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!44558 Add tensor.det() and tensor.ndimension() 

Merge pull request !44558 from shaojunsong/feature/tensor1025
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i-robot 2022-10-27 03:52:38 +00:00 committed by Gitee
parent e54b62bd30 4c3b9a84fe
commit bf99cc4fa1
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9 changed files with 109 additions and 2 deletions
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6
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.det.rst Normal file
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@ -0,0 +1,6 @@
mindspore.Tensor.det
====================
.. py:method:: mindspore.Tensor.det()
参考 `Tensor.matrix_determinant() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.matrix_determinant.html>`_。

6
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.ndimension.rst Normal file
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@ -0,0 +1,6 @@
mindspore.Tensor.ndimension
===========================
.. py:method:: mindspore.Tensor.ndimension()
参考 `Tensor.ndim() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.ndim.html>`_。

2
docs/api/api_python/mindspore/mindspore.Tensor.rst
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@ -156,6 +156,7 @@ Reduction方法
mindspore.Tensor.ger
mindspore.Tensor.log_matrix_determinant
mindspore.Tensor.matrix_determinant
mindspore.Tensor.det
Tensor操作方法
----------------
@ -215,6 +216,7 @@ Array操作
mindspore.Tensor.minimum
mindspore.Tensor.nbytes
mindspore.Tensor.ndim
mindspore.Tensor.ndimension
mindspore.Tensor.nonzero
mindspore.Tensor.narrow
mindspore.Tensor.ptp

2
docs/api/api_python_en/Tensor_list.rst
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@ -161,6 +161,7 @@ Linear Algebraic Methods
mindspore.Tensor.ger
mindspore.Tensor.log_matrix_determinant
mindspore.Tensor.matrix_determinant
mindspore.Tensor.det
Tensor Operation Methods
------------------------
@ -220,6 +221,7 @@ Array Methods
mindspore.Tensor.minimum
mindspore.Tensor.nbytes
mindspore.Tensor.ndim
mindspore.Tensor.ndimension
mindspore.Tensor.nonzero
mindspore.Tensor.narrow
mindspore.Tensor.ptp

4
mindspore/ccsrc/pipeline/jit/resource.cc
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@ -232,7 +232,9 @@ BuiltInTypeMap &GetMethodMap() {
{"log1p", std::string("log1p")}, // P.Log1p()
{"logit", std::string("logit")}, // Logit()
{"log_matrix_determinant", std::string("log_matrix_determinant")}, // log_matrix_determinant()
{"matrix_determinant", std::string("matrix_determinant")}, // log_matrix_determinant()
{"matrix_determinant", std::string("matrix_determinant")}, // matrix_determinant()
{"det", std::string("matrix_determinant")}, // det()
{"ndimension", std::string("ndim_")}, // ndimension()
{"max", std::string("max")}, // P.reduce_max()
{"min", std::string("min")}, // P.reduce_min()
{"pow", std::string("pow")}, // P.Pow()

10
mindspore/python/mindspore/_extends/parse/standard_method.py
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@ -92,6 +92,11 @@ def mean(x, axis=(), keep_dims=False):
return reduce_mean(x, axis)
def ndimension(x):
"""Return the number of tensor dimensions."""
return len(x.shape)
def prod(x, axis=(), keep_dims=False):
"""
Reduces a dimension of a tensor by product all elements in the dimension.
@ -1068,6 +1073,11 @@ def resize(x, *new_shape):
return res.reshape(new_shape)
def det(x):
"""Computes the determinant of one or more square matrices."""
return F.matrix_determinant(x)
def diagonal(x, offset=0, axis1=0, axis2=1):
"""
Returns specified diagonals.

18
mindspore/python/mindspore/common/tensor.py
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@ -627,6 +627,13 @@ class Tensor(Tensor_):
"""
return Tensor(data, dtype=dtype)
def ndimension(self):
r"""
Refer to `Tensor.ndim()
<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.ndim.html>` _.
"""
return len(self._shape)
def set_const_arg(self, const_arg=True):
"""
Specify whether the tensor is a constant when it is used for the argument of a network.
@ -2011,7 +2018,7 @@ class Tensor(Tensor_):
ValueError: If the dimension of self tensor is less than 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
``GPU`` ``CPU``
Examples:
>>> input_x = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]), mindspore.float32)
@ -4244,6 +4251,15 @@ class Tensor(Tensor_):
res = flattened[:new_size]
return res.reshape(new_shape)
def det(self):
r"""
Refer to `Tensor.matrix_determinant()
<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.matrix_determinant.html
>` _.
"""
self._init_check()
return tensor_operator_registry.get('matrix_determinant')(self)
def diagonal(self, offset=0, axis1=0, axis2=1):
"""
Return specified diagonals.

31
tests/st/tensor/test_det.py Normal file
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@ -0,0 +1,31 @@
import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
class Net(nn.Cell):
def construct(self, x):
return x.det()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
def test_det(mode):
"""
Feature: tensor.det()
Description: Verify the result of tensor.det()
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)

32
tests/st/tensor/test_ndimension.py Normal file
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@ -0,0 +1,32 @@
import pytest
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
class Net(nn.Cell):
def construct(self, x):
return x.ndimension()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
def test_ndimension(mode):
"""
Feature: tensor.ndimension()
Description: Verify the result of tensor.ndimension
Expectation: success
"""
context.set_context(mode=mode)
net = Net()
x = Tensor([[1.5, 2.0], [3, 4.6], [0.3, 4.6]], dtype=mstype.float32)
output = net(x)
expected = 2
assert output == expected