add api: ops.det

docs/api/api_python/mindspore.ops.rst

@@ -250,8 +250,6 @@ mindspore.ops
     mindspore.ops.logical_or
     mindspore.ops.logical_xor
     mindspore.ops.logit
-    mindspore.ops.log_matrix_determinant
-    mindspore.ops.matrix_determinant
     mindspore.ops.mul
     mindspore.ops.multiply
     mindspore.ops.mvlgamma
@@ -366,15 +364,18 @@ Reduction函数
     mindspore.ops.cholesky
     mindspore.ops.cholesky_inverse
     mindspore.ops.batch_dot
+    mindspore.ops.det
     mindspore.ops.dot
     mindspore.ops.inner
     mindspore.ops.inverse
     mindspore.ops.ger
     mindspore.ops.kron
+    mindspore.ops.log_matrix_determinant
     mindspore.ops.matmul
     mindspore.ops.matrix_solve
     mindspore.ops.matrix_exp
     mindspore.ops.matrix_band_part
+    mindspore.ops.matrix_determinant
     mindspore.ops.matrix_diag
     mindspore.ops.matrix_diag_part
     mindspore.ops.matrix_set_diag

docs/api/api_python/mindspore/Tensor/mindspore.Tensor.swapaxes.rst

@@ -3,15 +3,4 @@ mindspore.Tensor.swapaxes
 
 .. py:method:: mindspore.Tensor.swapaxes(axis1, axis2)
 
-    交换Tensor的两个维度。
-
-    参数：
-        - **axis1** (int) - 第一个维度。
-        - **axis2** (int) - 第二个维度。
-
-    返回：
-        转化后的Tensor，与输入具有相同的数据类型。
-
-    异常：
-        - **TypeError** - `axis1` 或 `axis2` 不是整数。
-        - **ValueError** - `axis1` 或 `axis2` 不在 `[-ndim, ndim-1]` 范围内。
+    详情请参考 :func:`mindspore.ops.swapaxes` 。

docs/api/api_python/ops/mindspore.ops.TripletMarginLoss.rst

@@ -8,7 +8,7 @@ mindspore.ops.TripletMarginLoss
     创建一个标准，用于计算输入Tensor :math:`x` 、 :math:`x2` 和 :math:`x3` 与大于 :math:`0` 的 `margin` 之间的三元组损失值。
     可以用来测量样本之间的相似度。一个三元组包含 `a` 、 `p` 和 `n` （即分别代表示  `anchor` 、 `positive examples` 和 `negative examples` ）。
     所有输入Tensor的shape都应该为 :math:`(N, D)` 。
-    距离交换在V. Balntas、E. Riba等人在论文 `Learning local feature descriptors with triplets and shallow convolutional neural networks <http://158.109.8.37/files/BRP2016.pdf>`_ 中有详细的阐述。
+    距离交换在V. Balntas、E. Riba等人的论文 `Learning local feature descriptors with triplets and shallow convolutional neural networks <http://158.109.8.37/files/BRP2016.pdf>`_ 中有详细的阐述。
 
     对于每个小批量样本，损失值为：
 

docs/api/api_python/ops/mindspore.ops.func_det.rst

@@ -0,0 +1,6 @@
+mindspore.ops.det
+================================
+
+.. py:function:: mindspore.ops.det(x)
+
+    :func:`mindspore.ops.matrix_determinant` 的别名。

docs/api/api_python_en/mindspore.ops.rst

@@ -250,8 +250,6 @@ Element-wise Operations
     mindspore.ops.logical_or
     mindspore.ops.logical_xor
     mindspore.ops.logit
-    mindspore.ops.log_matrix_determinant
-    mindspore.ops.matrix_determinant
     mindspore.ops.mul
     mindspore.ops.multiply
     mindspore.ops.mvlgamma
@@ -366,15 +364,18 @@ Linear Algebraic Functions
     mindspore.ops.cholesky
     mindspore.ops.cholesky_inverse
     mindspore.ops.batch_dot
+    mindspore.ops.det
     mindspore.ops.dot
     mindspore.ops.inner
     mindspore.ops.inverse
     mindspore.ops.ger
     mindspore.ops.kron
+    mindspore.ops.log_matrix_determinant
     mindspore.ops.matmul
     mindspore.ops.matrix_solve
     mindspore.ops.matrix_exp
     mindspore.ops.matrix_band_part
+    mindspore.ops.matrix_determinant
     mindspore.ops.matrix_diag
     mindspore.ops.matrix_diag_part
     mindspore.ops.matrix_set_diag

mindspore/python/mindspore/_extends/parse/standard_method.py

@@ -1321,7 +1321,7 @@ def resize(x, *new_shape):
 
 def det(x):
     """Computes the determinant of one or more square matrices."""
-    return F.matrix_determinant(x)
+    return F.det(x)
 
 
 def diagonal(x, offset=0, axis1=0, axis2=1):

mindspore/python/mindspore/common/tensor.py

@@ -1732,29 +1732,7 @@ class Tensor(Tensor_):
 
     def swapaxes(self, axis1, axis2):
         """
-        Interchange two axes of a tensor.
-
-        Args:
-            axis1 (int): First axis.
-            axis2 (int): Second axis.
-
-        Returns:
-            Transposed tensor, has the same data type as the input.
-
-        Raises:
-            TypeError: If `axis1` or `axis2` is not integer.
-            ValueError: If `axis1` or `axis2` is not in the range of :math:`[-ndim, ndim-1]`.
-
-        Supported Platforms:
-            ``Ascend`` ``GPU`` ``CPU``
-
-        Examples:
-            >>> import numpy as np
-            >>> from mindspore import Tensor
-            >>> x = Tensor(np.ones((2,3,4), dtype=np.float32))
-            >>> output = x.swapaxes(0, 2)
-            >>> print(output.shape)
-            (4,3,2)
+        For details, please refer to :func:`mindspore.ops.swapaxes`.
         """
         self._init_check()
         return tensor_operator_registry.get('swapaxes')(self, axis1, axis2)
@@ -2457,10 +2435,10 @@ class Tensor(Tensor_):
 
     def det(self):
         r"""
-        Refer to :func:`mindspore.Tensor.matrix_determinant`.
+        Refer to :func:`mindspore.ops.det`.
         """
         self._init_check()
-        return tensor_operator_registry.get('matrix_determinant')(self)
+        return tensor_operator_registry.get('det')(self)
 
     def diff(self, n=1, axis=-1, prepend=None, append=None):
         r"""

mindspore/python/mindspore/ops/function/__init__.py

@@ -234,6 +234,7 @@ from .math_func import (
     log_matrix_determinant,
     slogdet,
     matrix_determinant,
+    det,
     linspace,
     matrix_solve,
     maximum,

mindspore/python/mindspore/ops/function/math_func.py

@@ -1308,8 +1308,8 @@ def logdet(x):
         >>> print(output)
         [1.9459091 0.6931454]
     """
-    det = matrix_determinant(x)
-    return log_(det)
+    det_x = matrix_determinant(x)
+    return log_(det_x)
 
 
 def floor(x):
@@ -3224,7 +3224,17 @@ def matrix_determinant(x):
         >>> print(output)
         [-16.5 21. ]
     """
-    return matrix_determinant_(x)
+    return _get_cache_prim(P.MatrixDeterminant)()(x)
+
+
+def det(x):
+    """
+    Alias for :func:`mindspore.ops.matrix_determinant` .
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+    """
+    return matrix_determinant(x)
 
 
 def matrix_exp(x):
@@ -10017,6 +10027,7 @@ __all__ = [
     'logdet',
     'log_matrix_determinant',
     'matrix_determinant',
+    'det',
     'linspace',
     'matrix_solve',
     'std',

mindspore/python/mindspore/ops/functional.py

@@ -196,6 +196,7 @@ tensor_operator_registry.register('random_categorical', random_categorical)
 tensor_operator_registry.register('mirror_pad', P.MirrorPad)
 tensor_operator_registry.register('minimum', P.Minimum)
 tensor_operator_registry.register('matrix_determinant', matrix_determinant)
+tensor_operator_registry.register('det', det)
 tensor_operator_registry.register('log1p', log1p)
 tensor_operator_registry.register('logdet', logdet)
 tensor_operator_registry.register('log_matrix_determinant', log_matrix_determinant)

tests/st/ops/test_func_det.py

@@ -0,0 +1,48 @@
+# Copyright 2023 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import pytest
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore import Tensor
+from mindspore import context
+
+
+class Net(nn.Cell):
+    def construct(self, x):
+        return ops.det(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
+def test_det(mode):
+    """
+    Feature: ops.det(x)
+    Description: Verify the result of ops.det(x)
+    Expectation: success
+    """
+    context.set_context(mode=mode)
+    net = Net()
+    x = Tensor([[1.5, 2.0], [3, 4.6]], dtype=mstype.float32)
+    output = net(x)
+    expected = np.array(0.9)
+    assert np.allclose(output.asnumpy(), expected)