add gray_cnn doc

master/html/.buildinfo

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 # Sphinx build info version 1
-# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: b811412b4dcb327d38006d71aa1bfbad
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 0d3025befe3dc6f9dfad874d86b2ff0a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

master/html/_sources/appdevelop/ai_dsp/gray_cnn.rst.txt

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+GRAY_CNN(图片灰度化处理+图片识别)
+==================================
+
+应用概述
+-------------
+    这里将以图片灰度化加图片识别来介绍AI+DSP应用开发的开发流程。
+    其中图片灰度化可以使用DSP来完成，图片识别则使用AI来完成。
+    灰度化处理使用灰度化公式来进行，图片识别使用CNN模型。
+
+开发流程
+-------------
+
+1. 定义模型
+~~~~~~~~~~~~~~
+
+    - **图片灰度化处理过程。**
+
+    这里实现图片灰度化，使用蓝、绿、红三个通道的值进行加权求和，计算出一个灰度值。
+    这里使用的权重分别是0.114、0.587和0.299，这些数值是基于人眼对不同颜色的敏感度来选择的，
+    用于将彩色图像转换为灰度图像。公式为：
+
+    .. math::
+        Gray = B \times 0.114 + G \times 0.587 + R \times 0.299
+
+    以及定义一个Clip操作，确保灰度值在0到255之间。
+    代码示例如下：
+
+    .. code-block:: python
+        :linenos:
+
+        import mindspore as ms
+        from mindspore.train.serialization import export 
+        import numpy as np
+        from mindspore import nn, ops
+
+        class Gray(nn.Cell):
+            def __init__(self):
+                super(Gray, self).__init__()
+                self.split = ops.Split(axis=2, output_num=3)
+            def construct(self, x):
+                x = self.split(x)
+                b, g, r = x
+                x = (
+                    b * 0.114 + 
+                    g * 0.587 + 
+                    r * 0.299
+                ).squeeze(-1)
+                x = ops.clip_by_value(x, 0, 255)
+                return x
+
+    - **定义AI模型，用于图片识别。**
+
+    这里的AI模型是卷积神经网络（CNN）。CNN主要由卷积，池化，激活等算子组成；
+    CNN模型架构如下表所示：
+
+    .. list-table::
+        :header-rows: 1
+        :widths: 15 20 30 20
+        :align: center
+
+        * - **层级**
+          - **操作类型**
+          - **参数细节**
+          - **输出维度**
+        * - **输入层**
+          - 灰度图像输入
+          - 1通道，尺寸 H×W
+          - H×W×1
+        * - **卷积块1**
+          - Conv2d
+          - 输入通道:1 → 输出通道:32, 卷积核:3×3
+          - H×W×32
+        * - 
+          - ReLU激活
+          - 非线性变换
+          - H×W×32
+        * - 
+          - MaxPool2d
+          - 窗口:2×2, 步长:2
+          - H/2×W/2×32
+        * - **卷积块2**
+          - Conv2d
+          - 输入通道:32 → 输出通道:64, 卷积核:3×3
+          - H/2×W/2×64
+        * - 
+          - ReLU激活
+          - 非线性变换
+          - H/2×W/2×64
+        * - 
+          - MaxPool2d
+          - 窗口:2×2, 步长:2
+          - H/4×W/4×64
+        * - **卷积块3**
+          - Conv2d
+          - 输入通道:64 → 输出通道:128, 卷积核:3×3
+          - H/4×W/4×128
+        * - 
+          - ReLU激活
+          - 非线性变换
+          - H/4×W/4×128
+        * - 
+          - MaxPool2d
+          - 窗口:2×2, 步长:2
+          - H/8×W/8×128
+        * - **全连接层**
+          - Flatten
+          - 展平多维特征图
+          - 32768 (H/8×W/8×128)
+        * - 
+          - Dense
+          - 输入:32768 → 输出:64, 激活:ReLU
+          - 64
+        * - 
+          - Dense (输出层)
+          - 输入:64 → 输出:5, 无激活
+          - 5
+        * - **输出层**
+          - 分类结果
+          - 5类概率分布
+          - 5
+
+    CNN模型用于图片识别，不能直接导出模型,需要训练模型，该部分内容见: :ref:`model_training`
+    
+    定义CNN模型的代码示例如下：
+
+    .. code-block:: python
+        :linenos:  
+
+        import mindspore as ms
+        from mindspore import nn
+        class CNN(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(1, 32, kernel_size=3, has_bias=True)
+            self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+            self.conv2 = nn.Conv2d(32, 64, kernel_size=3, has_bias=True)
+            self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+            self.conv3 = nn.Conv2d(64, 128, kernel_size=3, has_bias=True)
+            self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+            self.flatten = nn.Flatten()
+            self.dense1 = nn.Dense(32768, 64)
+            self.dense2 = nn.Dense(64, 5)
+            self.relu = nn.ReLU()
+
+        def construct(self, x):
+            x = self.relu(self.conv1(x))
+            x = self.pool1(x)
+            x = self.relu(self.conv2(x))
+            x = self.pool2(x)
+            x = self.relu(self.conv3(x))
+            x = self.pool3(x)
+            x = self.flatten(x)
+            x = self.relu(self.dense1(x))
+            x = self.dense2(x)
+            return x
+    
+
+.. _model_training:
+
+2. 模型训练
+~~~~~~~~~~~~~~~
+
+    CNN模型需要进行训练才能正确识别，训练需要数据集，这里已经提前准备好数据集，放在Target文件夹下，
+    使用MindSpore框架进行模型训练，需要导入相关库和模块，定义数据预处理、模型结构、损失函数和优化器等。
+    重新组网时，直接使用 ``nn.GraphCell()`` 接口会导致权重丢失，
+    可以在训练前时使用 ``ms.save_checkpoint()`` 接口保存成ckpt文件，
+    重新组网时，使用 ``ms.load_checkpoint()`` 接口加载ckpt文件即可。
+    以下代码展示了如何加载数据集，进行10次模型训练，以及导出模型。
+    训练以及导出模型代码如下：
+
+    .. code-block:: python
+        :linenos:
+
+        import mindspore as ms
+        from mindspore.train.serialization import export 
+        from mindspore import nn, context
+        import numpy as np
+        from PIL import Image
+        import mindspore.dataset as ds  
+        from mindspore.dataset import py_transforms
+        import mindspore.dataset.vision as CV
+        from mindspore.train.callback import  LossMonitor
+
+        batch_size = 32
+        img_size = (128, 128)
+        data_path = './Target'
+
+        # 数据预处理
+        def rescale_to_0_1(image):  
+            return image / 255.0  
+
+        # 自定义函数，添加 color 通道维度
+        def add_channels(image):  
+            if len(image.shape) == 2:  # 单个图像，没有 cin_channel 维度  
+                image_four_channels = np.expand_dims(image, axis=0)
+            else:
+                pass
+                return image
+            return image_four_channels 
+
+        def export_cnn():
+            context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
+            resize_op = CV.Resize(img_size)
+            rescale_transform = ms.dataset.transforms.Compose([rescale_to_0_1]) 
+            f32_typecast = ms.dataset.transforms.TypeCast(ms.float32)
+            # 将读取的 RGB 转为 GRAY 模式
+            convert_gray = ms.dataset.vision.ConvertColor(ms.dataset.vision.ConvertMode.COLOR_RGB2GRAY)
+
+            transform = [convert_gray, resize_op, f32_typecast, rescale_transform, CV.HWC2CHW()]
+            train_data = ds.ImageFolderDataset(dataset_dir=data_path, decode=True, extensions=[".JPEG", ".PNG", ".JPG"])
+            train_data = train_data.map(input_columns="image", operations= transform)
+            train_data = train_data.map(operations=lambda image: add_channels(image), \
+                                            input_columns=["image"], \
+                                            output_columns=["image"])
+            train_data = train_data.batch(batch_size=batch_size)
+            data_iter = train_data.create_dict_iterator()
+            print("数据集加载完成")
+
+            my_model = CNN()  
+            loss = nn.CrossEntropyLoss(reduction='mean')  
+            optimizer = nn.Adam(my_model.trainable_params(), learning_rate=0.01)  
+            cnn_model = ms.Model(my_model, loss_fn=loss, optimizer=optimizer, metrics={'Accuracy': nn.Accuracy()})
+            print("网络构建完成")
+            num_epoch = 10
+            cnn_model.train(num_epoch, train_data,callbacks=[LossMonitor()],dataset_sink_mode=False)
+            inputs = ms.Tensor(np.random.randn(1, 1, 128, 128).astype(np.float32))
+            ms.save_checkpoint(my_model, 'cnn.ckpt')
+            export(my_model, inputs, file_name='cnn', file_format="MINDIR")
+
+3. 重新组网
+~~~~~~~~~~~~~~~
+
+    在前面章节中，我们已经完成了AI的模型的训练和导出。
+    需要重新构建成一个新的网络结构，可以使用MindSpore框架的 ``nn.GraphCell()`` 接口来实现。
+    该部分内容包括加载训练好的cnn模型，灰度化处理，重新构建网络结构，并导出新的模型。
+    重新组网、导出模型以及测试的代码如下：
+    
+    .. code-block:: python
+        :linenos:
+
+        import cv2
+        import mindspore as ms
+        from mindspore.train.serialization import export 
+        import numpy as np
+        from mindspore import nn, ops, context
+        from CNN import export_cnn
+        from GRAY import export_gray
+        from Connection import export_connection
+
+        class_names = ['BRDM_2', 'BTR_60', 'SLICY', 'T62', 'ZSU_23_4']
+        class_labels = ["装甲侦察车", "装甲运输车", "不明", "坦克", "自行高炮"]
+        class GrayCNN(nn.Cell):
+            def __init__(self):
+                super(GrayCNN, self).__init__()
+                self.split = ops.Split(axis=2, output_num=3)
+                self.conv1 = nn.Conv2d(1, 32, kernel_size=3, has_bias=True)
+                self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv2 = nn.Conv2d(32, 64, kernel_size=3, has_bias=True)
+                self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv3 = nn.Conv2d(64, 128, kernel_size=3, has_bias=True)
+                self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.flatten = nn.Flatten()
+                self.dense1 = nn.Dense(32768, 64)
+                self.dense2 = nn.Dense(64, 5)
+                self.relu = nn.ReLU()
+                self.cnn = nn.GraphCell(ms.load(file_name="cnn.mindir"))
+            def construct(self, x):
+                x = self.split(x)
+                b, g, r = x
+                x = (
+                    b * 0.114 + 
+                    g * 0.587 + 
+                    r * 0.299
+                ).squeeze(-1)
+                x = ops.clip_by_value(x, 0, 255)
+                x = x / 255.0   # 数据归一化
+                x = x.reshape(1, 1, 128, 128)   # 灰度化处理后结果需要重塑成cnn输入形状
+                x = self.cnn(x)
+                return x
+
+        if __name__ == "__main__":
+            export_cnn()
+            context.set_context(mode=context.GRAPH_MODE)
+            # 1. 读取图片（保持原始uint8类型）
+            color_image = cv2.imread("image_origin.jpg")  # 默认uint8
+            resized_image = cv2.resize(
+                color_image, 
+                (128, 128),  # 目标尺寸（width, height）
+                interpolation=cv2.INTER_LINEAR
+            )
+            resized_image = resized_image.astype(np.float32)
+            resized_image_tensor = ms.Tensor(resized_image)
+
+            my_model = GrayCNN()
+            param_dict = ms.load_checkpoint("cnn.ckpt")
+            param_not_load, _ = ms.load_param_into_net(my_model, param_dict, strict_load=True)
+
+            input_tensor = ms.Tensor(np.ones((128, 128, 3), dtype=np.float32))
+            export(my_model, input_tensor, file_name='gray_cnn', file_format='MINDIR')
+
+            reload_cnn = nn.GraphCell(ms.load(file_name='gray_cnn.mindir'))
+            reload_cnn = ms.Model(reload_cnn)
+            m = reload_cnn.predict(resized_image_tensor)
+            print(m)
+            output_np = m.asnumpy()
+            if len(output_np.shape) == 2:  # 对于分类任务，通常输出是[batch_size, num_classes]
+                output_prob = np.exp(output_np) / np.exp(output_np).sum(axis=1, keepdims=True)
+                predicted_class = np.argmax(output_prob, axis=1)
+                print(f"预测结果: {class_labels[predicted_class[0]]}")
+    
+    该代码首先定义了一个新的网络结构GrayCNN，
+    在 ``construct`` 方法中，首先通过gray模型处理输入图像，然后通过归一化和重塑张量，最后cnn模型进行识别。
+    在主函数中，首先导出cnn模型。
+    然后加载cnn模型参数，并使用MindSpore的 ``export`` 方法导出新的网络结构。
+    最后，使用重新组网后的模型进行预测，并输出结果。
+
+4. 转换模型
+~~~~~~~~~~~~~~~~~
+
+    在前面的代码中，我们已经使用了 ``export`` 方法导出了gray_cnn.mindir模型。
+    要将该模型部署到FT78NE平台，需要将MINDIR格式转换成mindspore lite的ms格式模型。
+    要将该模型转换为ms格式，可以使用MindSpore的转换工具(converter_lite)。
+    该工具已经集成在我们的 ``YHFT-IDE`` 中，可以直接在IDE中使用。或者也可以使用命令行工具进行转换。
+
+    转换命令如下：
+
+    .. code-block:: bash
+
+        ./converter_lite --fmk=MINDIR --modelFile=gray_cnn.mindir --outputFile=gray_cnn
+    
+    转换完成后，会在当前目录下生成gray_cnn.ms模型文件。该模型可以使用可视化工具(netron)可以打开该文件，查看模型结构。
+    该工具可以直接在 ``YHFT-IDE`` 中使用，可以从官网下载使用，也可以在线使用。在线地址为：https://netron.app/。
+    
+    该模型文件可视化如图所示：
+
+    .. image:: ../../_static/gray_cnn.png
+        :width: 80%
+        :align: center
+        :alt: gray_cnn模型结构图
+        :scale: 50%
+
+
+
+5. 部署和运行程序
+~~~~~~~~~~~~~~~~~~
+
+    - 在IDE中新建一个Python项目，将下面的 :ref:`python_code` 代码拷贝到项目中，并运行。
+      运行成功后，会在当前目录下生成一个名为 ``gray_cnn.midir`` 的模型文件，以及输出图片的预测结果。
+      结果如图所示：
+
+      .. image:: ../../_static/python_gray_cnn_res.png
+            :width: 80%
+            :align: center
+            :alt: python运行结果
+            :scale: 50%
+
+      将该文件转换为ms格式，并将ms格式模型和测试图片拷贝到FT78NE平台中。
+      
+    - 打开 ``YHFT-IDE`` ，新建工程。输入工程名、路径，工程类型选择 ``Heterogeneous`` ，
+      输入交叉编译工具路径，然后点确定。会生成一个异构模板工程。通过修改 ``data_handler.cc`` 文件中的函数来调整输入输出数据，
+      输入改成读取的图片路径；输出改成相应的后处理。在 ``main`` 函数设置运行后端；修改 ``CMakeLists.txt`` 文件，
+      添加openCV库的lib和include路径， 最后编译该工程，编译成功后将build文件夹下的  ``main`` 拷贝到FT78NE平台中，
+      要和gray_cnn.ms模型同一个文件夹下。
+
+      输入的c++代码示例如下：
+
+    .. code-block:: c++
+        :linenos:
+
+        #include <opencv2/opencv.hpp>
+        int readImage(float *reslut, std::string imagePath) {
+            cv::Mat color_image = cv::imread(imagePath.c_str(), cv::IMREAD_COLOR);
+            if (color_image.empty()) {
+                fprintf(stderr, "read image failed\n");
+                return -1;
+            }
+
+            int width = 128;
+            int height = 128;
+            cv::Mat resized_image;
+            cv::resize(color_image, resized_image, cv::Size(width, height), 0, 0, cv::INTER_LINEAR);
+            resized_image.convertTo(resized_image, CV_32F);
+            const int channels = resized_image.channels();
+            const int element_count = width * height * channels;
+
+            for (int i = 0; i < height; ++i) {
+                const float *src_ptr = resized_image.ptr<float>(i);
+                float *dst_ptr = reslut + i * width * channels;
+                std::memcpy(dst_ptr, src_ptr, width * channels * sizeof(float));
+            }
+            return 0;
+        }
+
+    设置后端代码如下：
+
+    .. code-block:: c++
+        :linenos:
+
+        auto context = std::make_shared<mindspore::Context>();
+        auto &device_list = context->MutableDeviceInfo();
+        context->SetBuiltInDelegate(mindspore::DelegateMode::kPNNA);
+        auto cpu_info = std::make_shared<mindspore::CPUDeviceInfo>();
+        auto dsp_info = std::make_shared<mindspore::FT78NEDeviceInfo>();
+        device_list.push_back(dsp_info);
+        device_list.push_back(cpu_info);
+
+    输出结果后处理代码如下：
+
+    .. code-block:: c++
+        :linenos:
+
+        std::vector<float> floatArrayToVector(const float *array, size_t size) {
+            std::vector<float> vec(array, array + size);
+            return vec;
+        }
+
+        std::vector<float> numpy_exp(const std::vector<float> &x) {
+            std::vector<float> result;
+            result.reserve(x.size());
+            for (float num : x) {
+                result.push_back(exp(num));
+            }
+            return result;
+        }
+
+        float sum_rows(const std::vector<float> &vec, int axis) {
+            float sum = std::accumulate(vec.begin(), vec.end(), 0.0);
+            return sum;
+        }
+
+        std::vector<std::vector<float>> convertTo2D(const std::vector<float> &vec, int rows) {
+            std::vector<std::vector<float>> result;
+            if (vec.empty() || rows <= 0) {
+                return result;
+            }
+            int cols = (vec.size() + rows - 1) / rows;  // 计算列数
+            for (int i = 0; i < rows; ++i) {
+                std::vector<float> row;
+                for (int j = 0; j < cols; ++j) {
+                size_t index = i * cols + j;  // 计算索引
+                if (index < vec.size()) {
+                    row.push_back(vec[index]);
+                } else {
+                    row.push_back(0);  // 填充剩余空间，或者你可以选择不填充
+                }
+                }
+                result.push_back(row);
+            }
+            return result;
+        }
+
+        void GetOutputData(std::vector<MSTensor> &outputs) {
+            for (auto tensor : outputs) {
+                std::cout << "tensor name is:" << tensor.Name() << " tensor size is:" << tensor.DataSize()
+                        << " tensor elements num is:" << tensor.ElementNum() << std::endl;
+                auto out_data = reinterpret_cast<const float *>(tensor.Data().get());
+                std::cout << "output data is:";
+                for (int i = 0; i < tensor.ElementNum() && i <= 50; i++) {
+                std::cout << out_data[i] << " ";
+                }
+                std::cout << std::endl;
+                std::vector<float> out = floatArrayToVector(out_data, tensor.ElementNum());
+                std::vector<float> exp_x = numpy_exp(out);
+                float sums = sum_rows(exp_x, 1);
+                std::vector<float> x1;
+                for (float num : exp_x) {
+                    x1.push_back(num / sums);
+                }
+                std::vector<std::vector<float>> twoD = convertTo2D(x1, 1);
+                std::vector<int> argmax_indices = argmax(twoD);
+                std::vector<std::string> Predicted_class = {"装甲侦察车", "装甲运输车", "不明", "坦克", "自行高炮"};
+                std::cout << "预测结果: " << Predicted_class[argmax_indices[0]] << std::endl;
+            }
+        }
+
+    - 在FT78NE上运行可执行文件 ``main`` ，观察输出结果。与预期结果对比，验证模型推理的正确性。
+      执行命令如下：
+
+      .. code-block:: bash
+        :linenos:
+
+        ./main gray_cnn.ms image_origin.jpg
+
+    执行结果如下图：
+
+    .. image:: ../../_static/ft78ne_gray_cnn_output.png
+            :width: 80%
+            :align: center
+            :alt: FT78NE运行结果
+            :scale: 50%
+
+.. _python_code:
+
+python完整代码示例
+-------------------
+
+    .. code-block:: python
+        :linenos:
+
+        # CNN.py
+        import mindspore as ms
+        from mindspore.train.serialization import export 
+        from mindspore import nn, context
+        import numpy as np
+        from PIL import Image
+        import mindspore.dataset as ds  
+        from mindspore.dataset import py_transforms
+        import mindspore.dataset.vision as CV
+        from mindspore.train.callback import  LossMonitor
+
+        # # Define directories and parameters
+        batch_size = 32
+        img_size = (128, 128)
+        data_path = '../cnn-sar/Target'
+
+        # 数据预处理
+        def rescale_to_0_1(image):  
+            return image / 255.0  
+
+        # 自定义函数，添加 color 通道维度
+        def add_channels(image):  
+            if len(image.shape) == 2:  # 单个图像，没有 cin_channel 维度  
+                # print("before ===> ", image.shape)
+                # 添加 color 通道维度，(128x128) => (1, 128, 128)
+                image_four_channels = np.expand_dims(image, axis=0)
+                # print("after  ===> ", image_four_channels.shape)
+            else:
+                pass
+                return image
+            return image_four_channels  
+
+        class CNN(ms.nn.Cell):
+            def __init__(self):
+                super().__init__()
+                self.conv1 = nn.Conv2d(1, 32, kernel_size=3, has_bias=True)
+                self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv2 = nn.Conv2d(32, 64, kernel_size=3, has_bias=True)
+                self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv3 = nn.Conv2d(64, 128, kernel_size=3, has_bias=True)
+                self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.flatten = nn.Flatten()
+                self.dense1 = nn.Dense(32768, 64)
+                self.dense2 = nn.Dense(64, 5)
+                self.relu = nn.ReLU()
+
+            def construct(self, x):
+                x = self.relu(self.conv1(x))
+                x = self.pool1(x)
+                x = self.relu(self.conv2(x))
+                x = self.pool2(x)
+                x = self.relu(self.conv3(x))
+                x = self.pool3(x)
+                x = self.flatten(x)
+                x = self.relu(self.dense1(x))
+                x = self.dense2(x)
+                return x
+
+        def export_cnn():
+            context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
+            resize_op = CV.Resize(img_size)
+            rescale_transform = ms.dataset.transforms.Compose([rescale_to_0_1]) 
+            f32_typecast = ms.dataset.transforms.TypeCast(ms.float32)
+            # 将读取的 RGB 转为 GRAY 模式
+            convert_gray = ms.dataset.vision.ConvertColor(ms.dataset.vision.ConvertMode.COLOR_RGB2GRAY)
+
+            transform = [convert_gray, resize_op, f32_typecast, rescale_transform, CV.HWC2CHW()]
+            train_data = ds.ImageFolderDataset(dataset_dir=data_path, decode=True, extensions=[".JPEG", ".PNG", ".JPG"])
+            train_data = train_data.map(input_columns="image", operations= transform)
+            train_data = train_data.map(operations=lambda image: add_channels(image), \
+                                            input_columns=["image"], \
+                                            output_columns=["image"])
+            train_data = train_data.batch(batch_size=batch_size)
+            data_iter = train_data.create_dict_iterator()
+            print("数据集加载完成")
+            my_model = CNN()  
+            loss = nn.CrossEntropyLoss(reduction='mean')  
+            optimizer = nn.Adam(my_model.trainable_params(), learning_rate=0.01)  
+            cnn_model = ms.Model(my_model, loss_fn=loss, optimizer=optimizer, metrics={'Accuracy': nn.Accuracy()})
+            print("网络构建完成")
+            num_epoch = 10
+            cnn_model.train(num_epoch, train_data,callbacks=[LossMonitor()],dataset_sink_mode=False)
+            inputs = ms.Tensor(np.random.randn(1, 1, 128, 128).astype(np.float32))
+            ms.save_checkpoint(my_model, 'cnn.ckpt')
+            export(my_model, inputs, file_name='cnn', file_format="MINDIR")
+
+    .. code-block:: python
+        :linenos:
+
+        # GRAY_CNN.py
+        import cv2
+        import mindspore as ms
+        from mindspore.train.serialization import export 
+        import numpy as np
+        from mindspore import nn, ops, context
+        from CNN import export_cnn
+
+        class_names = ['BRDM_2', 'BTR_60', 'SLICY', 'T62', 'ZSU_23_4']
+        class_labels = ["装甲侦察车", "装甲运输车", "不明", "坦克", "自行高炮"]
+
+        class GrayCNN(nn.Cell):
+            def __init__(self):
+                super(GrayCNN, self).__init__()
+                self.split = ops.Split(axis=2, output_num=3)
+                self.conv1 = nn.Conv2d(1, 32, kernel_size=3, has_bias=True)
+                self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv2 = nn.Conv2d(32, 64, kernel_size=3, has_bias=True)
+                self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.conv3 = nn.Conv2d(64, 128, kernel_size=3, has_bias=True)
+                self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+                self.flatten = nn.Flatten()
+                self.dense1 = nn.Dense(32768, 64)
+                self.dense2 = nn.Dense(64, 5)
+                self.relu = nn.ReLU()
+                self.cnn = nn.GraphCell(ms.load(file_name="cnn.mindir"))
+
+            def construct(self, x):
+                x = self.split(x)
+                b, g, r = x
+                x = (
+                    b * 0.114 + 
+                    g * 0.587 + 
+                    r * 0.299
+                ).squeeze(-1)
+                x = ops.clip_by_value(x, 0, 255)
+                x = x / 255.0   # 数据归一化
+                x = x.reshape(1, 1, 128, 128)   # 灰度化处理后结果需要重塑成cnn输入形状
+                x = self.cnn(x)
+                return x
+
+        if __name__ == "__main__":
+            export_gray()
+            export_connection()
+            export_cnn()
+            context.set_context(mode=context.GRAPH_MODE)
+            # 1. 读取图片（保持原始uint8类型）
+            color_image = cv2.imread("image_origin.jpg")  # 默认uint8
+            resized_image = cv2.resize(
+                color_image, 
+                (128, 128),  # 目标尺寸（width, height）
+                interpolation=cv2.INTER_LINEAR
+            )
+            resized_image = resized_image.astype(np.float32)
+            resized_image_tensor = ms.Tensor(resized_image)
+            my_model = GrayCNN()
+            param_dict = ms.load_checkpoint("cnn.ckpt")
+            param_not_load, _ = ms.load_param_into_net(my_model, param_dict, strict_load=True)
+            input_tensor = ms.Tensor(np.ones((128, 128, 3), dtype=np.float32))
+            export(my_model, input_tensor, file_name='gray_cnn', file_format='MINDIR')
+            reload_cnn = nn.GraphCell(ms.load(file_name='gray_cnn.mindir'))
+            reload_cnn = ms.Model(reload_cnn)
+            m = reload_cnn.predict(resized_image_tensor)
+            print(m)
+            output_np = m.asnumpy()
+            if len(output_np.shape) == 2:  # 对于分类任务，通常输出是[batch_size, num_classes]
+                output_prob = np.exp(output_np) / np.exp(output_np).sum(axis=1, keepdims=True)
+                predicted_class = np.argmax(output_prob, axis=1)
+                print(f"预测结果: {class_labels[predicted_class[0]]}")
+

master/html/_sources/appdevelop/ai_dsp/index.rst.txt

@@ -4,5 +4,7 @@ AI+DSP应用示例
 .. toctree::
    :maxdepth: 1
    
+   gray_cnn
+   
    
 

master/html/_sources/functionlib/dsplib/log10.rst.txt

@@ -29,7 +29,7 @@ Log10
 
     .. code-block:: c
         :linenos:
-        :emphasize-lines: 8
+        :emphasize-lines: 9
 
         //MT7004示例
         #include <stdio.h>

master/html/_static/basic.css

@@ -1,5 +1,12 @@
 /*
+ * basic.css
+ * ~~~~~~~~~
+ *
  * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
  */
 
 /* -- main layout ----------------------------------------------------------- */
@@ -108,11 +115,15 @@ img {
 /* -- search page ----------------------------------------------------------- */
 
 ul.search {
-    margin-top: 10px;
+    margin: 10px 0 0 20px;
+    padding: 0;
 }
 
 ul.search li {
-    padding: 5px 0;
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
 }
 
 ul.search li a {
@@ -226,10 +237,6 @@ a.headerlink {
     visibility: hidden;
 }
 
-a:visited {
-    color: #551A8B;
-}
-
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,

master/html/_static/doctools.js

@@ -1,5 +1,12 @@
 /*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
  * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
  */
 "use strict";
 

master/html/_static/documentation_options.js

@@ -1,4 +1,5 @@
-const DOCUMENTATION_OPTIONS = {
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
     VERSION: 'alpha',
     LANGUAGE: 'zh-CN',
     COLLAPSE_INDEX: false,

master/html/_static/language_data.js

@@ -1,12 +1,19 @@
 /*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
  */
 
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, if available */
+/* Non-minified version is copied as a separate JS file, is available */
 
 /**
  * Porter Stemmer

master/html/_static/searchtools.js

@@ -1,5 +1,12 @@
 /*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
  * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
  */
 "use strict";
 
@@ -13,7 +20,7 @@ if (typeof Scorer === "undefined") {
     // and returns the new score.
     /*
     score: result => {
-      const [docname, title, anchor, descr, score, filename, kind] = result
+      const [docname, title, anchor, descr, score, filename] = result
       return score
     },
     */
@@ -40,14 +47,6 @@ if (typeof Scorer === "undefined") {
   };
 }
 
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
-    static get index() { return  "index"; }
-    static get object() { return "object"; }
-    static get text() { return "text"; }
-    static get title() { return "title"; }
-}
-
 const _removeChildren = (element) => {
   while (element && element.lastChild) element.removeChild(element.lastChild);
 };
@@ -58,20 +57,16 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms, highlightTerms) => {
+const _displayItem = (item, searchTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-  const contentRoot = document.documentElement.dataset.content_root;
 
-  const [docName, title, anchor, descr, score, _filename, kind] = item;
+  const [docName, title, anchor, descr, score, _filename] = item;
 
   let listItem = document.createElement("li");
-  // Add a class representing the item's type:
-  // can be used by a theme's CSS selector for styling
-  // See SearchResultKind for the class names.
-  listItem.classList.add(`kind-${kind}`);
   let requestUrl;
   let linkUrl;
   if (docBuilder === "dirhtml") {
@@ -80,35 +75,28 @@ const _displayItem = (item, searchTerms, highlightTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = contentRoot + dirname;
+    requestUrl = docUrlRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = contentRoot + docName + docFileSuffix;
+    requestUrl = docUrlRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr) {
+  if (descr)
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
-    // highlight search terms in the description
-    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
-  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, anchor)
+            Search.makeSearchSummary(data, searchTerms)
           );
-        // highlight search terms in the summary
-        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -120,46 +108,27 @@ const _finishSearch = (resultCount) => {
       "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
     );
   else
-    Search.status.innerText = Documentation.ngettext(
-      "Search finished, found one page matching the search query.",
-      "Search finished, found ${resultCount} pages matching the search query.",
-      resultCount,
-    ).replace('${resultCount}', resultCount);
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
 };
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms,
-  highlightTerms,
+  searchTerms
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms, highlightTerms);
+    _displayItem(results.pop(), searchTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      () => _displayNextItem(results, resultCount, searchTerms),
       5
     );
   }
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
-  const leftScore = a[4];
-  const rightScore = b[4];
-  if (leftScore === rightScore) {
-    // same score: sort alphabetically
-    const leftTitle = a[1].toLowerCase();
-    const rightTitle = b[1].toLowerCase();
-    if (leftTitle === rightTitle) return 0;
-    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-  }
-  return leftScore > rightScore ? 1 : -1;
-};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -183,26 +152,13 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString, anchor) => {
+  htmlToText: (htmlString) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlink", "script", "style"]) {
-      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
-    }
-    if (anchor) {
-      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
-      if (anchorContent) return anchorContent.textContent;
-
-      console.warn(
-        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
-      );
-    }
-
-    // if anchor not specified or not found, fall back to main content
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent) return docContent.textContent;
-
+    if (docContent !== undefined) return docContent.textContent;
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
     );
     return "";
   },
@@ -255,7 +211,6 @@ const Search = {
     searchSummary.classList.add("search-summary");
     searchSummary.innerText = "";
     const searchList = document.createElement("ul");
-    searchList.setAttribute("role", "list");
     searchList.classList.add("search");
 
     const out = document.getElementById("search-results");
@@ -276,7 +231,16 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  _parseQuery: (query) => {
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -312,40 +276,22 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
-    // Collect multiple result groups to be sorted separately and then ordered.
-    // Each is an array of [docname, title, anchor, descr, score, filename, kind].
-    const normalResults = [];
-    const nonMainIndexResults = [];
-
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase().trim();
+    const queryLower = query.toLowerCase();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
-          const score = Math.round(Scorer.title * queryLower.length / title.length);
-          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
-          normalResults.push([
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
             null,
-            score + boost,
+            score,
             filenames[file],
-            SearchResultKind.title,
           ]);
         }
       }
@@ -354,48 +300,46 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id, isMain] of foundEntries) {
-          const score = Math.round(100 * queryLower.length / entry.length);
-          const result = [
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-            SearchResultKind.index,
-          ];
-          if (isMain) {
-            normalResults.push(result);
-          } else {
-            nonMainIndexResults.push(result);
-          }
+          ]);
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+      results.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
-      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
-    }
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
 
-    // Sort each group of results by score and then alphabetically by name.
-    normalResults.sort(_orderResultsByScoreThenName);
-    nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
-    // Combine the result groups in (reverse) order.
-    // Non-main index entries are typically arbitrary cross-references,
-    // so display them after other results.
-    let results = [...nonMainIndexResults, ...normalResults];
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -409,19 +353,14 @@ const Search = {
       return acc;
     }, []);
 
-    return results.reverse();
-  },
-
-  query: (query) => {
-    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
-    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+    results = results.reverse();
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+    _displayNextItem(results, results.length, searchTerms);
   },
 
   /**
@@ -485,7 +424,6 @@ const Search = {
         descr,
         score,
         filenames[match[0]],
-        SearchResultKind.object,
       ]);
     };
     Object.keys(objects).forEach((prefix) =>
@@ -520,18 +458,14 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        if (!terms.hasOwnProperty(word)) {
-          Object.keys(terms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: terms[term], score: Scorer.partialTerm });
-          });
-        }
-        if (!titleTerms.hasOwnProperty(word)) {
-          Object.keys(titleTerms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
-          });
-        }
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
       }
 
       // no match but word was a required one
@@ -554,8 +488,9 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (!fileMap.has(file)) fileMap.set(file, [word]);
-        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
       });
     });
 
@@ -596,7 +531,6 @@ const Search = {
         null,
         score,
         filenames[file],
-        SearchResultKind.text,
       ]);
     }
     return results;
@@ -607,8 +541,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords, anchor) => {
-    const text = Search.htmlToText(htmlText, anchor);
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();

master/html/_static/sphinx_highlight.js

@@ -29,19 +29,14 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          rest,
+          document.createTextNode(val.substr(pos + text.length)),
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
-      /* There may be more occurrences of search term in this node. So call this
-       * function recursively on the remaining fragment.
-       */
-      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -145,10 +140,5 @@ const SphinxHighlight = {
   },
 };
 
-_ready(() => {
-  /* Do not call highlightSearchWords() when we are on the search page.
-   * It will highlight words from the *previous* search query.
-   */
-  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
-  SphinxHighlight.initEscapeListener();
-});
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);

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@@ -0,0 +1,765 @@
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">GRAY_CNN(图片灰度化处理+图片识别)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#id1">应用概述</a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="gray-cnn">
+<h1>GRAY_CNN(图片灰度化处理+图片识别)<a class="headerlink" href="#gray-cnn" title="此标题的永久链接"></a></h1>
+<section id="id1">
+<h2>应用概述<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h2>
+<blockquote>
+<div><p>这里将以图片灰度化加图片识别来介绍AI+DSP应用开发的开发流程。
+其中图片灰度化可以使用DSP来完成，图片识别则使用AI来完成。
+灰度化处理使用灰度化公式来进行，图片识别使用CNN模型。</p>
+</div></blockquote>
+</section>
+<section id="id2">
+<h2>开发流程<a class="headerlink" href="#id2" title="此标题的永久链接"></a></h2>
+<section id="id3">
+<h3>1. 定义模型<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h3>
+<blockquote>
+<div><ul class="simple">
+<li><p><strong>图片灰度化处理过程。</strong></p></li>
+</ul>
+<p>这里实现图片灰度化，使用蓝、绿、红三个通道的值进行加权求和，计算出一个灰度值。
+这里使用的权重分别是0.114、0.587和0.299，这些数值是基于人眼对不同颜色的敏感度来选择的，
+用于将彩色图像转换为灰度图像。公式为：</p>
+<div class="math notranslate nohighlight">
+\[Gray = B \times 0.114 + G \times 0.587 + R \times 0.299\]</div>
+<p>以及定义一个Clip操作，确保灰度值在0到255之间。
+代码示例如下：</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 2</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.serialization</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>
+<span class="linenos"> 3</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="linenos"> 4</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">ops</span>
+<span class="linenos"> 5</span>
+<span class="linenos"> 6</span><span class="k">class</span><span class="w"> </span><span class="nc">Gray</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
+<span class="linenos"> 7</span>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos"> 8</span>        <span class="nb">super</span><span class="p">(</span><span class="n">Gray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos"> 9</span>        <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">Split</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">output_num</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="linenos">10</span>    <span class="k">def</span><span class="w"> </span><span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">11</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">12</span>        <span class="n">b</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">r</span> <span class="o">=</span> <span class="n">x</span>
+<span class="linenos">13</span>        <span class="n">x</span> <span class="o">=</span> <span class="p">(</span>
+<span class="linenos">14</span>            <span class="n">b</span> <span class="o">*</span> <span class="mf">0.114</span> <span class="o">+</span>
+<span class="linenos">15</span>            <span class="n">g</span> <span class="o">*</span> <span class="mf">0.587</span> <span class="o">+</span>
+<span class="linenos">16</span>            <span class="n">r</span> <span class="o">*</span> <span class="mf">0.299</span>
+<span class="linenos">17</span>        <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="linenos">18</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">clip_by_value</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span>
+<span class="linenos">19</span>        <span class="k">return</span> <span class="n">x</span>
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><strong>定义AI模型，用于图片识别。</strong></p></li>
+</ul>
+<p>这里的AI模型是卷积神经网络（CNN）。CNN主要由卷积，池化，激活等算子组成；
+CNN模型架构如下表所示：</p>
+<table class="docutils align-center">
+<colgroup>
+<col style="width: 17.6%" />
+<col style="width: 23.5%" />
+<col style="width: 35.3%" />
+<col style="width: 23.5%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p><strong>层级</strong></p></th>
+<th class="head"><p><strong>操作类型</strong></p></th>
+<th class="head"><p><strong>参数细节</strong></p></th>
+<th class="head"><p><strong>输出维度</strong></p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><strong>输入层</strong></p></td>
+<td><p>灰度图像输入</p></td>
+<td><p>1通道，尺寸 H×W</p></td>
+<td><p>H×W×1</p></td>
+</tr>
+<tr class="row-odd"><td><p><strong>卷积块1</strong></p></td>
+<td><p>Conv2d</p></td>
+<td><p>输入通道:1 → 输出通道:32, 卷积核:3×3</p></td>
+<td><p>H×W×32</p></td>
+</tr>
+<tr class="row-even"><td></td>
+<td><p>ReLU激活</p></td>
+<td><p>非线性变换</p></td>
+<td><p>H×W×32</p></td>
+</tr>
+<tr class="row-odd"><td></td>
+<td><p>MaxPool2d</p></td>
+<td><p>窗口:2×2, 步长:2</p></td>
+<td><p>H/2×W/2×32</p></td>
+</tr>
+<tr class="row-even"><td><p><strong>卷积块2</strong></p></td>
+<td><p>Conv2d</p></td>
+<td><p>输入通道:32 → 输出通道:64, 卷积核:3×3</p></td>
+<td><p>H/2×W/2×64</p></td>
+</tr>
+<tr class="row-odd"><td></td>
+<td><p>ReLU激活</p></td>
+<td><p>非线性变换</p></td>
+<td><p>H/2×W/2×64</p></td>
+</tr>
+<tr class="row-even"><td></td>
+<td><p>MaxPool2d</p></td>
+<td><p>窗口:2×2, 步长:2</p></td>
+<td><p>H/4×W/4×64</p></td>
+</tr>
+<tr class="row-odd"><td><p><strong>卷积块3</strong></p></td>
+<td><p>Conv2d</p></td>
+<td><p>输入通道:64 → 输出通道:128, 卷积核:3×3</p></td>
+<td><p>H/4×W/4×128</p></td>
+</tr>
+<tr class="row-even"><td></td>
+<td><p>ReLU激活</p></td>
+<td><p>非线性变换</p></td>
+<td><p>H/4×W/4×128</p></td>
+</tr>
+<tr class="row-odd"><td></td>
+<td><p>MaxPool2d</p></td>
+<td><p>窗口:2×2, 步长:2</p></td>
+<td><p>H/8×W/8×128</p></td>
+</tr>
+<tr class="row-even"><td><p><strong>全连接层</strong></p></td>
+<td><p>Flatten</p></td>
+<td><p>展平多维特征图</p></td>
+<td><p>32768 (H/8×W/8×128)</p></td>
+</tr>
+<tr class="row-odd"><td></td>
+<td><p>Dense</p></td>
+<td><p>输入:32768 → 输出:64, 激活:ReLU</p></td>
+<td><p>64</p></td>
+</tr>
+<tr class="row-even"><td></td>
+<td><p>Dense (输出层)</p></td>
+<td><p>输入:64 → 输出:5, 无激活</p></td>
+<td><p>5</p></td>
+</tr>
+<tr class="row-odd"><td><p><strong>输出层</strong></p></td>
+<td><p>分类结果</p></td>
+<td><p>5类概率分布</p></td>
+<td><p>5</p></td>
+</tr>
+</tbody>
+</table>
+<p>CNN模型用于图片识别，不能直接导出模型,需要训练模型，该部分内容见: <a class="reference internal" href="#model-training"><span class="std std-ref">2. 模型训练</span></a></p>
+<p>定义CNN模型的代码示例如下：</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 2</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span>
+<span class="linenos"> 3</span><span class="k">class</span><span class="w"> </span><span class="nc">CNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
+<span class="linenos"> 4</span><span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos"> 5</span>    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos"> 6</span>    <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos"> 7</span>    <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos"> 8</span>    <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos"> 9</span>    <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">10</span>    <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">11</span>    <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">12</span>    <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span>
+<span class="linenos">13</span>    <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32768</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
+<span class="linenos">14</span>    <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="linenos">15</span>    <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+<span class="linenos">16</span>
+<span class="linenos">17</span><span class="k">def</span><span class="w"> </span><span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">18</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">19</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">20</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">21</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">22</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">23</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">24</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">25</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">26</span>    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">27</span>    <span class="k">return</span> <span class="n">x</span>
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+<section id="model-training">
+<span id="id4"></span><h3>2. 模型训练<a class="headerlink" href="#model-training" title="此标题的永久链接"></a></h3>
+<blockquote>
+<div><p>CNN模型需要进行训练才能正确识别，训练需要数据集，这里已经提前准备好数据集，放在Target文件夹下，
+使用MindSpore框架进行模型训练，需要导入相关库和模块，定义数据预处理、模型结构、损失函数和优化器等。
+重新组网时，直接使用 <code class="docutils literal notranslate"><span class="pre">nn.GraphCell()</span></code> 接口会导致权重丢失，
+可以在训练前时使用 <code class="docutils literal notranslate"><span class="pre">ms.save_checkpoint()</span></code> 接口保存成ckpt文件，
+重新组网时，使用 <code class="docutils literal notranslate"><span class="pre">ms.load_checkpoint()</span></code> 接口加载ckpt文件即可。
+以下代码展示了如何加载数据集，进行10次模型训练，以及导出模型。
+训练以及导出模型代码如下：</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 2</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.serialization</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>
+<span class="linenos"> 3</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">context</span>
+<span class="linenos"> 4</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="linenos"> 5</span><span class="kn">from</span><span class="w"> </span><span class="nn">PIL</span><span class="w"> </span><span class="kn">import</span> <span class="n">Image</span>
+<span class="linenos"> 6</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore.dataset</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ds</span>
+<span class="linenos"> 7</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.dataset</span><span class="w"> </span><span class="kn">import</span> <span class="n">py_transforms</span>
+<span class="linenos"> 8</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore.dataset.vision</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">CV</span>
+<span class="linenos"> 9</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.callback</span><span class="w"> </span><span class="kn">import</span>  <span class="n">LossMonitor</span>
+<span class="linenos">10</span>
+<span class="linenos">11</span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
+<span class="linenos">12</span><span class="n">img_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
+<span class="linenos">13</span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;./Target&#39;</span>
+<span class="linenos">14</span>
+<span class="linenos">15</span><span class="c1"># 数据预处理</span>
+<span class="linenos">16</span><span class="k">def</span><span class="w"> </span><span class="nf">rescale_to_0_1</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
+<span class="linenos">17</span>    <span class="k">return</span> <span class="n">image</span> <span class="o">/</span> <span class="mf">255.0</span>
+<span class="linenos">18</span>
+<span class="linenos">19</span><span class="c1"># 自定义函数，添加 color 通道维度</span>
+<span class="linenos">20</span><span class="k">def</span><span class="w"> </span><span class="nf">add_channels</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
+<span class="linenos">21</span>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># 单个图像，没有 cin_channel 维度</span>
+<span class="linenos">22</span>        <span class="n">image_four_channels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="linenos">23</span>    <span class="k">else</span><span class="p">:</span>
+<span class="linenos">24</span>        <span class="k">pass</span>
+<span class="linenos">25</span>        <span class="k">return</span> <span class="n">image</span>
+<span class="linenos">26</span>    <span class="k">return</span> <span class="n">image_four_channels</span>
+<span class="linenos">27</span>
+<span class="linenos">28</span><span class="k">def</span><span class="w"> </span><span class="nf">export_cnn</span><span class="p">():</span>
+<span class="linenos">29</span>    <span class="n">context</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">PYNATIVE_MODE</span><span class="p">,</span> <span class="n">device_target</span><span class="o">=</span><span class="s2">&quot;CPU&quot;</span><span class="p">)</span>
+<span class="linenos">30</span>    <span class="n">resize_op</span> <span class="o">=</span> <span class="n">CV</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">img_size</span><span class="p">)</span>
+<span class="linenos">31</span>    <span class="n">rescale_transform</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">rescale_to_0_1</span><span class="p">])</span>
+<span class="linenos">32</span>    <span class="n">f32_typecast</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">TypeCast</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="linenos">33</span>    <span class="c1"># 将读取的 RGB 转为 GRAY 模式</span>
+<span class="linenos">34</span>    <span class="n">convert_gray</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ConvertColor</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ConvertMode</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
+<span class="linenos">35</span>
+<span class="linenos">36</span>    <span class="n">transform</span> <span class="o">=</span> <span class="p">[</span><span class="n">convert_gray</span><span class="p">,</span> <span class="n">resize_op</span><span class="p">,</span> <span class="n">f32_typecast</span><span class="p">,</span> <span class="n">rescale_transform</span><span class="p">,</span> <span class="n">CV</span><span class="o">.</span><span class="n">HWC2CHW</span><span class="p">()]</span>
+<span class="linenos">37</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">dataset_dir</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span> <span class="n">decode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;.JPEG&quot;</span><span class="p">,</span> <span class="s2">&quot;.PNG&quot;</span><span class="p">,</span> <span class="s2">&quot;.JPG&quot;</span><span class="p">])</span>
+<span class="linenos">38</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">input_columns</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">operations</span><span class="o">=</span> <span class="n">transform</span><span class="p">)</span>
+<span class="linenos">39</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">operations</span><span class="o">=</span><span class="k">lambda</span> <span class="n">image</span><span class="p">:</span> <span class="n">add_channels</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> \
+<span class="linenos">40</span>                                    <span class="n">input_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span> \
+<span class="linenos">41</span>                                    <span class="n">output_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">])</span>
+<span class="linenos">42</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
+<span class="linenos">43</span>    <span class="n">data_iter</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">create_dict_iterator</span><span class="p">()</span>
+<span class="linenos">44</span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;数据集加载完成&quot;</span><span class="p">)</span>
+<span class="linenos">45</span>
+<span class="linenos">46</span>    <span class="n">my_model</span> <span class="o">=</span> <span class="n">CNN</span><span class="p">()</span>
+<span class="linenos">47</span>    <span class="n">loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">(</span><span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+<span class="linenos">48</span>    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">trainable_params</span><span class="p">(),</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="linenos">49</span>    <span class="n">cnn_model</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()})</span>
+<span class="linenos">50</span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;网络构建完成&quot;</span><span class="p">)</span>
+<span class="linenos">51</span>    <span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="linenos">52</span>    <span class="n">cnn_model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">num_epoch</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span><span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">LossMonitor</span><span class="p">()],</span><span class="n">dataset_sink_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="linenos">53</span>    <span class="n">inputs</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="linenos">54</span>    <span class="n">ms</span><span class="o">.</span><span class="n">save_checkpoint</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="s1">&#39;cnn.ckpt&#39;</span><span class="p">)</span>
+<span class="linenos">55</span>    <span class="n">export</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;cnn&#39;</span><span class="p">,</span> <span class="n">file_format</span><span class="o">=</span><span class="s2">&quot;MINDIR&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+<section id="id5">
+<h3>3. 重新组网<a class="headerlink" href="#id5" title="此标题的永久链接"></a></h3>
+<blockquote>
+<div><p>在前面章节中，我们已经完成了AI的模型的训练和导出。
+需要重新构建成一个新的网络结构，可以使用MindSpore框架的 <code class="docutils literal notranslate"><span class="pre">nn.GraphCell()</span></code> 接口来实现。
+该部分内容包括加载训练好的cnn模型，灰度化处理，重新构建网络结构，并导出新的模型。
+重新组网、导出模型以及测试的代码如下：</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span><span class="w"> </span><span class="nn">cv2</span>
+<span class="linenos"> 2</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 3</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.serialization</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>
+<span class="linenos"> 4</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="linenos"> 5</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">ops</span><span class="p">,</span> <span class="n">context</span>
+<span class="linenos"> 6</span><span class="kn">from</span><span class="w"> </span><span class="nn">CNN</span><span class="w"> </span><span class="kn">import</span> <span class="n">export_cnn</span>
+<span class="linenos"> 7</span><span class="kn">from</span><span class="w"> </span><span class="nn">GRAY</span><span class="w"> </span><span class="kn">import</span> <span class="n">export_gray</span>
+<span class="linenos"> 8</span><span class="kn">from</span><span class="w"> </span><span class="nn">Connection</span><span class="w"> </span><span class="kn">import</span> <span class="n">export_connection</span>
+<span class="linenos"> 9</span>
+<span class="linenos">10</span><span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;BRDM_2&#39;</span><span class="p">,</span> <span class="s1">&#39;BTR_60&#39;</span><span class="p">,</span> <span class="s1">&#39;SLICY&#39;</span><span class="p">,</span> <span class="s1">&#39;T62&#39;</span><span class="p">,</span> <span class="s1">&#39;ZSU_23_4&#39;</span><span class="p">]</span>
+<span class="linenos">11</span><span class="n">class_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;装甲侦察车&quot;</span><span class="p">,</span> <span class="s2">&quot;装甲运输车&quot;</span><span class="p">,</span> <span class="s2">&quot;不明&quot;</span><span class="p">,</span> <span class="s2">&quot;坦克&quot;</span><span class="p">,</span> <span class="s2">&quot;自行高炮&quot;</span><span class="p">]</span>
+<span class="linenos">12</span><span class="k">class</span><span class="w"> </span><span class="nc">GrayCNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
+<span class="linenos">13</span>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">14</span>        <span class="nb">super</span><span class="p">(</span><span class="n">GrayCNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">15</span>        <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">Split</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">output_num</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="linenos">16</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">17</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">18</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">19</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">20</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">21</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">22</span>        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span>
+<span class="linenos">23</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32768</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
+<span class="linenos">24</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="linenos">25</span>        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+<span class="linenos">26</span>        <span class="bp">self</span><span class="o">.</span><span class="n">cnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GraphCell</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="s2">&quot;cnn.mindir&quot;</span><span class="p">))</span>
+<span class="linenos">27</span>    <span class="k">def</span><span class="w"> </span><span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">28</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">29</span>        <span class="n">b</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">r</span> <span class="o">=</span> <span class="n">x</span>
+<span class="linenos">30</span>        <span class="n">x</span> <span class="o">=</span> <span class="p">(</span>
+<span class="linenos">31</span>            <span class="n">b</span> <span class="o">*</span> <span class="mf">0.114</span> <span class="o">+</span>
+<span class="linenos">32</span>            <span class="n">g</span> <span class="o">*</span> <span class="mf">0.587</span> <span class="o">+</span>
+<span class="linenos">33</span>            <span class="n">r</span> <span class="o">*</span> <span class="mf">0.299</span>
+<span class="linenos">34</span>        <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="linenos">35</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">clip_by_value</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span>
+<span class="linenos">36</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">255.0</span>   <span class="c1"># 数据归一化</span>
+<span class="linenos">37</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>   <span class="c1"># 灰度化处理后结果需要重塑成cnn输入形状</span>
+<span class="linenos">38</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cnn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">39</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">40</span>
+<span class="linenos">41</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class="linenos">42</span>    <span class="n">export_cnn</span><span class="p">()</span>
+<span class="linenos">43</span>    <span class="n">context</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">GRAPH_MODE</span><span class="p">)</span>
+<span class="linenos">44</span>    <span class="c1"># 1. 读取图片（保持原始uint8类型）</span>
+<span class="linenos">45</span>    <span class="n">color_image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">&quot;image_origin.jpg&quot;</span><span class="p">)</span>  <span class="c1"># 默认uint8</span>
+<span class="linenos">46</span>    <span class="n">resized_image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span>
+<span class="linenos">47</span>        <span class="n">color_image</span><span class="p">,</span>
+<span class="linenos">48</span>        <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>  <span class="c1"># 目标尺寸（width, height）</span>
+<span class="linenos">49</span>        <span class="n">interpolation</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">INTER_LINEAR</span>
+<span class="linenos">50</span>    <span class="p">)</span>
+<span class="linenos">51</span>    <span class="n">resized_image</span> <span class="o">=</span> <span class="n">resized_image</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="linenos">52</span>    <span class="n">resized_image_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">resized_image</span><span class="p">)</span>
+<span class="linenos">53</span>
+<span class="linenos">54</span>    <span class="n">my_model</span> <span class="o">=</span> <span class="n">GrayCNN</span><span class="p">()</span>
+<span class="linenos">55</span>    <span class="n">param_dict</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s2">&quot;cnn.ckpt&quot;</span><span class="p">)</span>
+<span class="linenos">56</span>    <span class="n">param_not_load</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">load_param_into_net</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">,</span> <span class="n">strict_load</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">57</span>
+<span class="linenos">58</span>    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="linenos">59</span>    <span class="n">export</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">input_tensor</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;gray_cnn&#39;</span><span class="p">,</span> <span class="n">file_format</span><span class="o">=</span><span class="s1">&#39;MINDIR&#39;</span><span class="p">)</span>
+<span class="linenos">60</span>
+<span class="linenos">61</span>    <span class="n">reload_cnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GraphCell</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;gray_cnn.mindir&#39;</span><span class="p">))</span>
+<span class="linenos">62</span>    <span class="n">reload_cnn</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">reload_cnn</span><span class="p">)</span>
+<span class="linenos">63</span>    <span class="n">m</span> <span class="o">=</span> <span class="n">reload_cnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">resized_image_tensor</span><span class="p">)</span>
+<span class="linenos">64</span>    <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+<span class="linenos">65</span>    <span class="n">output_np</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="linenos">66</span>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_np</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># 对于分类任务，通常输出是[batch_size, num_classes]</span>
+<span class="linenos">67</span>        <span class="n">output_prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output_np</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output_np</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">68</span>        <span class="n">predicted_class</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output_prob</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="linenos">69</span>        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;预测结果: </span><span class="si">{</span><span class="n">class_labels</span><span class="p">[</span><span class="n">predicted_class</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>该代码首先定义了一个新的网络结构GrayCNN，
+在 <code class="docutils literal notranslate"><span class="pre">construct</span></code> 方法中，首先通过gray模型处理输入图像，然后通过归一化和重塑张量，最后cnn模型进行识别。
+在主函数中，首先导出cnn模型。
+然后加载cnn模型参数，并使用MindSpore的 <code class="docutils literal notranslate"><span class="pre">export</span></code> 方法导出新的网络结构。
+最后，使用重新组网后的模型进行预测，并输出结果。</p>
+</div></blockquote>
+</section>
+<section id="id6">
+<h3>4. 转换模型<a class="headerlink" href="#id6" title="此标题的永久链接"></a></h3>
+<blockquote>
+<div><p>在前面的代码中，我们已经使用了 <code class="docutils literal notranslate"><span class="pre">export</span></code> 方法导出了gray_cnn.mindir模型。
+要将该模型部署到FT78NE平台，需要将MINDIR格式转换成mindspore lite的ms格式模型。
+要将该模型转换为ms格式，可以使用MindSpore的转换工具(converter_lite)。
+该工具已经集成在我们的 <code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code> 中，可以直接在IDE中使用。或者也可以使用命令行工具进行转换。</p>
+<p>转换命令如下：</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./converter_lite<span class="w"> </span>--fmk<span class="o">=</span>MINDIR<span class="w"> </span>--modelFile<span class="o">=</span>gray_cnn.mindir<span class="w"> </span>--outputFile<span class="o">=</span>gray_cnn
+</pre></div>
+</div>
+<p>转换完成后，会在当前目录下生成gray_cnn.ms模型文件。该模型可以使用可视化工具(netron)可以打开该文件，查看模型结构。
+该工具可以直接在 <code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code> 中使用，可以从官网下载使用，也可以在线使用。在线地址为：<a class="reference external" href="https://netron.app/">https://netron.app/</a>。</p>
+<p>该模型文件可视化如图所示：</p>
+<a class="reference internal image-reference" href="../../_images/gray_cnn.png"><img alt="gray_cnn模型结构图" class="align-center" src="../../_images/gray_cnn.png" style="width: 40.0%; height: 2025.0px;" /></a>
+</div></blockquote>
+</section>
+<section id="id7">
+<h3>5. 部署和运行程序<a class="headerlink" href="#id7" title="此标题的永久链接"></a></h3>
+<blockquote>
+<div><ul>
+<li><p>在IDE中新建一个Python项目，将下面的 <a class="reference internal" href="#python-code"><span class="std std-ref">python完整代码示例</span></a> 代码拷贝到项目中，并运行。
+运行成功后，会在当前目录下生成一个名为 <code class="docutils literal notranslate"><span class="pre">gray_cnn.midir</span></code> 的模型文件，以及输出图片的预测结果。
+结果如图所示：</p>
+<a class="reference internal image-reference" href="../../_images/python_gray_cnn_res.png"><img alt="python运行结果" class="align-center" src="../../_images/python_gray_cnn_res.png" style="width: 40.0%; height: 67.5px;" /></a>
+<p>将该文件转换为ms格式，并将ms格式模型和测试图片拷贝到FT78NE平台中。</p>
+</li>
+<li><p>打开 <code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code> ，新建工程。输入工程名、路径，工程类型选择 <code class="docutils literal notranslate"><span class="pre">Heterogeneous</span></code> ，
+输入交叉编译工具路径，然后点确定。会生成一个异构模板工程。通过修改 <code class="docutils literal notranslate"><span class="pre">data_handler.cc</span></code> 文件中的函数来调整输入输出数据，
+输入改成读取的图片路径；输出改成相应的后处理。在 <code class="docutils literal notranslate"><span class="pre">main</span></code> 函数设置运行后端；修改 <code class="docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> 文件，
+添加openCV库的lib和include路径， 最后编译该工程，编译成功后将build文件夹下的  <code class="docutils literal notranslate"><span class="pre">main</span></code> 拷贝到FT78NE平台中，
+要和gray_cnn.ms模型同一个文件夹下。</p>
+<p>输入的c++代码示例如下：</p>
+</li>
+</ul>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;opencv2/opencv.hpp&gt;</span>
+<span class="linenos"> 2</span><span class="kt">int</span><span class="w"> </span><span class="nf">readImage</span><span class="p">(</span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">reslut</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">imagePath</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos"> 3</span><span class="w">    </span><span class="n">cv</span><span class="o">::</span><span class="n">Mat</span><span class="w"> </span><span class="n">color_image</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cv</span><span class="o">::</span><span class="n">imread</span><span class="p">(</span><span class="n">imagePath</span><span class="p">.</span><span class="n">c_str</span><span class="p">(),</span><span class="w"> </span><span class="n">cv</span><span class="o">::</span><span class="n">IMREAD_COLOR</span><span class="p">);</span>
+<span class="linenos"> 4</span><span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">color_image</span><span class="p">.</span><span class="n">empty</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos"> 5</span><span class="w">        </span><span class="n">fprintf</span><span class="p">(</span><span class="n">stderr</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;read image failed</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">);</span>
+<span class="linenos"> 6</span><span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span>
+<span class="linenos"> 7</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">width</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">128</span><span class="p">;</span>
+<span class="linenos">10</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">height</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">128</span><span class="p">;</span>
+<span class="linenos">11</span><span class="w">    </span><span class="n">cv</span><span class="o">::</span><span class="n">Mat</span><span class="w"> </span><span class="n">resized_image</span><span class="p">;</span>
+<span class="linenos">12</span><span class="w">    </span><span class="n">cv</span><span class="o">::</span><span class="n">resize</span><span class="p">(</span><span class="n">color_image</span><span class="p">,</span><span class="w"> </span><span class="n">resized_image</span><span class="p">,</span><span class="w"> </span><span class="n">cv</span><span class="o">::</span><span class="n">Size</span><span class="p">(</span><span class="n">width</span><span class="p">,</span><span class="w"> </span><span class="n">height</span><span class="p">),</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">cv</span><span class="o">::</span><span class="n">INTER_LINEAR</span><span class="p">);</span>
+<span class="linenos">13</span><span class="w">    </span><span class="n">resized_image</span><span class="p">.</span><span class="n">convertTo</span><span class="p">(</span><span class="n">resized_image</span><span class="p">,</span><span class="w"> </span><span class="n">CV_32F</span><span class="p">);</span>
+<span class="linenos">14</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">channels</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">resized_image</span><span class="p">.</span><span class="n">channels</span><span class="p">();</span>
+<span class="linenos">15</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">element_count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">width</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">height</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">channels</span><span class="p">;</span>
+<span class="linenos">16</span>
+<span class="linenos">17</span><span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">height</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">18</span><span class="w">        </span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">src_ptr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">resized_image</span><span class="p">.</span><span class="n">ptr</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
+<span class="linenos">19</span><span class="w">        </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">dst_ptr</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">reslut</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">width</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">channels</span><span class="p">;</span>
+<span class="linenos">20</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">memcpy</span><span class="p">(</span><span class="n">dst_ptr</span><span class="p">,</span><span class="w"> </span><span class="n">src_ptr</span><span class="p">,</span><span class="w"> </span><span class="n">width</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">channels</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="kt">float</span><span class="p">));</span>
+<span class="linenos">21</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos">22</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+<span class="linenos">23</span><span class="p">}</span>
+</pre></div>
+</div>
+<p>设置后端代码如下：</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span><span class="k">auto</span><span class="w"> </span><span class="n">context</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_shared</span><span class="o">&lt;</span><span class="n">mindspore</span><span class="o">::</span><span class="n">Context</span><span class="o">&gt;</span><span class="p">();</span>
+<span class="linenos">2</span><span class="k">auto</span><span class="w"> </span><span class="o">&amp;</span><span class="n">device_list</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">context</span><span class="o">-&gt;</span><span class="n">MutableDeviceInfo</span><span class="p">();</span>
+<span class="linenos">3</span><span class="n">context</span><span class="o">-&gt;</span><span class="n">SetBuiltInDelegate</span><span class="p">(</span><span class="n">mindspore</span><span class="o">::</span><span class="n">DelegateMode</span><span class="o">::</span><span class="n">kPNNA</span><span class="p">);</span>
+<span class="linenos">4</span><span class="k">auto</span><span class="w"> </span><span class="n">cpu_info</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_shared</span><span class="o">&lt;</span><span class="n">mindspore</span><span class="o">::</span><span class="n">CPUDeviceInfo</span><span class="o">&gt;</span><span class="p">();</span>
+<span class="linenos">5</span><span class="k">auto</span><span class="w"> </span><span class="n">dsp_info</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">make_shared</span><span class="o">&lt;</span><span class="n">mindspore</span><span class="o">::</span><span class="n">FT78NEDeviceInfo</span><span class="o">&gt;</span><span class="p">();</span>
+<span class="linenos">6</span><span class="n">device_list</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">dsp_info</span><span class="p">);</span>
+<span class="linenos">7</span><span class="n">device_list</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">cpu_info</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>输出结果后处理代码如下：</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">floatArrayToVector</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">array</span><span class="p">,</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">size</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos"> 2</span><span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">vec</span><span class="p">(</span><span class="n">array</span><span class="p">,</span><span class="w"> </span><span class="n">array</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">size</span><span class="p">);</span>
+<span class="linenos"> 3</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">vec</span><span class="p">;</span>
+<span class="linenos"> 4</span><span class="p">}</span>
+<span class="linenos"> 5</span>
+<span class="linenos"> 6</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">numpy_exp</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos"> 7</span><span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">result</span><span class="p">;</span>
+<span class="linenos"> 8</span><span class="w">    </span><span class="n">result</span><span class="p">.</span><span class="n">reserve</span><span class="p">(</span><span class="n">x</span><span class="p">.</span><span class="n">size</span><span class="p">());</span>
+<span class="linenos"> 9</span><span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="w"> </span><span class="n">num</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">10</span><span class="w">        </span><span class="n">result</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">exp</span><span class="p">(</span><span class="n">num</span><span class="p">));</span>
+<span class="linenos">11</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos">12</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">result</span><span class="p">;</span>
+<span class="linenos">13</span><span class="p">}</span>
+<span class="linenos">14</span>
+<span class="linenos">15</span><span class="kt">float</span><span class="w"> </span><span class="n">sum_rows</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">vec</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">axis</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">16</span><span class="w">    </span><span class="kt">float</span><span class="w"> </span><span class="n">sum</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">accumulate</span><span class="p">(</span><span class="n">vec</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">vec</span><span class="p">.</span><span class="n">end</span><span class="p">(),</span><span class="w"> </span><span class="mf">0.0</span><span class="p">);</span>
+<span class="linenos">17</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">sum</span><span class="p">;</span>
+<span class="linenos">18</span><span class="p">}</span>
+<span class="linenos">19</span>
+<span class="linenos">20</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">convertTo2D</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">vec</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">rows</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">21</span><span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">result</span><span class="p">;</span>
+<span class="linenos">22</span><span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">vec</span><span class="p">.</span><span class="n">empty</span><span class="p">()</span><span class="w"> </span><span class="o">||</span><span class="w"> </span><span class="n">rows</span><span class="w"> </span><span class="o">&lt;=</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">23</span><span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="n">result</span><span class="p">;</span>
+<span class="linenos">24</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos">25</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">cols</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">vec</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">rows</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">rows</span><span class="p">;</span><span class="w">  </span><span class="c1">// 计算列数</span>
+<span class="linenos">26</span><span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">rows</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">i</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">27</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">row</span><span class="p">;</span>
+<span class="linenos">28</span><span class="w">        </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">j</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">cols</span><span class="p">;</span><span class="w"> </span><span class="o">++</span><span class="n">j</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">29</span><span class="w">        </span><span class="kt">size_t</span><span class="w"> </span><span class="n">index</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">cols</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">j</span><span class="p">;</span><span class="w">  </span><span class="c1">// 计算索引</span>
+<span class="linenos">30</span><span class="w">        </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">index</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">vec</span><span class="p">.</span><span class="n">size</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">31</span><span class="w">            </span><span class="n">row</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">vec</span><span class="p">[</span><span class="n">index</span><span class="p">]);</span>
+<span class="linenos">32</span><span class="w">        </span><span class="p">}</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">33</span><span class="w">            </span><span class="n">row</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span><span class="w">  </span><span class="c1">// 填充剩余空间，或者你可以选择不填充</span>
+<span class="linenos">34</span><span class="w">        </span><span class="p">}</span>
+<span class="linenos">35</span><span class="w">        </span><span class="p">}</span>
+<span class="linenos">36</span><span class="w">        </span><span class="n">result</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">row</span><span class="p">);</span>
+<span class="linenos">37</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos">38</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">result</span><span class="p">;</span>
+<span class="linenos">39</span><span class="p">}</span>
+<span class="linenos">40</span>
+<span class="linenos">41</span><span class="kt">void</span><span class="w"> </span><span class="n">GetOutputData</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">MSTensor</span><span class="o">&gt;</span><span class="w"> </span><span class="o">&amp;</span><span class="n">outputs</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">42</span><span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">tensor</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">outputs</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">43</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;tensor name is:&quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">tensor</span><span class="p">.</span><span class="n">Name</span><span class="p">()</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot; tensor size is:&quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">tensor</span><span class="p">.</span><span class="n">DataSize</span><span class="p">()</span>
+<span class="linenos">44</span><span class="w">                </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot; tensor elements num is:&quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">tensor</span><span class="p">.</span><span class="n">ElementNum</span><span class="p">()</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
+<span class="linenos">45</span><span class="w">        </span><span class="k">auto</span><span class="w"> </span><span class="n">out_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*&gt;</span><span class="p">(</span><span class="n">tensor</span><span class="p">.</span><span class="n">Data</span><span class="p">().</span><span class="n">get</span><span class="p">());</span>
+<span class="linenos">46</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;output data is:&quot;</span><span class="p">;</span>
+<span class="linenos">47</span><span class="w">        </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">tensor</span><span class="p">.</span><span class="n">ElementNum</span><span class="p">()</span><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">&lt;=</span><span class="w"> </span><span class="mi">50</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">48</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">out_data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot; &quot;</span><span class="p">;</span>
+<span class="linenos">49</span><span class="w">        </span><span class="p">}</span>
+<span class="linenos">50</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
+<span class="linenos">51</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">floatArrayToVector</span><span class="p">(</span><span class="n">out_data</span><span class="p">,</span><span class="w"> </span><span class="n">tensor</span><span class="p">.</span><span class="n">ElementNum</span><span class="p">());</span>
+<span class="linenos">52</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">exp_x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">numpy_exp</span><span class="p">(</span><span class="n">out</span><span class="p">);</span>
+<span class="linenos">53</span><span class="w">        </span><span class="kt">float</span><span class="w"> </span><span class="n">sums</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sum_rows</span><span class="p">(</span><span class="n">exp_x</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
+<span class="linenos">54</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;</span><span class="w"> </span><span class="n">x1</span><span class="p">;</span>
+<span class="linenos">55</span><span class="w">        </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="w"> </span><span class="n">num</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">exp_x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">56</span><span class="w">            </span><span class="n">x1</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">num</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">sums</span><span class="p">);</span>
+<span class="linenos">57</span><span class="w">        </span><span class="p">}</span>
+<span class="linenos">58</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">float</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">twoD</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">convertTo2D</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">);</span>
+<span class="linenos">59</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="w"> </span><span class="n">argmax_indices</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">argmax</span><span class="p">(</span><span class="n">twoD</span><span class="p">);</span>
+<span class="linenos">60</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">Predicted_class</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="s">&quot;装甲侦察车&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;装甲运输车&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;不明&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;坦克&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;自行高炮&quot;</span><span class="p">};</span>
+<span class="linenos">61</span><span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;预测结果: &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">Predicted_class</span><span class="p">[</span><span class="n">argmax_indices</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
+<span class="linenos">62</span><span class="w">    </span><span class="p">}</span>
+<span class="linenos">63</span><span class="p">}</span>
+</pre></div>
+</div>
+<ul>
+<li><p>在FT78NE上运行可执行文件 <code class="docutils literal notranslate"><span class="pre">main</span></code> ，观察输出结果。与预期结果对比，验证模型推理的正确性。
+执行命令如下：</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span>./main<span class="w"> </span>gray_cnn.ms<span class="w"> </span>image_origin.jpg
+</pre></div>
+</div>
+</li>
+</ul>
+<p>执行结果如下图：</p>
+<a class="reference internal image-reference" href="../../_images/ft78ne_gray_cnn_output.png"><img alt="FT78NE运行结果" class="align-center" src="../../_images/ft78ne_gray_cnn_output.png" style="width: 40.0%; height: 49.0px;" /></a>
+</div></blockquote>
+</section>
+</section>
+<section id="python">
+<span id="python-code"></span><h2>python完整代码示例<a class="headerlink" href="#python" title="此标题的永久链接"></a></h2>
+<blockquote>
+<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># CNN.py</span>
+<span class="linenos"> 2</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 3</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.serialization</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>
+<span class="linenos"> 4</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">context</span>
+<span class="linenos"> 5</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="linenos"> 6</span><span class="kn">from</span><span class="w"> </span><span class="nn">PIL</span><span class="w"> </span><span class="kn">import</span> <span class="n">Image</span>
+<span class="linenos"> 7</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore.dataset</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ds</span>
+<span class="linenos"> 8</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.dataset</span><span class="w"> </span><span class="kn">import</span> <span class="n">py_transforms</span>
+<span class="linenos"> 9</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore.dataset.vision</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">CV</span>
+<span class="linenos">10</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.callback</span><span class="w"> </span><span class="kn">import</span>  <span class="n">LossMonitor</span>
+<span class="linenos">11</span>
+<span class="linenos">12</span><span class="c1"># # Define directories and parameters</span>
+<span class="linenos">13</span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
+<span class="linenos">14</span><span class="n">img_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
+<span class="linenos">15</span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;../cnn-sar/Target&#39;</span>
+<span class="linenos">16</span>
+<span class="linenos">17</span><span class="c1"># 数据预处理</span>
+<span class="linenos">18</span><span class="k">def</span><span class="w"> </span><span class="nf">rescale_to_0_1</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
+<span class="linenos">19</span>    <span class="k">return</span> <span class="n">image</span> <span class="o">/</span> <span class="mf">255.0</span>
+<span class="linenos">20</span>
+<span class="linenos">21</span><span class="c1"># 自定义函数，添加 color 通道维度</span>
+<span class="linenos">22</span><span class="k">def</span><span class="w"> </span><span class="nf">add_channels</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
+<span class="linenos">23</span>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># 单个图像，没有 cin_channel 维度</span>
+<span class="linenos">24</span>        <span class="c1"># print(&quot;before ===&gt; &quot;, image.shape)</span>
+<span class="linenos">25</span>        <span class="c1"># 添加 color 通道维度，(128x128) =&gt; (1, 128, 128)</span>
+<span class="linenos">26</span>        <span class="n">image_four_channels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="linenos">27</span>        <span class="c1"># print(&quot;after  ===&gt; &quot;, image_four_channels.shape)</span>
+<span class="linenos">28</span>    <span class="k">else</span><span class="p">:</span>
+<span class="linenos">29</span>        <span class="k">pass</span>
+<span class="linenos">30</span>        <span class="k">return</span> <span class="n">image</span>
+<span class="linenos">31</span>    <span class="k">return</span> <span class="n">image_four_channels</span>
+<span class="linenos">32</span>
+<span class="linenos">33</span><span class="k">class</span><span class="w"> </span><span class="nc">CNN</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
+<span class="linenos">34</span>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">35</span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">36</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">37</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">38</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">39</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">40</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">41</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">42</span>        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span>
+<span class="linenos">43</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32768</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
+<span class="linenos">44</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="linenos">45</span>        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+<span class="linenos">46</span>
+<span class="linenos">47</span>    <span class="k">def</span><span class="w"> </span><span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">48</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">49</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">50</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">51</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">52</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">53</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">54</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">55</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">56</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">57</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">58</span>
+<span class="linenos">59</span><span class="k">def</span><span class="w"> </span><span class="nf">export_cnn</span><span class="p">():</span>
+<span class="linenos">60</span>    <span class="n">context</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">PYNATIVE_MODE</span><span class="p">,</span> <span class="n">device_target</span><span class="o">=</span><span class="s2">&quot;CPU&quot;</span><span class="p">)</span>
+<span class="linenos">61</span>    <span class="n">resize_op</span> <span class="o">=</span> <span class="n">CV</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">img_size</span><span class="p">)</span>
+<span class="linenos">62</span>    <span class="n">rescale_transform</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">rescale_to_0_1</span><span class="p">])</span>
+<span class="linenos">63</span>    <span class="n">f32_typecast</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">TypeCast</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="linenos">64</span>    <span class="c1"># 将读取的 RGB 转为 GRAY 模式</span>
+<span class="linenos">65</span>    <span class="n">convert_gray</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ConvertColor</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">ConvertMode</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
+<span class="linenos">66</span>
+<span class="linenos">67</span>    <span class="n">transform</span> <span class="o">=</span> <span class="p">[</span><span class="n">convert_gray</span><span class="p">,</span> <span class="n">resize_op</span><span class="p">,</span> <span class="n">f32_typecast</span><span class="p">,</span> <span class="n">rescale_transform</span><span class="p">,</span> <span class="n">CV</span><span class="o">.</span><span class="n">HWC2CHW</span><span class="p">()]</span>
+<span class="linenos">68</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">dataset_dir</span><span class="o">=</span><span class="n">data_path</span><span class="p">,</span> <span class="n">decode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;.JPEG&quot;</span><span class="p">,</span> <span class="s2">&quot;.PNG&quot;</span><span class="p">,</span> <span class="s2">&quot;.JPG&quot;</span><span class="p">])</span>
+<span class="linenos">69</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">input_columns</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">operations</span><span class="o">=</span> <span class="n">transform</span><span class="p">)</span>
+<span class="linenos">70</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">operations</span><span class="o">=</span><span class="k">lambda</span> <span class="n">image</span><span class="p">:</span> <span class="n">add_channels</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> \
+<span class="linenos">71</span>                                    <span class="n">input_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">],</span> \
+<span class="linenos">72</span>                                    <span class="n">output_columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">])</span>
+<span class="linenos">73</span>    <span class="n">train_data</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
+<span class="linenos">74</span>    <span class="n">data_iter</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">create_dict_iterator</span><span class="p">()</span>
+<span class="linenos">75</span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;数据集加载完成&quot;</span><span class="p">)</span>
+<span class="linenos">76</span>    <span class="n">my_model</span> <span class="o">=</span> <span class="n">CNN</span><span class="p">()</span>
+<span class="linenos">77</span>    <span class="n">loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">(</span><span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+<span class="linenos">78</span>    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">trainable_params</span><span class="p">(),</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="linenos">79</span>    <span class="n">cnn_model</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()})</span>
+<span class="linenos">80</span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;网络构建完成&quot;</span><span class="p">)</span>
+<span class="linenos">81</span>    <span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="linenos">82</span>    <span class="n">cnn_model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">num_epoch</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span><span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">LossMonitor</span><span class="p">()],</span><span class="n">dataset_sink_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="linenos">83</span>    <span class="n">inputs</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="linenos">84</span>    <span class="n">ms</span><span class="o">.</span><span class="n">save_checkpoint</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="s1">&#39;cnn.ckpt&#39;</span><span class="p">)</span>
+<span class="linenos">85</span>    <span class="n">export</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;cnn&#39;</span><span class="p">,</span> <span class="n">file_format</span><span class="o">=</span><span class="s2">&quot;MINDIR&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># GRAY_CNN.py</span>
+<span class="linenos"> 2</span><span class="kn">import</span><span class="w"> </span><span class="nn">cv2</span>
+<span class="linenos"> 3</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
+<span class="linenos"> 4</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore.train.serialization</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>
+<span class="linenos"> 5</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="linenos"> 6</span><span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">ops</span><span class="p">,</span> <span class="n">context</span>
+<span class="linenos"> 7</span><span class="kn">from</span><span class="w"> </span><span class="nn">CNN</span><span class="w"> </span><span class="kn">import</span> <span class="n">export_cnn</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;BRDM_2&#39;</span><span class="p">,</span> <span class="s1">&#39;BTR_60&#39;</span><span class="p">,</span> <span class="s1">&#39;SLICY&#39;</span><span class="p">,</span> <span class="s1">&#39;T62&#39;</span><span class="p">,</span> <span class="s1">&#39;ZSU_23_4&#39;</span><span class="p">]</span>
+<span class="linenos">10</span><span class="n">class_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;装甲侦察车&quot;</span><span class="p">,</span> <span class="s2">&quot;装甲运输车&quot;</span><span class="p">,</span> <span class="s2">&quot;不明&quot;</span><span class="p">,</span> <span class="s2">&quot;坦克&quot;</span><span class="p">,</span> <span class="s2">&quot;自行高炮&quot;</span><span class="p">]</span>
+<span class="linenos">11</span>
+<span class="linenos">12</span><span class="k">class</span><span class="w"> </span><span class="nc">GrayCNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
+<span class="linenos">13</span>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">14</span>        <span class="nb">super</span><span class="p">(</span><span class="n">GrayCNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">15</span>        <span class="bp">self</span><span class="o">.</span><span class="n">split</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">Split</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">output_num</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="linenos">16</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">17</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">18</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">19</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">20</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">has_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">21</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="linenos">22</span>        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span>
+<span class="linenos">23</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32768</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
+<span class="linenos">24</span>        <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="linenos">25</span>        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+<span class="linenos">26</span>        <span class="bp">self</span><span class="o">.</span><span class="n">cnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GraphCell</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="s2">&quot;cnn.mindir&quot;</span><span class="p">))</span>
+<span class="linenos">27</span>
+<span class="linenos">28</span>    <span class="k">def</span><span class="w"> </span><span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">29</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">30</span>        <span class="n">b</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">r</span> <span class="o">=</span> <span class="n">x</span>
+<span class="linenos">31</span>        <span class="n">x</span> <span class="o">=</span> <span class="p">(</span>
+<span class="linenos">32</span>            <span class="n">b</span> <span class="o">*</span> <span class="mf">0.114</span> <span class="o">+</span>
+<span class="linenos">33</span>            <span class="n">g</span> <span class="o">*</span> <span class="mf">0.587</span> <span class="o">+</span>
+<span class="linenos">34</span>            <span class="n">r</span> <span class="o">*</span> <span class="mf">0.299</span>
+<span class="linenos">35</span>        <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="linenos">36</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">clip_by_value</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span>
+<span class="linenos">37</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">255.0</span>   <span class="c1"># 数据归一化</span>
+<span class="linenos">38</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>   <span class="c1"># 灰度化处理后结果需要重塑成cnn输入形状</span>
+<span class="linenos">39</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cnn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">40</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">41</span>
+<span class="linenos">42</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class="linenos">43</span>    <span class="n">export_gray</span><span class="p">()</span>
+<span class="linenos">44</span>    <span class="n">export_connection</span><span class="p">()</span>
+<span class="linenos">45</span>    <span class="n">export_cnn</span><span class="p">()</span>
+<span class="linenos">46</span>    <span class="n">context</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">GRAPH_MODE</span><span class="p">)</span>
+<span class="linenos">47</span>    <span class="c1"># 1. 读取图片（保持原始uint8类型）</span>
+<span class="linenos">48</span>    <span class="n">color_image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">&quot;image_origin.jpg&quot;</span><span class="p">)</span>  <span class="c1"># 默认uint8</span>
+<span class="linenos">49</span>    <span class="n">resized_image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span>
+<span class="linenos">50</span>        <span class="n">color_image</span><span class="p">,</span>
+<span class="linenos">51</span>        <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>  <span class="c1"># 目标尺寸（width, height）</span>
+<span class="linenos">52</span>        <span class="n">interpolation</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">INTER_LINEAR</span>
+<span class="linenos">53</span>    <span class="p">)</span>
+<span class="linenos">54</span>    <span class="n">resized_image</span> <span class="o">=</span> <span class="n">resized_image</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="linenos">55</span>    <span class="n">resized_image_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">resized_image</span><span class="p">)</span>
+<span class="linenos">56</span>    <span class="n">my_model</span> <span class="o">=</span> <span class="n">GrayCNN</span><span class="p">()</span>
+<span class="linenos">57</span>    <span class="n">param_dict</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s2">&quot;cnn.ckpt&quot;</span><span class="p">)</span>
+<span class="linenos">58</span>    <span class="n">param_not_load</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">load_param_into_net</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">,</span> <span class="n">strict_load</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">59</span>    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="linenos">60</span>    <span class="n">export</span><span class="p">(</span><span class="n">my_model</span><span class="p">,</span> <span class="n">input_tensor</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;gray_cnn&#39;</span><span class="p">,</span> <span class="n">file_format</span><span class="o">=</span><span class="s1">&#39;MINDIR&#39;</span><span class="p">)</span>
+<span class="linenos">61</span>    <span class="n">reload_cnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GraphCell</span><span class="p">(</span><span class="n">ms</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_name</span><span class="o">=</span><span class="s1">&#39;gray_cnn.mindir&#39;</span><span class="p">))</span>
+<span class="linenos">62</span>    <span class="n">reload_cnn</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Model</span><span class="p">(</span><span class="n">reload_cnn</span><span class="p">)</span>
+<span class="linenos">63</span>    <span class="n">m</span> <span class="o">=</span> <span class="n">reload_cnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">resized_image_tensor</span><span class="p">)</span>
+<span class="linenos">64</span>    <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+<span class="linenos">65</span>    <span class="n">output_np</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
+<span class="linenos">66</span>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_np</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># 对于分类任务，通常输出是[batch_size, num_classes]</span>
+<span class="linenos">67</span>        <span class="n">output_prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output_np</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output_np</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="linenos">68</span>        <span class="n">predicted_class</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output_prob</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="linenos">69</span>        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;预测结果: </span><span class="si">{</span><span class="n">class_labels</span><span class="p">[</span><span class="n">predicted_class</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
+        <a href="index.html" class="btn btn-neutral float-left" title="AI+DSP应用示例" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
+        <a href="../dsp/index.html" class="btn btn-neutral float-right" title="DSP应用示例" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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master/html/appdevelop/ai_dsp/index.html

@@ -1,7 +1,7 @@
 
 
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-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
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@@ -14,15 +14,15 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
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-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
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     <link rel="search" title="搜索" href="../../search.html" />
-    <link rel="next" title="DSP应用示例" href="../dsp/index.html" />
+    <link rel="next" title="GRAY_CNN(图片灰度化处理+图片识别)" href="gray_cnn.html" />
     <link rel="prev" title="应用开发示例" href="../index.html" /> 
 </head>
 
@@ -48,10 +48,12 @@
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">应用开发示例</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">AI+DSP应用示例</a><ul class="simple">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">AI+DSP应用示例</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="gray_cnn.html">GRAY_CNN(图片灰度化处理+图片识别)</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../dsp/index.html">DSP应用示例</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../autocodegen/index.html">AI辅助开发示例</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../functionlib/index.html">算子库支持</a></li>
@@ -84,8 +86,11 @@
            <div itemprop="articleBody">
              
   <section id="ai-dsp">
-<h1>AI+DSP应用示例<a class="headerlink" href="#ai-dsp" title="Link to this heading"></a></h1>
+<h1>AI+DSP应用示例<a class="headerlink" href="#ai-dsp" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="gray_cnn.html">GRAY_CNN(图片灰度化处理+图片识别)</a></li>
+</ul>
 </div>
 </section>
 
@@ -94,7 +99,7 @@
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
         <a href="../index.html" class="btn btn-neutral float-left" title="应用开发示例" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
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+        <a href="gray_cnn.html" class="btn btn-neutral float-right" title="GRAY_CNN(图片灰度化处理+图片识别)" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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master/html/appdevelop/autocodegen/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
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       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
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     <link rel="next" title="算子库支持" href="../../functionlib/index.html" />
-    <link rel="prev" title="DSP应用示例" href="../dsp/index.html" /> 
+    <link rel="prev" title="RDSAR（距离-多普勒SAR成像算法）" href="../dsp/rdsar.html" /> 
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@@ -85,7 +85,7 @@
            <div itemprop="articleBody">
              
   <section id="ai">
-<h1>AI辅助开发示例<a class="headerlink" href="#ai" title="Link to this heading"></a></h1>
+<h1>AI辅助开发示例<a class="headerlink" href="#ai" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
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@@ -94,7 +94,7 @@
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
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+        <a href="../dsp/rdsar.html" class="btn btn-neutral float-left" title="RDSAR（距离-多普勒SAR成像算法）" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
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master/html/appdevelop/dsp/index.html

@@ -1,7 +1,7 @@
 
 
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-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
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@@ -14,16 +14,16 @@
   
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-    <link rel="prev" title="AI+DSP应用示例" href="../ai_dsp/index.html" /> 
+    <link rel="prev" title="GRAY_CNN(图片灰度化处理+图片识别)" href="../ai_dsp/gray_cnn.html" /> 
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@@ -86,7 +86,7 @@
            <div itemprop="articleBody">
              
   <section id="dsp">
-<h1>DSP应用示例<a class="headerlink" href="#dsp" title="Link to this heading"></a></h1>
+<h1>DSP应用示例<a class="headerlink" href="#dsp" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
-        <a href="../ai_dsp/index.html" class="btn btn-neutral float-left" title="AI+DSP应用示例" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
+        <a href="../ai_dsp/gray_cnn.html" class="btn btn-neutral float-left" title="GRAY_CNN(图片灰度化处理+图片识别)" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
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master/html/appdevelop/dsp/rdsar.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
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@@ -94,13 +94,13 @@
            <div itemprop="articleBody">
              
   <section id="rdsar-sar">
-<h1>RDSAR（距离-多普勒SAR成像算法）<a class="headerlink" href="#rdsar-sar" title="Link to this heading"></a></h1>
+<h1>RDSAR（距离-多普勒SAR成像算法）<a class="headerlink" href="#rdsar-sar" title="此标题的永久链接"></a></h1>
 <section id="id1">
-<h2>算法概述<a class="headerlink" href="#id1" title="Link to this heading"></a></h2>
+<h2>算法概述<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h2>
 <p>RDSAR（Range-Doppler SAR）是一种经典的合成孔径雷达（SAR）成像算法，通过距离-多普勒域处理实现高分辨率雷达图像重建。该算法是SAR成像的基础方法之一，广泛应用于遥感、军事侦察、地形测绘等领域。</p>
 </section>
 <section id="matlab">
-<h2>MATLAB 实现<a class="headerlink" href="#matlab" title="Link to this heading"></a></h2>
+<h2>MATLAB 实现<a class="headerlink" href="#matlab" title="此标题的永久链接"></a></h2>
 <div class="highlight-matlab notranslate"><div class="highlight"><pre><span></span><span class="cm">%{</span>
 <span class="cm">    本代码用于对雷达的回波数据，利用RD算法~普通版本进行成像。</span>
 <span class="cm">    2023/11/18 20:47</span>
@@ -207,7 +207,7 @@
 </div>
 </section>
 <section id="mindspore-signal">
-<h2>MindSpore Signal+ 实现<a class="headerlink" href="#mindspore-signal" title="Link to this heading"></a></h2>
+<h2>MindSpore Signal+ 实现<a class="headerlink" href="#mindspore-signal" title="此标题的永久链接"></a></h2>
 <p>在开始编写 MindSpore Signal+ 实现之前，建议先对原始 MATLAB 代码做流程梳理。可以将整体算法分为以下几个部分：</p>
 <ul class="simple">
 <li><p>1.数据读取</p></li>
@@ -216,7 +216,7 @@
 <li><p>4.数据后处理</p></li>
 </ul>
 <section id="id2">
-<h3>1. 数据读取<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
+<h3>1. 数据读取<a class="headerlink" href="#id2" title="此标题的永久链接"></a></h3>
 <p>在matlab中，数据读取是通过<code class="docutils literal notranslate"><span class="pre">importdata</span></code>函数实现的。在Python中使用MindSpore Signal+时，我们可以使用NumPy的<code class="docutils literal notranslate"><span class="pre">loadmat</span></code>或SciPy的<code class="docutils literal notranslate"><span class="pre">io.loadmat</span></code>来加载MATLAB文件中的变量，因此在Python代码开头需要导入NumPy和SciPy。</p>
 <p>示例代码：</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># step 0 : 准备初始数据</span>
@@ -235,7 +235,7 @@
 </div>
 </section>
 <section id="id3">
-<h3>2. 数据预处理<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
+<h3>2. 数据预处理<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h3>
 <p>从算法整体分析，数据读取后到核心计算之前的步骤，主要是对数据进行填充和轴的生成，这部分都是核心计算的前期准备，建议将这部分代码封装在<code class="docutils literal notranslate"><span class="pre">__init__</span></code>函数中完成，不放在<code class="docutils literal notranslate"><span class="pre">construct</span></code>函数中可以避免额外的开销，当实例化一个类时自动触发一次<code class="docutils literal notranslate"><span class="pre">__init__</span></code>函数。</p>
 <p>示例代码：</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">rdsar</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
@@ -263,7 +263,7 @@
 </div>
 </section>
 <section id="id4">
-<h3>3. 核心计算<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
+<h3>3. 核心计算<a class="headerlink" href="#id4" title="此标题的永久链接"></a></h3>
 <p>核心计算部分是算法的核心，也是计算复杂度最高的部分，建议将这部分代码封装在<code class="docutils literal notranslate"><span class="pre">construct</span></code>函数中，这样可以方便后续的调用。核心计算的迁移主要是将matlab的计算逻辑转换为MindSpore Signal+的API调用，例如：matlab中的<code class="docutils literal notranslate"><span class="pre">fft(echo,[],2)</span></code>可以转换为<code class="docutils literal notranslate"><span class="pre">mr.FFT(dim=1)</span></code>，dim=1 表示按行计算（沿着列移动，计算每行的FFT）。MindSpore Signal+ API列表可以查阅<a class="reference external" href="https://www.mindspore.cn/docs/zh-CN/r2.3.1/api_python/mindspore.html">MindSpore官方文档</a>和<a class="reference internal" href="../../functionlib/custom_op/index.html"><span class="doc">自定义算子列表</span></a>。</p>
 <p>示例代码：</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">rdsar</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
@@ -329,7 +329,7 @@
 </div>
 </section>
 <section id="id5">
-<h3>4. 数据后处理<a class="headerlink" href="#id5" title="Link to this heading"></a></h3>
+<h3>4. 数据后处理<a class="headerlink" href="#id5" title="此标题的永久链接"></a></h3>
 <p>需要对计算结果进行成像，这部分主要是将计算结果转换为图像，可以使用matplotlib库进行绘制。</p>
 <p>示例代码：</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
@@ -486,7 +486,7 @@
 </section>
 </section>
 <section id="id6">
-<h2>板卡部署<a class="headerlink" href="#id6" title="Link to this heading"></a></h2>
+<h2>板卡部署<a class="headerlink" href="#id6" title="此标题的永久链接"></a></h2>
 <p>模型部署建议使用 <code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code>，它集成了模型转换、模型可视化与 MindSpore Lite 端部署模板。具体使用方法可参考 <a class="reference internal" href="../../quickstart/hellodsp.html#c"><span class="std std-ref">HelloDSP MindSpore Lite端</span></a>。</p>
 <div class="admonition tip">
 <p class="admonition-title">小技巧</p>
@@ -494,7 +494,7 @@
 </div>
 </section>
 <section id="id7">
-<h2>参考与源码<a class="headerlink" href="#id7" title="Link to this heading"></a></h2>
+<h2>参考与源码<a class="headerlink" href="#id7" title="此标题的永久链接"></a></h2>
 <ul class="simple">
 <li><p>基于RD、CS和ωk算法的合成孔径雷达成像算法原理与实现：<a class="reference external" href="https://github.com/highskyno1/SAR_imaging_with_RD_CS_wk">SAR_imaging_with_RD_CS_wk</a></p></li>
 <li><p>Python 示例：<a class="reference external" href="https://gitee.com/nudt-674/mind-radar/tree/master/examples/rdsar">RDSAR</a></p></li>

master/html/appdevelop/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -82,7 +82,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>应用开发示例<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>应用开发示例<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="ai_dsp/index.html">AI+DSP应用示例</a></li>

master/html/functionlib/custom_op/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
     <script src="../../_static/js/theme.js"></script>
@@ -85,7 +85,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>自定义算子列表<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>自定义算子列表<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 </div>
 </section>

master/html/functionlib/dsplib/add.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
     <script src="../../_static/js/theme.js"></script>
@@ -47,7 +47,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../supported_op.html">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../custom_op/index.html">自定义算子列表</a></li>
@@ -90,11 +90,11 @@
            <div itemprop="articleBody">
              
   <section id="add">
-<h1>Add<a class="headerlink" href="#add" title="Link to this heading"></a></h1>
+<h1>Add<a class="headerlink" href="#add" title="此标题的永久链接"></a></h1>
 <p><strong>共享存储版本:</strong></p>
 <dl class="c function">
 <dt class="sig sig-object c" id="c.my_c_add_anycore">
-<span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">my_c_add_anycore</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param2</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.my_c_add_anycore" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">my_c_add_anycore</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param2</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.my_c_add_anycore" title="永久链接至目标"></a><br /></dt>
 <dd><p>两个输入Tensor逐元素相加。</p>
 <dl class="simple">
 <dt>输入：</dt><dd><ul class="simple">
@@ -129,7 +129,7 @@
 <p><strong>私有存储版本:</strong></p>
 <dl class="c function">
 <dt class="sig sig-object c" id="c.my_c_add_1core">
-<span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">my_c_add_1core</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param2</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.my_c_add_1core" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">my_c_add_1core</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param2</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.my_c_add_1core" title="永久链接至目标"></a><br /></dt>
 <dd><p>两个输入Tensor逐元素相加。</p>
 <dl class="simple">
 <dt>输入：</dt><dd><ul class="simple">

master/html/functionlib/dsplib/dsplib_index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,11 +14,10 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
-      <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="索引" href="../../genindex.html" />
@@ -48,7 +47,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../supported_op.html">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../custom_op/index.html">自定义算子列表</a></li>
@@ -90,7 +89,7 @@
            <div itemprop="articleBody">
              
   <section id="dsp-library-c-api-reference">
-<h1>DSP Library C API Reference<a class="headerlink" href="#dsp-library-c-api-reference" title="Link to this heading"></a></h1>
+<h1>DSP Library C API Reference<a class="headerlink" href="#dsp-library-c-api-reference" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="add.html">Add</a></li>

master/html/functionlib/dsplib/equal.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
@@ -48,7 +48,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../supported_op.html">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../custom_op/index.html">自定义算子列表</a></li>
@@ -91,46 +91,46 @@
            <div itemprop="articleBody">
              
   <section id="equal">
-<h1>Equal<a class="headerlink" href="#equal" title="Link to this heading"></a></h1>
+<h1>Equal<a class="headerlink" href="#equal" title="此标题的永久链接"></a></h1>
 <p><strong>共享存储版本:</strong></p>
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i8_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i16_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i16_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i16_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i16_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i16_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i32_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i32_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i32_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i32_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i32_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.hp_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_equal_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.fp_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.dp_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dp_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.dp_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dp_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.dp_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.c64_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c64_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c64_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c64_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c64_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.c128_equal_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c128_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c128_equal_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c128_equal_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c128_equal_s" title="永久链接至目标"></a><br /></dt>
 <dd><p>逐元素计算两个输入是否相等</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}output_i = \begin{cases}
@@ -180,42 +180,42 @@
 <p><strong>私有存储版本:</strong></p>
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i8_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i16_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i16_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i16_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i16_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int16_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i16_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.i32_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i32_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int32_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int32_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i32_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i32_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int32_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">int32_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i32_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.hp_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_equal_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.fp_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.dp_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dp_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.dp_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dp_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.dp_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.c64_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c64_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c64_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c64_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c64_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd></dd></dl>
 
 <dl class="c function">
 <dt class="sig sig-object c" id="c.c128_equal_p">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c128_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c128_equal_p" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">c128_equal_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input0</span></span>, <span class="kt"><span class="pre">double</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">Input1</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">length</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.c128_equal_p" title="永久链接至目标"></a><br /></dt>
 <dd><p>逐元素计算两个输入是否相等</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}output_i = \begin{cases}

master/html/functionlib/dsplib/fft_with_size.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
@@ -48,7 +48,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../supported_op.html">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../custom_op/index.html">自定义算子列表</a></li>
@@ -94,10 +94,10 @@
            <div itemprop="articleBody">
              
   <section id="fftwithsize">
-<h1>FFTWithSize<a class="headerlink" href="#fftwithsize" title="Link to this heading"></a></h1>
+<h1>FFTWithSize<a class="headerlink" href="#fftwithsize" title="此标题的永久链接"></a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="mindspore.ops.FFTWithSize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mindspore.ops.</span></span><span class="sig-name descname"><span class="pre">FFTWithSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">signal_ndim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">real</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'backward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.ops.FFTWithSize" title="Link to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mindspore.ops.</span></span><span class="sig-name descname"><span class="pre">FFTWithSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">signal_ndim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">real</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'backward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.ops.FFTWithSize" title="永久链接至目标"></a></dt>
 <dd><p>傅里叶变换，可以对参数进行调整，以实现FFT/IFFT/RFFT/IRFFT。</p>
 <p>对于FFT，它计算以下表达式：</p>
 <div class="math notranslate nohighlight">
@@ -143,9 +143,9 @@
 </li>
 <li><p><strong>norm</strong> (str，可选) - 表示该操作的规范化方式，可选值：[ <code class="docutils literal notranslate"><span class="pre">&quot;backward&quot;</span></code> , <code class="docutils literal notranslate"><span class="pre">&quot;forward&quot;</span></code> , <code class="docutils literal notranslate"><span class="pre">&quot;ortho&quot;</span></code> ]。默认值： <code class="docutils literal notranslate"><span class="pre">&quot;backward&quot;</span></code> 。</p>
 <ul>
-<li><p>&quot;backward&quot;，正向变换不缩放，逆变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，其中 <cite>n</cite> 表示输入 <cite>x</cite> 的元素数量。。</p></li>
-<li><p>&quot;ortho&quot;，正向变换与逆变换均按 <span class="math notranslate nohighlight">\(1/\sqrt n\)</span> 缩放。</p></li>
-<li><p>&quot;forward&quot;，正向变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，逆变换不缩放。</p></li>
+<li><p>“backward”，正向变换不缩放，逆变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，其中 <cite>n</cite> 表示输入 <cite>x</cite> 的元素数量。。</p></li>
+<li><p>“ortho”，正向变换与逆变换均按 <span class="math notranslate nohighlight">\(1/\sqrt n\)</span> 缩放。</p></li>
+<li><p>“forward”，正向变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，逆变换不缩放。</p></li>
 </ul>
 </li>
 <li><p><strong>onesided</strong> (bool，可选) - 控制输入是否减半以避免冗余。默认值： <code class="docutils literal notranslate"><span class="pre">True</span></code> 。</p></li>
@@ -170,7 +170,7 @@
 <li><p><strong>TypeError</strong> - 如果输入的类型不是Tensor。</p></li>
 <li><p><strong>ValueError</strong> - 如果输入 <cite>x</cite> 的维度小于 <cite>signal_ndim</cite> 。</p></li>
 <li><p><strong>ValueError</strong> - 如果 <cite>signal_ndim</cite> 大于3或小于1。</p></li>
-<li><p><strong>ValueError</strong> - 如果 <cite>norm</cite> 取值不是&quot;backward&quot;、&quot;forward&quot;或&quot;ortho&quot;。</p></li>
+<li><p><strong>ValueError</strong> - 如果 <cite>norm</cite> 取值不是”backward”、”forward”或”ortho”。</p></li>
 </ul>
 </dd>
 </dl>

master/html/functionlib/dsplib/log10.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../../_static/jquery.js?v=5d32c60e"></script>
       <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../../_static/doctools.js?v=888ff710"></script>
+      <script src="../../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../../_static/translations.js?v=beaddf03"></script>
       <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="../../_static/table.js?v=99ed15ba"></script>
@@ -48,7 +48,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="../index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="../supported_op.html">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../custom_op/index.html">自定义算子列表</a></li>
@@ -91,11 +91,11 @@
            <div itemprop="articleBody">
              
   <section id="log10">
-<h1>Log10<a class="headerlink" href="#log10" title="Link to this heading"></a></h1>
+<h1>Log10<a class="headerlink" href="#log10" title="此标题的永久链接"></a></h1>
 <p><strong>共享存储版本:</strong></p>
 <dl class="c function">
 <dt class="sig sig-object c" id="c.fp_log10_s">
-<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_log10_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">n</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">mask</span></span><span class="sig-paren">)</span><span class="p"><span class="pre">;</span></span><a class="headerlink" href="#c.fp_log10_s" title="Link to this definition"></a><br /></dt>
+<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_log10_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input1</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">n</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">output</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">mask</span></span><span class="sig-paren">)</span><span class="p"><span class="pre">;</span></span><a class="headerlink" href="#c.fp_log10_s" title="永久链接至目标"></a><br /></dt>
 <dd><p>快速计算以10为底的对数</p>
 <div class="math notranslate nohighlight">
 \[output_i = \log_{10}(input1_i)\]</div>
@@ -127,9 +127,9 @@
 <span class="linenos"> 5</span><span class="w">    </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">input1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x100000000</span><span class="p">;</span><span class="w">   </span><span class="c1">//input在DDR空间</span>
 <span class="linenos"> 6</span><span class="w">    </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x100001000</span><span class="p">;</span>
 <span class="linenos"> 7</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">n</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1000</span><span class="p">;</span>
-<span class="hll"><span class="linenos"> 8</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mh">0xf</span><span class="p">;</span>
-</span><span class="linenos"> 9</span><span class="w">    </span><span class="n">fp_log10_s</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"> </span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">mask</span><span class="p">);</span>
-<span class="linenos">10</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+<span class="linenos"> 8</span><span class="w">    </span><span class="kt">int</span><span class="w"> </span><span class="n">mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mh">0xf</span><span class="p">;</span>
+<span class="hll"><span class="linenos"> 9</span><span class="w">    </span><span class="n">fp_log10_s</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"> </span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">mask</span><span class="p">);</span>
+</span><span class="linenos">10</span><span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
 <span class="linenos">11</span><span class="p">}</span>
 </pre></div>
 </div>

master/html/functionlib/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,11 +14,10 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
-      <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="索引" href="../genindex.html" />
@@ -83,7 +82,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>算子库支持<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>算子库支持<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="supported_op.html">算子库支持情况</a></li>

master/html/functionlib/supported_op.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -47,7 +47,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">算子库支持</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">算子库支持情况</a></li>
 <li class="toctree-l2"><a class="reference internal" href="custom_op/index.html">自定义算子列表</a></li>
@@ -83,7 +83,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>算子库支持情况<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>算子库支持情况<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <ul class="simple">
 <li><p>MindSpore Lite支持不同硬件后端的算子列表</p></li>
 </ul>
@@ -4315,7 +4315,8 @@
 </tr>
 </tbody>
 </table>
-</div></section>
+</div>
+</section>
 
 
            </div>

master/html/genindex.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="./">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
@@ -13,9 +13,9 @@
   
       <script src="_static/jquery.js?v=5d32c60e"></script>
       <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=406e4f49"></script>
-      <script src="_static/doctools.js?v=9bcbadda"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=595569a8"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="_static/translations.js?v=beaddf03"></script>
       <script src="_static/table.js?v=99ed15ba"></script>
     <script src="_static/js/theme.js"></script>

master/html/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="./">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,11 +14,10 @@
   
       <script src="_static/jquery.js?v=5d32c60e"></script>
       <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=406e4f49"></script>
-      <script src="_static/doctools.js?v=9bcbadda"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=595569a8"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="_static/translations.js?v=beaddf03"></script>
-      <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
       <script src="_static/table.js?v=99ed15ba"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="索引" href="genindex.html" />
@@ -77,7 +76,7 @@
            <div itemprop="articleBody">
              
   <section id="mindspore-signal">
-<h1>MindSpore Signal+ 使用手册<a class="headerlink" href="#mindspore-signal" title="Link to this heading"></a></h1>
+<h1>MindSpore Signal+ 使用手册<a class="headerlink" href="#mindspore-signal" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="quickstart/index.html">快速入门</a><ul>

master/html/quickstart/hellodsp.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -100,16 +100,16 @@
            <div itemprop="articleBody">
              
   <section id="hellodsp">
-<h1>HelloDSP<a class="headerlink" href="#hellodsp" title="Link to this heading"></a></h1>
+<h1>HelloDSP<a class="headerlink" href="#hellodsp" title="此标题的永久链接"></a></h1>
 <p>HelloDSP 是一个简单的MindSpore Signal+平台开发示例，展示如何开发一个矩阵乘程序。主要流程分为两部分操作，分别为<a class="reference internal" href="#python"><span class="std std-ref">MindSpore Python端</span></a>和<a class="reference internal" href="#c"><span class="std std-ref">MindSpore Lite端</span></a>。MindSpore Python端在本地运行，用于生成模型；MindSpore Lite端在本地交叉编译，编译产物拷贝到MT7004板卡上运行。以下是HelloDSP矩阵乘例子介绍：</p>
 <section id="mindspore-python">
-<span id="python"></span><h2>1.MindSpore Python端<a class="headerlink" href="#mindspore-python" title="Link to this heading"></a></h2>
+<span id="python"></span><h2>1.MindSpore Python端<a class="headerlink" href="#mindspore-python" title="此标题的永久链接"></a></h2>
 <section id="id1">
-<h3>1.1 新建Python文件<a class="headerlink" href="#id1" title="Link to this heading"></a></h3>
+<h3>1.1 新建Python文件<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h3>
 <p>打开<code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code>，新建<code class="docutils literal notranslate"><span class="pre">test_matmul.py</span></code>文件</p>
 </section>
 <section id="id2">
-<h3>1.2 编写Python代码<a class="headerlink" href="#id2" title="Link to this heading"></a></h3>
+<h3>1.2 编写Python代码<a class="headerlink" href="#id2" title="此标题的永久链接"></a></h3>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">ops</span>
@@ -154,21 +154,21 @@
 </ul>
 </section>
 <section id="id3">
-<h3>1.3 运行Python代码<a class="headerlink" href="#id3" title="Link to this heading"></a></h3>
+<h3>1.3 运行Python代码<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h3>
 <p>在<code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code>中运行<code class="docutils literal notranslate"><span class="pre">test_matmul.py</span></code>文件，会生成一个<code class="docutils literal notranslate"><span class="pre">matmul.mindir</span></code>模型文件，要在MindSpore Lite端运行则需要转成ms模型文件。mindir转ms的converter工具也已经集成到vscode，有两种方式使用converter工具：</p>
 <p>方式一：点击调试按钮；点击模型转换；选择要转的mindir模型文件即可。</p>
 <p>方式二：选择模型文件直接右键，找到模型转换选项，点击即可。</p>
 </section>
 </section>
 <section id="mindspore-lite">
-<span id="c"></span><h2>MindSpore Lite端<a class="headerlink" href="#mindspore-lite" title="Link to this heading"></a></h2>
+<span id="c"></span><h2>MindSpore Lite端<a class="headerlink" href="#mindspore-lite" title="此标题的永久链接"></a></h2>
 <section id="id4">
-<h3>1. 新建工程<a class="headerlink" href="#id4" title="Link to this heading"></a></h3>
+<h3>1. 新建工程<a class="headerlink" href="#id4" title="此标题的永久链接"></a></h3>
 <p>打开<code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code>，新建工程。输入工程名、路径，工程类型选择<code class="docutils literal notranslate"><span class="pre">Heterogeneous</span></code>，输入交叉编译工具路径，参照<a class="reference internal" href="installation.html#toolchain"><span class="std std-ref">配置交叉编译工具链</span></a>，然后点确定。会生成一个异构模板工程。</p>
 <img src="../_static/create_project.png" alt="create_project" width="auto"/>
 </section>
 <section id="id5">
-<h3>2. 工程目录结构<a class="headerlink" href="#id5" title="Link to this heading"></a></h3>
+<h3>2. 工程目录结构<a class="headerlink" href="#id5" title="此标题的永久链接"></a></h3>
 <p>生成的异构模板工程具有以下目录结构：</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>test_matmul/
 ├──<span class="w"> </span>model/
@@ -218,7 +218,7 @@
 </table>
 </section>
 <section id="id6">
-<h3>3. 更改输入数据<a class="headerlink" href="#id6" title="Link to this heading"></a></h3>
+<h3>3. 更改输入数据<a class="headerlink" href="#id6" title="此标题的永久链接"></a></h3>
 <p>通过修改 <code class="docutils literal notranslate"><span class="pre">data_handler.cc</span></code> 文件中的函数来调整输入输出数据：</p>
 <ul class="simple">
 <li><p><strong>修改输入数据</strong>：编辑 <code class="docutils literal notranslate"><span class="pre">GetInputData</span></code> 函数内容</p></li>
@@ -232,15 +232,15 @@
 <img src="../_static/init_input_data.png" alt="init_input_data" width="auto"/>
 </section>
 <section id="main-cc">
-<h3>4. main.cc 功能介绍<a class="headerlink" href="#main-cc" title="Link to this heading"></a></h3>
+<h3>4. main.cc 功能介绍<a class="headerlink" href="#main-cc" title="此标题的永久链接"></a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">main</span></code> 函数主要包含 6 个核心步骤，实现完整的模型推理流程：</p>
 <section id="id7">
-<h4>4.1 读取模型文件<a class="headerlink" href="#id7" title="Link to this heading"></a></h4>
+<h4>4.1 读取模型文件<a class="headerlink" href="#id7" title="此标题的永久链接"></a></h4>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">ReadFile</span></code> 接口读取 <code class="docutils literal notranslate"><span class="pre">.ms</span></code> 模型文件，将数据保存到 <code class="docutils literal notranslate"><span class="pre">model_buf</span></code> 中。</p>
 <img src="../_static/read_model.png" alt="read_model" width="auto"/>
 </section>
 <section id="id8">
-<h4>4.2 设置运行后端<a class="headerlink" href="#id8" title="Link to this heading"></a></h4>
+<h4>4.2 设置运行后端<a class="headerlink" href="#id8" title="此标题的永久链接"></a></h4>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">context</span></code> 的 <code class="docutils literal notranslate"><span class="pre">MutableDeviceInfo</span></code> 添加一个或多个后端：</p>
 <ul class="simple">
 <li><p><strong>默认后端</strong>：<code class="docutils literal notranslate"><span class="pre">MT7004</span></code></p></li>
@@ -249,7 +249,7 @@
 <img src="../_static/set_device.png" alt="set_device" width="auto"/>
 </section>
 <section id="id9">
-<h4>4.3 编译模型图<a class="headerlink" href="#id9" title="Link to this heading"></a></h4>
+<h4>4.3 编译模型图<a class="headerlink" href="#id9" title="此标题的永久链接"></a></h4>
 <p>通过 MindSpore 的 <code class="docutils literal notranslate"><span class="pre">Model</span></code> 类的 <code class="docutils literal notranslate"><span class="pre">Build</span></code> 方法编译模型图：</p>
 <p><strong>编译规则</strong>：按设置顺序查找后端算子，都没找到则编译失败</p>
 <p><strong>Build 方法参数：</strong></p>
@@ -282,12 +282,12 @@
 <img src="../_static/build_model.png" alt="build_model" width="auto"/>
 </section>
 <section id="id10">
-<h4>4.4 获取模型输入<a class="headerlink" href="#id10" title="Link to this heading"></a></h4>
+<h4>4.4 获取模型输入<a class="headerlink" href="#id10" title="此标题的永久链接"></a></h4>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">Model</span></code> 类的 <code class="docutils literal notranslate"><span class="pre">GetInputs</span></code> 方法获取所有模型输入的 <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> 地址，通过修改 <code class="docutils literal notranslate"><span class="pre">GetInputData</span></code> 函数传递输入值。</p>
 <img src="../_static/get_input.png" alt="get_input" width="auto"/>
 </section>
 <section id="id11">
-<h4>4.5 执行模型推理<a class="headerlink" href="#id11" title="Link to this heading"></a></h4>
+<h4>4.5 执行模型推理<a class="headerlink" href="#id11" title="此标题的永久链接"></a></h4>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">Model</span></code> 类的 <code class="docutils literal notranslate"><span class="pre">Predict</span></code> 方法进行模型推理：</p>
 <p><strong>Predict 方法参数：</strong></p>
 <table class="docutils align-default">
@@ -311,13 +311,13 @@
 <img src="../_static/model_predict.png" alt="model_predict" width="auto"/>
 </section>
 <section id="id12">
-<h4>4.6 获取模型结果<a class="headerlink" href="#id12" title="Link to this heading"></a></h4>
+<h4>4.6 获取模型结果<a class="headerlink" href="#id12" title="此标题的永久链接"></a></h4>
 <p>通过 <code class="docutils literal notranslate"><span class="pre">Model</span></code> 类的 <code class="docutils literal notranslate"><span class="pre">GetOutputs</span></code> 方法获取所有模型输出，通过修改 <code class="docutils literal notranslate"><span class="pre">GetOutputData</span></code> 函数查看输出值。</p>
 <img src="../_static/get_output.png" alt="get_output" width="auto"/>
 </section>
 </section>
 <section id="id13">
-<h3>5. 编译工程<a class="headerlink" href="#id13" title="Link to this heading"></a></h3>
+<h3>5. 编译工程<a class="headerlink" href="#id13" title="此标题的永久链接"></a></h3>
 <p>提供多种编译方式，选择其中一种即可：</p>
 <p><strong>编译方式：</strong></p>
 <ul class="simple">
@@ -328,10 +328,10 @@
 <img src="../_static/build_project.png" alt="build_project" width="auto"/>
 </section>
 <section id="id14">
-<h3>6. 运行工程<a class="headerlink" href="#id14" title="Link to this heading"></a></h3>
+<h3>6. 运行工程<a class="headerlink" href="#id14" title="此标题的永久链接"></a></h3>
 <p>编译生成的可执行文件位于 <code class="docutils literal notranslate"><span class="pre">build</span></code> 目录下，默认文件名为 <code class="docutils literal notranslate"><span class="pre">main</span></code>。需要将可执行文件拷贝到 MT7004 板卡上运行。</p>
 <section id="mt7004">
-<h4>6.1 连接 MT7004 板卡<a class="headerlink" href="#mt7004" title="Link to this heading"></a></h4>
+<h4>6.1 连接 MT7004 板卡<a class="headerlink" href="#mt7004" title="此标题的永久链接"></a></h4>
 <p><strong>步骤 1：</strong> 打开远程窗口</p>
 <ul class="simple">
 <li><p>在 <code class="docutils literal notranslate"><span class="pre">YHFT-IDE</span></code> 左下角找到远程窗口功能</p></li>
@@ -356,14 +356,15 @@
 <img src="../_static/ssh_success.png" alt="ssh_success" width="auto"/>
 </section>
 <section id="id15">
-<h4>6.2 部署和运行程序<a class="headerlink" href="#id15" title="Link to this heading"></a></h4>
+<h4>6.2 部署和运行程序<a class="headerlink" href="#id15" title="此标题的永久链接"></a></h4>
 <p><strong>步骤 4：</strong> 部署程序文件</p>
 <ul class="simple">
 <li><p>在 MT7004 板卡上新建目录存放程序</p></li>
 <li><p>通过拖拽方式将可执行文件和模型文件传输到板卡</p></li>
 <li><p>使用 <code class="docutils literal notranslate"><span class="pre">chmod</span></code> 命令赋予可执行文件执行权限</p></li>
 </ul>
-<img src="../_static/model_run.png" alt="model_run" width="auto"/></section>
+<img src="../_static/model_run.png" alt="model_run" width="auto"/>
+</section>
 </section>
 </section>
 </section>

master/html/quickstart/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -82,7 +82,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>快速入门<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>快速入门<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">环境安装</a></li>

master/html/quickstart/installation.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,15 +14,15 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="索引" href="../genindex.html" />
     <link rel="search" title="搜索" href="../search.html" />
-    <link rel="next" title="HelloDSP" href="helloworld.html" />
+    <link rel="next" title="HelloDSP" href="hellodsp.html" />
     <link rel="prev" title="快速入门" href="index.html" /> 
 </head>
 
@@ -60,11 +60,11 @@
 </li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="helloworld.html">HelloDSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hellodsp.html">HelloDSP</a></li>
 <li class="toctree-l2"><a class="reference internal" href="overview.html">整体概览</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../functionlib/index.html">算子库支持</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../refdoc/index.html">参考资料</a></li>
 </ul>
@@ -95,9 +95,9 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>环境安装<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>环境安装<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <section id="mindspore-radar">
-<h2>安装MindSpore Radar 与依赖软件<a class="headerlink" href="#mindspore-radar" title="Link to this heading"></a></h2>
+<h2>安装MindSpore Radar 与依赖软件<a class="headerlink" href="#mindspore-radar" title="此标题的永久链接"></a></h2>
 <table class="docutils align-default">
 <thead>
 <tr class="row-odd"><th class="head"><p>软件名称</p></th>
@@ -142,18 +142,18 @@
 </table>
 <p>上述所有软件包可以通过在线链接提取：<a class="reference external" href="https://">软件资源包</a></p>
 <section id="python">
-<span id="install-python"></span><h3>安装Python<a class="headerlink" href="#python" title="Link to this heading"></a></h3>
+<span id="install-python"></span><h3>安装Python<a class="headerlink" href="#python" title="此标题的永久链接"></a></h3>
 <p>本文提供两种不同的安装方式,自行选择合适的方式安装。</p>
 <section id="id2">
-<h4>1.安装包方式安装<a class="headerlink" href="#id2" title="Link to this heading"></a></h4>
+<h4>1.安装包方式安装<a class="headerlink" href="#id2" title="此标题的永久链接"></a></h4>
 <section id="id3">
-<h5>1.1 下载安装包<a class="headerlink" href="#id3" title="Link to this heading"></a></h5>
+<h5>1.1 下载安装包<a class="headerlink" href="#id3" title="此标题的永久链接"></a></h5>
 <p>根据机器的操作系统版本，在官网下载32位或64位windows版本的python3.8安装包：</p>
 <p>64位：<a class="reference external" href="https://www.python.org/ftp/python/3.8.10/python-3.8.10-amd64.exe">https://www.python.org/ftp/python/3.8.10/python-3.8.10-amd64.exe</a></p>
 <p>32位：<a class="reference external" href="https://www.python.org/ftp/python/3.8.10/python-3.8.10.exe">https://www.python.org/ftp/python/3.8.10/python-3.8.10.exe</a></p>
 </section>
 <section id="id4">
-<h5>1.2 开始安装<a class="headerlink" href="#id4" title="Link to this heading"></a></h5>
+<h5>1.2 开始安装<a class="headerlink" href="#id4" title="此标题的永久链接"></a></h5>
 <p>双击打开python安装包，选择Customize installation方式安装，并勾选<code class="docutils literal notranslate"><span class="pre">Add</span> <span class="pre">python3.8</span> <span class="pre">to</span> <span class="pre">PATH</span></code>。如下图：</p>
 <img src="../_static/python_install_step1.png" alt="python_install_step1" width="auto"/>
 <p>接着按默认选项点击下一步，可以根据需求选择安装路径，以及是否安装给所有用户。如下图：</p>
@@ -162,7 +162,7 @@
 <img src="../_static/python_install_success.png" alt="python_install_success" width="auto"/>
 </section>
 <section id="id5">
-<h5>1.3 验证安装<a class="headerlink" href="#id5" title="Link to this heading"></a></h5>
+<h5>1.3 验证安装<a class="headerlink" href="#id5" title="此标题的永久链接"></a></h5>
 <p>可以在cmd命令窗口通过以下命令查看Python版本。</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>--version
 </pre></div>
@@ -170,7 +170,7 @@
 </section>
 </section>
 <section id="conda">
-<h4>2.Conda方式安装<a class="headerlink" href="#conda" title="Link to this heading"></a></h4>
+<h4>2.Conda方式安装<a class="headerlink" href="#conda" title="此标题的永久链接"></a></h4>
 <p><a class="reference external" href="https://docs.conda.io/en/latest/">Conda</a>是一个开源跨平台语言无关的包管理与环境管理系统，允许用户方便地安装不同版本的二进制软件包，以及该计算平台需要的所有库。</p>
 <ul class="simple">
 <li><p>确认安装Windows是x86架构64位操作系统。</p></li>
@@ -182,7 +182,7 @@
 </li>
 </ul>
 <section id="id6">
-<h5>创建并进入Conda虚拟环境<a class="headerlink" href="#id6" title="Link to this heading"></a></h5>
+<h5>创建并进入Conda虚拟环境<a class="headerlink" href="#id6" title="此标题的永久链接"></a></h5>
 <p>在Windows上使用Anaconda，请通过<code class="docutils literal notranslate"><span class="pre">开始</span> <span class="pre">|</span> <span class="pre">Anaconda3</span> <span class="pre">|</span> <span class="pre">Anaconda</span> <span class="pre">Promt</span></code>打开Anaconda命令行。</p>
 <p>根据您希望使用的Python版本，创建对应的Conda虚拟环境，并进入虚拟环境。
 如果您希望使用Python3.8.10版本：</p>
@@ -194,7 +194,7 @@ conda<span class="w"> </span>activate<span class="w"> </span>mindspore_py38
 </section>
 </section>
 <section id="mindspore">
-<span id="install-mindspore"></span><h3>安装MindSpore<a class="headerlink" href="#mindspore" title="Link to this heading"></a></h3>
+<span id="install-mindspore"></span><h3>安装MindSpore<a class="headerlink" href="#mindspore" title="此标题的永久链接"></a></h3>
 <p>通过<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">mindspore-xxx-win_amd64.whl</span></code>命令安装MindSpore。验证是否成功安装，执行以下命令：</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import mindspore;mindspore.set_device(device_target=&#39;CPU&#39;);mindspore.run_check()&quot;</span>
 </pre></div>
@@ -211,7 +211,7 @@ The result of multiplication calculation is correct, MindSpore has been installe
 </div>
 </section>
 <section id="mindradar">
-<span id="install-mindradar"></span><h3>安装MindRadar<a class="headerlink" href="#mindradar" title="Link to this heading"></a></h3>
+<span id="install-mindradar"></span><h3>安装MindRadar<a class="headerlink" href="#mindradar" title="此标题的永久链接"></a></h3>
 <p>通过<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">mindradar-xxx-py3-none-any.whl</span></code>命令安装MindRadar。验证是否成功安装，执行以下命令：</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import mindspore;import mindradar;from mindradar import ComplexAbs;&quot;</span>
 </pre></div>
@@ -219,20 +219,20 @@ The result of multiplication calculation is correct, MindSpore has been installe
 <p>如果没有报错，说明MindRadar安装成功了。</p>
 </section>
 <section id="toolchain">
-<span id="id7"></span><h3>配置交叉编译工具链<a class="headerlink" href="#toolchain" title="Link to this heading"></a></h3>
+<span id="id7"></span><h3>配置交叉编译工具链<a class="headerlink" href="#toolchain" title="此标题的永久链接"></a></h3>
 <p>官网下载windows版本的交叉编译工具：
 <a class="reference external" href="https://releases.linaro.org/components/toolchain/binaries/7.5-2019.12/arm-linux-gnueabihf/gcc-linaro-7.5.0-2019.12-i686-mingw32_arm-linux-gnueabihf.tar.xz">https://releases.linaro.org/components/toolchain/binaries/7.5-2019.12/arm-linux-gnueabihf/gcc-linaro-7.5.0-2019.12-i686-mingw32_arm-linux-gnueabihf.tar.xz</a></p>
 <p>将下载好的交叉编译工具包解压到一个全英文的文件夹即可，后续编译MindSpore Signal+ C++ 应用时需要填写交叉编译工具包的绝对路径。</p>
 </section>
 <section id="yhft-ide">
-<span id="install-yhft-ide"></span><h3>安装YHFT-IDE<a class="headerlink" href="#yhft-ide" title="Link to this heading"></a></h3>
+<span id="install-yhft-ide"></span><h3>安装YHFT-IDE<a class="headerlink" href="#yhft-ide" title="此标题的永久链接"></a></h3>
 <p>找到软件安装包，双击打开安装，同意条款；选择安装目录，点击下一步：</p>
 <img src="../_static/yhft-ide_install.png" alt="yhft-ide_install" width="auto"/>
 <p>然后一直按默认选项点下一步，直到点击安装。安装完成如下：</p>
 <img src="../_static/yhft-ide_install_success.png" alt="yhft-ide_install_success" width="auto"/>
 </section>
 <section id="cmake">
-<span id="install-cmake"></span><h3>安装CMake<a class="headerlink" href="#cmake" title="Link to this heading"></a></h3>
+<span id="install-cmake"></span><h3>安装CMake<a class="headerlink" href="#cmake" title="此标题的永久链接"></a></h3>
 <p>CMake官网选择合适版本下载CMake安装包:<a class="reference external" href="https://cmake.org/download/">https://cmake.org/download/</a></p>
 <p>双击打开安装包，会弹出如下界面，点击<code class="docutils literal notranslate"><span class="pre">Next</span></code>，进入下一步：</p>
 <img src="../_static/cmake_install_step1.png" alt="cmake_install_step1" width="auto"/>
@@ -245,7 +245,7 @@ The result of multiplication calculation is correct, MindSpore has been installe
 <img src="../_static/cmake_install_success.png" alt="cmake_install_step3" width="auto"/>
 </section>
 <section id="netron">
-<span id="install-netron"></span><h3>安装Netron<a class="headerlink" href="#netron" title="Link to this heading"></a></h3>
+<span id="install-netron"></span><h3>安装Netron<a class="headerlink" href="#netron" title="此标题的永久链接"></a></h3>
 <p>找到软件安装包，双击打开安装即可。</p>
 </section>
 </section>
@@ -256,7 +256,7 @@ The result of multiplication calculation is correct, MindSpore has been installe
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
         <a href="index.html" class="btn btn-neutral float-left" title="快速入门" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
-        <a href="helloworld.html" class="btn btn-neutral float-right" title="HelloDSP" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="hellodsp.html" class="btn btn-neutral float-right" title="HelloDSP" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>

master/html/quickstart/overview.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,16 +14,16 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="索引" href="../genindex.html" />
     <link rel="search" title="搜索" href="../search.html" />
-    <link rel="next" title="应用开发" href="../appdevelop/index.html" />
-    <link rel="prev" title="HelloDSP" href="helloworld.html" /> 
+    <link rel="next" title="应用开发示例" href="../appdevelop/index.html" />
+    <link rel="prev" title="HelloDSP" href="hellodsp.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -48,11 +48,11 @@
               <ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">快速入门</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="installation.html">环境安装</a></li>
-<li class="toctree-l2"><a class="reference internal" href="helloworld.html">HelloDSP</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hellodsp.html">HelloDSP</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">整体概览</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../functionlib/index.html">算子库支持</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../refdoc/index.html">参考资料</a></li>
 </ul>
@@ -83,15 +83,15 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>整体概览<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>整体概览<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 </section>
 
 
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="页脚">
-        <a href="helloworld.html" class="btn btn-neutral float-left" title="HelloDSP" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
-        <a href="../appdevelop/index.html" class="btn btn-neutral float-right" title="应用开发" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="hellodsp.html" class="btn btn-neutral float-left" title="HelloDSP" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> 上一页</a>
+        <a href="../appdevelop/index.html" class="btn btn-neutral float-right" title="应用开发示例" accesskey="n" rel="next">下一页 <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>

master/html/refdoc/index.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -47,7 +47,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../functionlib/index.html">算子库支持</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">参考资料</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="mindspore.html">官方资料</a></li>
@@ -81,7 +81,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>参考资料<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>参考资料<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="mindspore.html">官方资料</a></li>

master/html/refdoc/mindspore.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -47,7 +47,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../functionlib/index.html">算子库支持</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">参考资料</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">官方资料</a></li>
@@ -82,7 +82,7 @@
            <div itemprop="articleBody">
              
   <section id="id1">
-<h1>官方资料<a class="headerlink" href="#id1" title="Link to this heading"></a></h1>
+<h1>官方资料<a class="headerlink" href="#id1" title="此标题的永久链接"></a></h1>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.mindspore.cn/">MindSpore</a></p></li>
 <li><p><a class="reference external" href="https://www.mindspore.cn/lite/">Minsdpore Lite</a></p></li>

master/html/refdoc/signal_scheduling.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="../">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
@@ -14,9 +14,9 @@
   
       <script src="../_static/jquery.js?v=5d32c60e"></script>
       <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=406e4f49"></script>
-      <script src="../_static/doctools.js?v=9bcbadda"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=595569a8"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="../_static/translations.js?v=beaddf03"></script>
       <script src="../_static/table.js?v=99ed15ba"></script>
     <script src="../_static/js/theme.js"></script>
@@ -46,7 +46,7 @@
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="导航菜单">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../quickstart/index.html">快速入门</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../appdevelop/index.html">应用开发示例</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../functionlib/index.html">算子库支持</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">参考资料</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="mindspore.html">官方资料</a></li>
@@ -81,7 +81,7 @@
            <div itemprop="articleBody">
              
   <section id="mindspore-signal">
-<h1>MindSpore Signal+ 调度方案<a class="headerlink" href="#mindspore-signal" title="Link to this heading"></a></h1>
+<h1>MindSpore Signal+ 调度方案<a class="headerlink" href="#mindspore-signal" title="此标题的永久链接"></a></h1>
 <p>1.xxx</p>
 </section>
 

master/html/search.html

@@ -1,7 +1,7 @@
 
 
 <!DOCTYPE html>
-<html class="writer-html5" lang="zh-CN" data-content_root="./">
+<html class="writer-html5" lang="zh-CN">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
@@ -14,9 +14,9 @@
     
       <script src="_static/jquery.js?v=5d32c60e"></script>
       <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=406e4f49"></script>
-      <script src="_static/doctools.js?v=9bcbadda"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=595569a8"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
       <script src="_static/translations.js?v=beaddf03"></script>
       <script src="_static/table.js?v=99ed15ba"></script>
     <script src="_static/js/theme.js"></script>