot_sign % 2) + 1.
+    return sign * P.ReduceProd(keep_dims=False)(diag, -1)

tests/st/scipy_st/test_linalg.py

@@ -23,6 +23,7 @@ import mindspore.nn as nn
 import mindspore.scipy as msp
 from mindspore import context, Tensor
 import mindspore.numpy as mnp
+from mindspore.scipy.linalg import det
 from tests.st.scipy_st.utils import match_array, create_full_rank_matrix, create_sym_pos_matrix, \
     create_random_rank_matrix
 
@@ -328,6 +329,50 @@ def test_lu_solve(n: int, dtype):
     assert onp.allclose(msp_x.asnumpy(), osp_x, rtol=rtol, atol=atol)
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('shape', [(3, 3), (5, 5), (10, 10), (20, 20)])
+@pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
+def test_det(shape, dtype):
+    """
+    Feature: ALL To ALL
+    Description: test cases for det
+    Expectation: the result match to scipy
+    """
+    a = onp.random.random(shape).astype(dtype)
+    sp_det = osp.linalg.det(a)
+    tensor_a = Tensor(a)
+    ms_det = msp.linalg.det(tensor_a)
+    rtol = 1.e-5
+    atol = 1.e-5
+    assert onp.allclose(ms_det.asnumpy(), sp_det, rtol=rtol, atol=atol)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('shape', [(2, 3, 3), (2, 3, 5, 5)])
+@pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
+def test_batch_det(shape, dtype):
+    """
+    Feature: ALL To ALL
+    Description: test batch cases for det
+    Expectation: the result match to scipy
+    """
+    a = onp.random.random(shape).astype(dtype)
+    tensor_a = Tensor(a)
+    ms_det = msp.linalg.det(tensor_a)
+    sp_det = onp.empty(shape=ms_det.shape, dtype=dtype)
+    for index, _ in onp.ndenumerate(sp_det):
+        sp_det[index] = osp.linalg.det(a[index])
+    rtol = 1.e-5
+    atol = 1.e-5
+    assert onp.allclose(ms_det.asnumpy(), sp_det, rtol=rtol, atol=atol)
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.platform_x86_cpu
@@ -351,3 +396,30 @@ def test_block_diag_graph(args):
 
     scipy_res = osp.linalg.block_diag(*args)
     match_array(ms_res.asnumpy(), scipy_res)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('shape', [(3, 3), (5, 5), (10, 10), (20, 20)])
+@pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
+def test_det_graph(shape, dtype):
+    """
+    Feature: ALL To ALL
+    Description: test cases for det in graph mode
+    Expectation: the result match to scipy
+    """
+    context.set_context(mode=context.GRAPH_MODE)
+
+    class TestNet(nn.Cell):
+        def construct(self, a):
+            return det(a)
+
+    a = onp.random.random(shape).astype(dtype)
+    sp_det = osp.linalg.det(a)
+    tensor_a = Tensor(a)
+    ms_det = TestNet()(tensor_a)
+    rtol = 1.e-5
+    atol = 1.e-5
+    assert onp.allclose(ms_det.asnumpy(), sp_det, rtol=rtol, atol=atol)