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!8100 New add matrix inverse ops. 

From: @linqingke
Reviewed-by: 
Signed-off-by:
tags/v1.2.0-rc1
mindspore-ci-bot Gitee 4 years ago
parent
13a69af59d 96183f8b12
commit
1bb9342aa0
7 changed files with 333 additions and 53 deletions
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  1. +26
    -0
      mindspore/ccsrc/backend/kernel_compiler/gpu/math/matrix_inverse_gpu_kernel.cc
  2. +145
    -0
      mindspore/ccsrc/backend/kernel_compiler/gpu/math/matrix_inverse_gpu_kernel.h
  3. +3
    -1
      mindspore/ops/operations/__init__.py
  4. +41
    -0
      mindspore/ops/operations/math_ops.py
  5. +61
    -51
      model_zoo/official/cv/psenet/README.md
  6. +1
    -1
      model_zoo/official/cv/psenet/README_CN.md
  7. +56
    -0
      tests/st/ops/gpu/test_matrix_inverse_op.py

+ 26
- 0
mindspore/ccsrc/backend/kernel_compiler/gpu/math/matrix_inverse_gpu_kernel.cc View File

@@ -0,0 +1,26 @@
/**
* Copyright 2020 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.
*/

#include "backend/kernel_compiler/gpu/math/matrix_inverse_gpu_kernel.h"

namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(MatrixInverse, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
MatrixInverseGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(MatrixInverse, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
MatrixInverseGpuKernel, double)
} // namespace kernel
} // namespace mindspore

+ 145
- 0
mindspore/ccsrc/backend/kernel_compiler/gpu/math/matrix_inverse_gpu_kernel.h View File

@@ -0,0 +1,145 @@
/**
* Copyright 2020 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.
*/

#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_MATRIX_INVERSE_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_MATRIX_INVERSE_GPU_KERNEL_H_
#include <cublas_v2.h>
#include <cuda_runtime_api.h>
#include <vector>
#include <type_traits>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/kernel_constants.h"

namespace mindspore {
namespace kernel {
template <typename T>
class MatrixInverseGpuKernel : public GpuKernel {
public:
MatrixInverseGpuKernel() : input_size_(0), adjoint_(false), batch_size_(1), size_(1) {}
~MatrixInverseGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }

bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *input_addr = GetDeviceAddress<T>(inputs, 0);
T *output_addr = GetDeviceAddress<T>(outputs, 0);
auto lu_batch_addr = GetDeviceAddress<T *>(workspace, 0);
auto inv_batch_addr = GetDeviceAddress<T *>(workspace, 1);
auto pivo_addr = GetDeviceAddress<int>(workspace, 2);
auto info_addr = GetDeviceAddress<int>(workspace, 3);

int len = SizeToInt(size_);
int batchsize = SizeToInt(batch_size_);
for (size_t i = 0; i < batch_size_; i++) {
lu_addr_[i] = input_addr + i * len * len;
inv_addr_[i] = output_addr + i * len * len;
}
CHECK_CUDA_RET_WITH_ERROR(kernel_node_,
cudaMemcpyAsync(lu_batch_addr, lu_addr_.data(), sizeof(T *) * batch_size_,
cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
"cuda memcopy Fail");
CHECK_CUDA_RET_WITH_ERROR(kernel_node_,
cudaMemcpyAsync(inv_batch_addr, inv_addr_.data(), sizeof(T *) * batch_size_,
cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
"cuda memcopy Fail");
if (std::is_same<T, float>::value) {
CHECK_CUBLAS_RET_WITH_EXCEPT(kernel_node_,
cublasSgetrfBatched(handle_, len, reinterpret_cast<float **>(lu_batch_addr), len,
pivo_addr, info_addr, batchsize),
"cublas trsm batched Fail");
CHECK_CUBLAS_RET_WITH_EXCEPT(
kernel_node_,
cublasSgetriBatched(handle_, len, reinterpret_cast<float **>(lu_batch_addr), len, pivo_addr,
reinterpret_cast<float **>(inv_batch_addr), len, info_addr, batchsize),
"cublas trsm batched Fail");
} else if (std::is_same<T, double>::value) {
CHECK_CUBLAS_RET_WITH_EXCEPT(kernel_node_,
cublasDgetrfBatched(handle_, len, reinterpret_cast<double **>(lu_batch_addr), len,
pivo_addr, info_addr, batchsize),
"cublas trsm batched Fail");
CHECK_CUBLAS_RET_WITH_EXCEPT(
kernel_node_,
cublasDgetriBatched(handle_, len, reinterpret_cast<double **>(lu_batch_addr), len, pivo_addr,
reinterpret_cast<double **>(inv_batch_addr), len, info_addr, batchsize),
"cublas trsm batched Fail");
} else {
MS_LOG(EXCEPTION) << "The data type entered must be float or double.";
}

return true;
}

bool Init(const CNodePtr &kernel_node) override {
kernel_node_ = kernel_node;
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCublasHandle();
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);

if (input_shape.empty() || input_shape.size() < 2) {
MS_LOG(EXCEPTION) << "The dim entered needs to be greater than 2, but " << input_shape.size() << " was taken";
}
size_t last_index = input_shape.size() - 1;
if (input_shape[last_index] != input_shape[last_index - 1]) {
MS_LOG(EXCEPTION) << "The last two dimensions of the input matrix should be equal!";
}
size_ = input_shape[last_index];
for (size_t i = 0; i < last_index - 1; i++) {
batch_size_ *= input_shape[i];
}

input_size_ = sizeof(T);
for (auto dim : input_shape) {
input_size_ *= dim;
}
adjoint_ = GetAttr<bool>(kernel_node, "adjoint");
lu_addr_.resize(batch_size_);
inv_addr_.resize(batch_size_);
InitSizeLists();
return true;
}

protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_);
output_size_list_.push_back(input_size_);
size_t lu_size = batch_size_ * sizeof(T *);
workspace_size_list_.push_back(lu_size);
size_t inv_size = batch_size_ * sizeof(T *);
workspace_size_list_.push_back(inv_size);
size_t pivo_size = batch_size_ * size_ * sizeof(int);
workspace_size_list_.push_back(pivo_size);
size_t info_size = batch_size_ * sizeof(int);
workspace_size_list_.push_back(info_size);
}

private:
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
size_t input_size_;
bool adjoint_;
cublasHandle_t handle_;
size_t batch_size_;
size_t size_;
std::vector<T *> lu_addr_;
std::vector<T *> inv_addr_;
};
} // namespace kernel
} // namespace mindspore

#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_MATRIX_INVERSE_GPU_KERNEL_H_

+ 3
- 1
mindspore/ops/operations/__init__.py View File

@@ -54,7 +54,8 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan)
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan,
MatrixInverse)

from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
RandomCategorical, StandardLaplace, Multinomial, UniformCandidateSampler,
@@ -400,6 +401,7 @@ __all__ = [
"Pull",
"ReLUV2",
"SparseToDense",
"MatrixInverse",
]

__all__.sort()

+ 41
- 0
mindspore/ops/operations/math_ops.py View File

@@ -4008,3 +4008,44 @@ class LinSpace(PrimitiveWithInfer):
'dtype': start['dtype'],
'value': None}
return out

class MatrixInverse(PrimitiveWithInfer):
"""
Returns the inverse of the input matrix. If the matrix is irreversible, an error may be reported or an unknown
result may be returned

Args:
adjoint (bool) : An optional bool. Default: False.

Inputs:
- **x** (Tensor) - A matrix to be calculated.
types: float32, double.

Outputs:
Tensor, has the same type and shape as input `x`.

Examples:
>>> x = Tensor(np.random.uniform(-2, 2, (2, 2, 2)), mstype.float32)
>>> matrix_inverse = P.MatrixInverse(adjoint=False)
>>> result = matrix_inverse(x)
[[[ 0.6804 0.8111]
[-2.3257 -1.0616]
[[-0.7074 -0.4963]
[0.1896 -1.5285]]]
"""

@prim_attr_register
def __init__(self, adjoint=False):
"""Initialize MatrixInverse"""
validator.check_value_type("adjoint", adjoint, [bool], self.name)
self.adjoint = adjoint

def infer_dtype(self, x_dtype):
valid_type = [mstype.float32, mstype.double]
validator.check_tensor_dtype_valid("x_dtype", x_dtype, valid_type, self.name)
return x_dtype

def infer_shape(self, x_shape):
validator.check_int(len(x_shape), 2, Rel.GE, self.name, None)
validator.check_equal_int(x_shape[-1], x_shape[-2], self.name, None)
return x_shape

+ 61
- 51
model_zoo/official/cv/psenet/README.md View File

@@ -5,7 +5,7 @@
- [Features](#features)
- [Mixed Precision](#mixed-precision)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
@@ -19,19 +19,20 @@
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [How to use](#how-to-use)
- [Inference](#inference)
- [Inference](#inference)
- [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
- [Transfer Learning](#transfer-learning)
- [Transfer Learning](#transfer-learning)
# [PSENet Description](#contents)
With the development of convolutional neural network, scene text detection technology has been developed rapidly. However, there are still two problems in this algorithm, which hinders its application in industry. On the one hand, most of the existing algorithms require quadrilateral bounding boxes to accurately locate arbitrary shape text. On the other hand, two adjacent instances of text can cause error detection overwriting both instances. Traditionally, a segmentation-based approach can solve the first problem, but usually not the second. To solve these two problems, a new PSENet (PSENet) is proposed, which can accurately detect arbitrary shape text instances. More specifically, PSENet generates different scale kernels for each text instance and gradually expands the minimum scale kernel to a text instance with full shape. Because of the large geometric margins between the minimum scale kernels, our method can effectively segment closed text instances, making it easier to detect arbitrary shape text instances. The effectiveness of PSENet has been verified by numerous experiments on CTW1500, full text, ICDAR 2015, and ICDAR 2017 MLT.
[Paper](https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Shape_Robust_Text_Detection_With_Progressive_Scale_Expansion_Network_CVPR_2019_paper.html): Wenhai Wang, Enze Xie, Xiang Li, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9336-9345
With the development of convolutional neural network, scene text detection technology has been developed rapidly. However, there are still two problems in this algorithm, which hinders its application in industry. On the one hand, most of the existing algorithms require quadrilateral bounding boxes to accurately locate arbitrary shape text. On the other hand, two adjacent instances of text can cause error detection overwriting both instances. Traditionally, a segmentation-based approach can solve the first problem, but usually not the second. To solve these two problems, a new PSENet (PSENet) is proposed, which can accurately detect arbitrary shape text instances. More specifically, PSENet generates different scale kernels for each text instance and gradually expands the minimum scale kernel to a text instance with full shape. Because of the large geometric margins between the minimum scale kernels, our method can effectively segment closed text instances, making it easier to detect arbitrary shape text instances. The effectiveness of PSENet has been verified by numerous experiments on CTW1500, full text, ICDAR 2015, and ICDAR 2017 MLT.
[Paper](https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Shape_Robust_Text_Detection_With_Progressive_Scale_Expansion_Network_CVPR_2019_paper.html): Wenhai Wang, Enze Xie, Xiang Li, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9336-9345
# PSENet Example
## Description
Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.
# [Dataset](#contents)
@@ -39,23 +40,26 @@ Progressive Scale Expansion Network (PSENet) is a text detector which is able to
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
Dataset used: [ICDAR2015](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)
A training set of 1000 images containing about 4500 readable words
A training set of 1000 images containing about 4500 readable words
A testing set containing about 2000 readable words
# [Environment Requirements](#contents)
- Hardware（Ascend）
- Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework
- [MindSpore](http://www.mindspore.cn/install/en)
- [MindSpore](http://www.mindspore.cn/install/en)
- For more information, please check the resources below：
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
- install Mindspore
- install [pyblind11](https://github.com/pybind/pybind11)
- install [Opencv3.4](https://docs.opencv.org/3.4.9/d7/d9f/tutorial_linux_install.html)
- install [Opencv3.4](https://docs.opencv.org/3.4.9/)
# [Quick Start](#contents)
After installing MindSpore via the official website, you can start training and evaluation as follows:
After installing MindSpore via the official website, you can start training and evaluation as follows:
```python
# run distributed training example
sh scripts/run_distribute_train.sh rank_table_file pretrained_model.ckpt
@@ -83,34 +87,34 @@ sh scripts/run_eval_ascend.sh
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```
```path
└── PSENet
├── README.md // descriptions about PSENet
├── scripts
├── run_distribute_train.sh // shell script for distributed
└── run_eval_ascend.sh // shell script for evaluation
├── src
├── __init__.py
├── ETSNET
├── __init__.py
├── base.py // convolution and BN operator
├── dice_loss.py // calculate PSENet loss value
├── etsnet.py // Subnet in PSENet
├── fpn.py // Subnet in PSENet
├── resnet50.py // Subnet in PSENet
├── pse // Subnet in PSENet
├── README.md // descriptions about PSENet
├── scripts
├── run_distribute_train.sh // shell script for distributed
└── run_eval_ascend.sh // shell script for evaluation
├──src
├── __init__.py
├── ETSNET
├── __init__.py
├── base.py // convolution and BN operator
├── dice_loss.py // calculate PSENet loss value
├── etsnet.py // Subnet in PSENet
├── fpn.py // Subnet in PSENet
├── resnet50.py // Subnet in PSENet
├── pse // Subnet in PSENet
├── __init__.py
├── adaptor.cpp
├── adaptor.h
├── Makefile
├── config.py // parameter configuration
├── dataset.py // creating dataset
├── lr_schedule.py // learning ratio generation
└── network_define.py // PSENet architecture
├── export.py // export mindir file
├── mindspore_hub_conf.py // hub config file
├── test.py // test script
└── train.py // training script
├──config.py // parameter configuration
├──dataset.py // creating dataset
├──network_define.py // learning ratio generation
├──export.py // export mindir file
├──mindspore_hub_conf.py // hub config file
├──test.py // test script
├──train.py // training script
```
@@ -120,26 +124,26 @@ sh scripts/run_eval_ascend.sh
Major parameters in train.py and config.py are:
--pre_trained: Whether training from scratch or training based on the
pre-trained model.Optional values are True, False.
pre-trained model.Optional values are True, False.
--device_id: Device ID used to train or evaluate the dataset. Ignore it
when you use train.sh for distributed training.
--device_num: devices used when you use train.sh for distributed training.
```
## [Training Process](#contents)
### Distributed Training
```
```shell
sh scripts/run_distribute_train.sh rank_table_file pretrained_model.ckpt
```
rank_table_file which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
The above shell script will run distribute training in the background. You can view the results through the file
The above shell script will run distribute training in the background. You can view the results through the file
`device[X]/test_*.log`. The loss value will be achieved as follows:
```
```log
# grep "epoch: " device_*/loss.log
device_0/log:epoch: 1, step: 20, loss is 0.80383
device_0/log:epcoh: 2, step: 40, loss is 0.77951
@@ -150,25 +154,32 @@ device_1/log:epcoh: 2, step: 40, loss is 0.76629
```
## [Evaluation Process](#contents)
### run test code
python test.py --ckpt=./device*/ckpt*/ETSNet-*.ckpt
### Eval Script for ICDAR2015
#### Usage
+ step 1: download eval method from [here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization).
+ step 2: click "My Methods" button,then download Evaluation Scripts.
+ step 3: it is recommended to symlink the eval method root to $MINDSPORE/model_zoo/psenet/eval_ic15/. if your folder structure is different,you may need to change the corresponding paths in eval script files.
```
step 1: download eval method from [here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization).
step 2: click "My Methods" button,then download Evaluation Scripts.
step 3: it is recommended to symlink the eval method root to $MINDSPORE/model_zoo/psenet/eval_ic15/. if your folder structure is different,you may need to change the corresponding paths in eval script files.
```shell
sh ./script/run_eval_ascend.sh.sh
```
#### Result
Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean": 0.8076736279747451, "AP": 0}
Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean": 0.8076736279747451, "AP": 0}
# [Model Description](#contents)
## [Performance](#contents)
### Evaluation Performance
### Evaluation Performance
| Parameters | PSENet |
| -------------------------- | ----------------------------------------------------------- |
@@ -186,8 +197,7 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
| Total time | 1pc: 75.48 h; 8pcs: 10.01 h |
| Parameters (M) | 27.36 |
| Checkpoint for Fine tuning | 109.44M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet |
| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet> |
### Inference Performance
@@ -207,11 +217,11 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
```
```python
# Load unseen dataset for inference
dataset = dataset.create_dataset(cfg.data_path, 1, False)
# Define model
# Define model
config.INFERENCE = False
net = ETSNet(config)
net = net.set_train()


+ 1
- 1
model_zoo/official/cv/psenet/README_CN.md View File

@@ -56,7 +56,7 @@
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
- 安装Mindspore
- 安装[pyblind11](https://github.com/pybind/pybind11)
- 安装[Opencv3.4](https://docs.opencv.org/3.4.9/d7/d9f/tutory_linux_install.html)
- 安装[Opencv3.4](https://docs.opencv.org/3.4.9/)

# 快速入门



+ 56
- 0
tests/st/ops/gpu/test_matrix_inverse_op.py View File

@@ -0,0 +1,56 @@
# Copyright 2019 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 matrix_inverseress or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import numpy as np
from numpy.linalg import inv
import pytest

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P


class NetMatrixInverse(nn.Cell):
def __init__(self):
super(NetMatrixInverse, self).__init__()
self.matrix_inverse = P.MatrixInverse()

def construct(self, x):
return self.matrix_inverse(x)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_matrix_inverse():
x0_np = np.random.uniform(-2, 2, (3, 4, 4)).astype(np.float32)
x0 = Tensor(x0_np)
expect0 = inv(x0_np)
error0 = np.ones(shape=expect0.shape) * 1.0e-5

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
matrix_inverse = NetMatrixInverse()
output0 = matrix_inverse(x0)
diff0 = output0.asnumpy() - expect0
assert np.all(diff0 < error0)
assert output0.shape == expect0.shape

context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
matrix_inverse = NetMatrixInverse()
output0 = matrix_inverse(x0)
diff0 = output0.asnumpy() - expect0
assert np.all(diff0 < error0)
assert output0.shape == expect0.shape

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