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tensor_inverse_master

This commit is contained in:
yide12 2022-11-22 11:14:49 +08:00
parent dc5d0d782c
commit c4b0223330
16 changed files with 175 additions and 7 deletions
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1
docs/api/api_python/mindspore.ops.rst
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@ -203,6 +203,7 @@ mindspore.ops
mindspore.ops.hypot
mindspore.ops.i0
mindspore.ops.inv
mindspore.ops.inverse
mindspore.ops.invert
mindspore.ops.lcm
mindspore.ops.ldexp

6
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.inverse.rst Normal file
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@ -0,0 +1,6 @@
mindspore.Tensor.inverse
========================
.. py:method:: mindspore.Tensor.inverse()
详情请参考 :func:`mindspore.ops.inverse`。

1
docs/api/api_python/mindspore/mindspore.Tensor.rst
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@ -129,6 +129,7 @@ mindspore.Tensor
mindspore.Tensor.inplace_update
mindspore.Tensor.int
mindspore.Tensor.inv
mindspore.Tensor.inverse
mindspore.Tensor.invert
mindspore.Tensor.isclose
mindspore.Tensor.isfinite

17
docs/api/api_python/ops/mindspore.ops.func_inverse.rst Normal file
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@ -0,0 +1,17 @@
mindspore.ops.inverse
=====================
.. py:function:: mindspore.ops.inverse(x)
计算输入矩阵的逆。
参数：
- **x** (Tensor) - 计算的矩阵。`x` 至少是两维的，最后两个维度大小相同。
返回：
Tensor，shape和类型和 `x` 相同。
异常：
- **TypeError** - `x` 不是Tensor。
- **ValueError** - `x` 最后两个维度的大小不相同。
- **ValueError** - `x` 的维数小于2。

1
docs/api/api_python_en/Tensor_list.rst
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@ -135,6 +135,7 @@
mindspore.Tensor.inplace_update
mindspore.Tensor.int
mindspore.Tensor.inv
mindspore.Tensor.inverse
mindspore.Tensor.invert
mindspore.Tensor.isclose
mindspore.Tensor.isfinite

1
docs/api/api_python_en/mindspore.ops.rst
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@ -204,6 +204,7 @@ Element-by-Element Operations
mindspore.ops.hypot
mindspore.ops.i0
mindspore.ops.inv
mindspore.ops.inverse
mindspore.ops.invert
mindspore.ops.lcm
mindspore.ops.ldexp

1
mindspore/ccsrc/pipeline/jit/resource.cc
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@ -287,6 +287,7 @@ BuiltInTypeMap &GetMethodMap() {
{"is_floating_point", std::string("is_floating_point")}, // is_floating_point()
{"is_signed", std::string("is_signed")}, // is_signed()
{"inv", std::string("inv")}, // inv()
{"inverse", std::string("inverse")}, // inverse()
{"invert", std::string("invert")}, // invert()
{"searchsorted", std::string("searchsorted")}, // P.Select()
{"take", std::string("take")}, // P.GatherNd()

7
mindspore/python/mindspore/_extends/parse/standard_method.py
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@ -1418,6 +1418,13 @@ def inv(x):
return F.inv(x)
def inverse(x):
"""
Computes the inverse of a square matrix.
"""
return F.inverse(x)
def invert(x):
"""
Flips all bits of input tensor element-wise.

7
mindspore/python/mindspore/common/tensor.py
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@ -1478,6 +1478,13 @@ class Tensor(Tensor_):
self._init_check()
return tensor_operator_registry.get('inv')(self)
def inverse(self):
r"""
For details, please refer to :func:`mindspore.ops.inverse`.
"""
self._init_check()
return tensor_operator_registry.get('inverse')(self)
def invert(self):
r"""
For details, please refer to :func:`mindspore.ops.invert`.

1
mindspore/python/mindspore/ops/function/__init__.py
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@ -216,6 +216,7 @@ from .math_func import (
inplace_sub,
inplace_update,
inv,
inverse,
invert,
minimum,
renorm,

31
mindspore/python/mindspore/ops/function/math_func.py
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@ -2123,6 +2123,36 @@ def inv(x):
return inv_(x)
def inverse(x):
"""
Compute the inverse of the input matrix.
Inputs:
- **x** (Tensor) - A matrix to be calculated. Input `x` must be at least two dimensions, and the size of
the last two dimensions must be the same size.
Returns:
Tensor, has the same type and shape as input `x`.
Raises:
TypeError: If `x` is not a Tensor.
ValueError: If the size of the last two dimensions of `x` is not the same.
ValueError: If the dimension of `x` is less than 2.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> x = Tensor([[1., 2.], [3., 4.]], ms.float32)
>>> print(ops.inverse(x))
[[-2. 1. ]
[ 1.5 -0.5]]
"""
if x.dtype in mstype.int_type:
_get_cache_prim(P.Cast)()(x, mstype.float64)
return _get_cache_prim(P.MatrixInverse)()(x)
def invert(x):
r"""
Flips all bits of input tensor element-wise.
@ -7484,6 +7514,7 @@ __all__ = [
'bitwise_or',
'bitwise_xor',
'inv',
'inverse',
'invert',
'erf',
'erfc',

4
mindspore/python/mindspore/ops/function/nn_func.py
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@ -1419,7 +1419,7 @@ def fractional_max_pool2d(input_x, kernel_size, output_size=None, output_ratio=N
raise ValueError(f"For fractional_max_pool2d, 'output_size' and 'output_ratio' can not be specified or None"
f"at the same time, but got {output_ratio} and {output_size} .")
if len(input_x.shape) == 3:
input_x.expend_dims(axis=0)
input_x = input_x.expand_dims(axis=0)
if _random_samples is None:
_random_samples = Tensor([[[0, 0]]], mstype.float32)
if output_ratio is not None:
@ -1517,8 +1517,6 @@ def fractional_max_pool3d(input_x, kernel_size, output_size=None, output_ratio=N
if output_ratio is not None and output_size is not None or output_ratio is None and output_size is None:
raise ValueError(f"For fractional_max_pool2d, 'output_size' and 'output_ratio' can not be specified or None"
f"at the same time, but got {output_ratio} and {output_size} .")
if len(input_x.shape) == 4:
input_x.expend_dims(axis=0)
if _random_samples is None:
_random_samples = Tensor([[[0, 0, 0]]], mstype.float32)
if output_ratio is not None:

1
mindspore/python/mindspore/ops/functional.py
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@ -204,6 +204,7 @@ tensor_operator_registry.register('inv', inv)
tensor_operator_registry.register('logaddexp', logaddexp)
tensor_operator_registry.register('logaddexp2', logaddexp2)
tensor_operator_registry.register('logsumexp', logsumexp)
tensor_operator_registry.register('inverse', inverse)
tensor_operator_registry.register('invert', invert)
tensor_operator_registry.register('hardshrink', P.HShrink)
tensor_operator_registry.register('heaviside', heaviside)

8
tests/st/ops/test_ops_fractional_max_pool.py
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@ -44,7 +44,7 @@ def test_fractional_maxpool2d_normal(mode):
"""
ms.set_context(mode=mode)
net = FractionalMaxPool2dNet()
input_x = Tensor(np.random.rand(25).reshape([1, 1, 5, 5]), mstype.float32)
input_x = Tensor(np.random.rand(25).reshape([1, 5, 5]), mstype.float32)
output1, output2 = net(input_x)
assert output1[0].shape == output1[1].shape == (1, 1, 2, 2)
assert output2[0].shape == output2[1].shape == (1, 1, 2, 2)
@ -88,11 +88,11 @@ def test_fractional_maxpool3d_normal(mode):
Expectation: Success
"""
ms.set_context(mode=mode)
input_x = Tensor(np.random.rand(16).reshape([1, 1, 2, 2, 4]), mstype.float32)
input_x = Tensor(np.random.rand(16).reshape([1, 2, 2, 4]), mstype.float32)
net = FractionalMaxPool3dNet()
output1, output2 = net(input_x)
assert output1[0].shape == output1[1].shape == (1, 1, 1, 1, 2)
assert output2[0].shape == output2[1].shape == (1, 1, 1, 1, 2)
assert output1[0].shape == output1[1].shape == (1, 1, 1, 2)
assert output2[0].shape == output2[1].shape == (1, 1, 1, 2)
input_x = Tensor([[[[[5.76273143e-001, 7.97047436e-001, 5.05385816e-001, 7.98332036e-001],
[5.79880655e-001, 9.75979388e-001, 3.17571498e-002, 8.08261558e-002]],
[[3.82758647e-001, 7.09801614e-001, 4.39641386e-001, 5.71077049e-001],

48
tests/st/ops/test_ops_inverse.py Normal file
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@ -0,0 +1,48 @@
# Copyright 2022 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 as ms
import mindspore.nn as nn
from mindspore import Tensor, ops
class Net(nn.Cell):
def construct(self, x):
return ops.inverse(x)
@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', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_ops_inverse(mode):
"""
Feature: ops.inverse
Description: Verify the result of inverse
Expectation: success
"""
ms.set_context(mode=mode)
x = Tensor([[1., 2, 3],
[4, 5., 6],
[8, 8, 9]], ms.float32)
net = Net()
output = net(x)
expect_output = [[1.0000008, -2.000001, 1.0000005],
[-4.0000014, 5.000002, -2.000001],
[2.6666675, -2.6666675, 1.0000002]]
assert np.allclose(output.asnumpy(), expect_output)

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tests/st/tensor/test_inverse.py Normal file
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@ -0,0 +1,47 @@
# Copyright 2022 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 as ms
import mindspore.nn as nn
class Net(nn.Cell):
def construct(self, x):
return x.inverse()
@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', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_tensor_inverse(mode):
"""
Feature: tensor.inverse
Description: Verify the result of inverse
Expectation: success
"""
ms.set_context(mode=mode)
x = ms.Tensor([[1., 2, 3],
[4, 5., 6],
[8, 8, 9]], ms.float32)
net = Net()
output = net(x)
expect_output = [[1.0000008, -2.000001, 1.0000005],
[-4.0000014, 5.000002, -2.000001],
[2.6666675, -2.6666675, 1.0000002]]
assert np.allclose(output.asnumpy(), expect_output)