Merge branch 'docs' into 'master'

Update docs for transformation after model loading

See merge request !1229

docs/development/how_to_debug.rst

@@ -39,7 +39,7 @@ You can validate it by specifying `--validate` option while running the model.
 MACE automatically validate these metrics by running models with synthetic inputs.
 If you want to specify input data to use, you can add an option in yaml config under 'subgraphs', e.g.,
 
-.. code:: yaml
+.. code-block:: yaml
 
 	models:
 	  mobilenet_v1:
@@ -82,13 +82,13 @@ For CMake users, modify the cmake configuration located in ``tools/cmake/`` as `
 
 For Bazel users,
 
-  .. code:: sh
+  .. code-block:: sh
 
     python tools/converter.py run --config=/path/to/config.yml --debug_mode
 
 For android, you can use `ndk-stack tools <https://developer.android.com/ndk/guides/ndk-stack?hl=EN>`__ to symbolize stack trace, e.g.,
 
-  .. code:: sh
+  .. code-block:: sh
 
     adb logcat | $ANDROID_NDK_HOME/ndk-stack -sym /path/to/local/binary/directory/
 
@@ -140,7 +140,7 @@ Debug engine using GDB
 GDB can be used as the last resort, as it is powerful that it can trace stacks of your process. If you run models on android,
 things may be a little bit complicated.
 
-	.. code:: sh
+	.. code-block:: sh
 
 		# push gdbserver to your phone
 		adb push $ANDROID_NDK_HOME/prebuilt/android-arm64/gdbserver/gdbserver /data/local/tmp/

docs/installation/env_requirement.rst

@@ -36,7 +36,7 @@ Required dependencies
 
 For Bazel, install it following installation guide. For python dependencies,
 
-	.. code:: sh
+	.. code-block:: sh
 
 		pip install -U --user -r setup/requirements.txt
 
@@ -78,7 +78,7 @@ Optional dependencies
 
 For python dependencies,
 
-	.. code:: sh
+	.. code-block:: sh
 
 		pip install -U --user -r setup/optionals.txt
 

docs/installation/manual_setup.rst

@@ -10,7 +10,7 @@ Install Bazel
 
 Recommend bazel with version larger than ``0.13.0`` (Refer to `Bazel documentation <https://docs.bazel.build/versions/master/install.html>`__).
 
-.. code:: sh
+.. code-block:: sh
 
     export BAZEL_VERSION=0.13.1
     mkdir /bazel && \
@@ -27,7 +27,7 @@ Install Android NDK
 The recommended Android NDK versions includes r15b, r15c and r16b (Refers to
 `NDK installation guide <https://developer.android.com/ndk/guides/setup#install>`__).
 
-.. code:: sh
+.. code-block:: sh
 
     # Download NDK r15c
     cd /opt/ && \
@@ -45,7 +45,7 @@ The recommended Android NDK versions includes r15b, r15c and r16b (Refers to
 Install extra tools
 --------------------
 
-.. code:: sh
+.. code-block:: sh
 
     apt-get install -y --no-install-recommends \
         cmake \
@@ -63,7 +63,7 @@ Install extra tools
 Install TensorFlow (Optional)
 ------------------------------
 
-.. code:: sh
+.. code-block:: sh
 
     pip install -i http://pypi.douban.com/simple/ --trusted-host pypi.douban.com tensorflow==1.8.0
 

docs/installation/using_docker.rst

@@ -13,7 +13,7 @@ In most cases, the ``lite edition`` image can satisfy developer's basic needs.
 
 - ``lite edition`` docker image.
 
-.. code:: sh
+.. code-block:: sh
 
     # You can pull lite edition docker image from docker repo (recommended)
     docker pull registry.cn-hangzhou.aliyuncs.com/xiaomimace/mace-dev-lite
@@ -22,7 +22,7 @@ In most cases, the ``lite edition`` image can satisfy developer's basic needs.
 
 - ``full edition`` docker image (which contains multiple NDK versions and other dev tools).
 
-.. code:: sh
+.. code-block:: sh
 
     # You can pull full edition docker image from docker repo (recommended)
     docker pull registry.cn-hangzhou.aliyuncs.com/xiaomimace/mace-dev
@@ -39,7 +39,7 @@ Using the image
 
 Create container with the following command
 
-.. code:: sh
+.. code-block:: sh
 
     # Create a container named `mace-dev`
     docker run -it --privileged -d --name mace-dev \
@@ -58,7 +58,7 @@ Update images to repository
 If you are mace inner developer and need update images in remote repository,
 it can be achieved by `docker/update_images.sh` script.
 
-.. code:: sh
+.. code-block:: sh
 
     cd docker/
     ./update_images.sh

docs/user_guide/advanced_usage.rst

@@ -106,7 +106,7 @@ in one deployment file.
 
     Some command tools:
 
-    .. code:: bash
+    .. code-block:: bash
 
         # Get device's soc info.
         adb shell getprop | grep platform
@@ -129,7 +129,7 @@ Run you model on the embedded device(ARM Linux)
 
 The way to run your model on the ARM Linux is nearly same as with android, except you need specify a device config file.
 
-.. code:: bash
+.. code-block:: bash
 
     python tools/converter.py run --config=/path/to/your/model_deployment_file.yml --device_yml=/path/to/devices.yml
 
@@ -139,7 +139,7 @@ There are two steps to do before run:
 
     MACE use ssh to connect embedded device, you should copy your public key to embedded device with the blow command.
 
-    .. code:: bash
+    .. code-block:: bash
 
       cat ~/.ssh/id_rsa.pub | ssh -q {user}@{ip} "cat >> ~/.ssh/authorized_keys"
 
@@ -176,7 +176,7 @@ There are two steps to do before run:
 
 
 Convert model(s) to C++ code
---------------------------------
+----------------------------
 
     * **1. Change the model deployment file(.yml)**
 
@@ -184,14 +184,14 @@ Convert model(s) to C++ code
 
         * convert model graph to code and model weight to file with below model configuration.
 
-        .. code:: sh
+        .. code-block:: sh
 
             model_graph_format: code
             model_data_format: file
 
         * convert both model graph and model weight to code with below model configuration.
 
-        .. code:: sh
+        .. code-block:: sh
 
             model_graph_format: code
             model_data_format: code
@@ -202,14 +202,14 @@ Convert model(s) to C++ code
 
     * **2. Convert model(s) to code**
 
-        .. code:: sh
+        .. code-block:: sh
 
             python tools/converter.py convert --config=/path/to/model_deployment_file.yml
 
         The command will generate **${library_name}.a** in **build/${library_name}/model** directory and
         ** *.h ** in **build/${library_name}/include** like the following dir-tree.
 
-        .. code::
+        .. code-block:: none
 
              # model_graph_format: code
              # model_data_format: file
@@ -240,7 +240,7 @@ Convert model(s) to C++ code
         * Link `libmace.a` and `${library_name}.a` to your target.
         * Refer to \ ``mace/tools/mace_run.cc``\ for full usage. The following list the key steps.
 
-        .. code:: cpp
+        .. code-block:: cpp
 
             // Include the headers
             #include "mace/public/mace.h"
@@ -269,9 +269,70 @@ Convert model(s) to C++ code
             // ... Same with the code in basic usage
 
 
-Tuning for specific SoC's GPU
+Transform models after conversion
 ---------------------------------
 
+    If ``model_graph_format`` or ``model_data_format`` is specified as `file`, the model or weight file will
+    be generated as a `.pb` or `.data` file after model conversion. After that, more transformations can be
+    applied to the generated files, such as compression or encryption. To achieve that, the model loading is
+    split to two stages: 1) load the file from file system to memory buffer; 2) create the MACE engine from the
+    model buffer. So between the two stages, transformations can be inserted to decompress or decrypt the model
+    buffer. The transformations are user defined. The following lists the key steps when both ``model_graph_format``
+    and ``model_data_format`` are set as `file`.
+
+    .. code-block:: cpp
+
+        // Load model graph from file system
+        std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_graph_data =
+            make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
+        if (FLAGS_model_file != "") {
+          auto fs = GetFileSystem();
+          status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_file.c_str(),
+              &model_graph_data);
+          if (status != MaceStatus::MACE_SUCCESS) {
+            // Report error or fallback
+          }
+        }
+        // Load model data from file system
+        std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_weights_data =
+            make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
+        if (FLAGS_model_data_file != "") {
+          auto fs = GetFileSystem();
+          status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_data_file.c_str(),
+              &model_weights_data);
+          if (status != MaceStatus::MACE_SUCCESS) {
+            // Report error or fallback
+          }
+        }
+        if (model_graph_data == nullptr || model_weights_data == nullptr) {
+          // Report error or fallback
+        }
+
+        // Add transformations here.
+        ...
+
+        // Create the MACE engine from the model buffer
+        std::shared_ptr<mace::MaceEngine> engine;
+        MaceStatus create_engine_status;
+        create_engine_status =
+            CreateMaceEngineFromProto(reinterpret_cast<const unsigned char *>(
+                                        model_graph_data->data()),
+                                      model_graph_data->length(),
+                                      reinterpret_cast<const unsigned char *>(
+                                        model_weights_data->data()),
+                                      model_weights_data->length(),
+                                      input_names,
+                                      output_names,
+                                      config,
+                                      &engine);
+        if (create_engine_status != MaceStatus::MACE_SUCCESS) {
+          // Report error or fallback
+        }
+
+
+Tuning for specific SoC's GPU
+-----------------------------
+
     If you want to use the GPU of a specific device, you can just specify the ``target_socs`` in your YAML file and
     then tune the MACE lib for it (OpenCL kernels), which may get 1~10% performance improvement.
 
@@ -279,7 +340,7 @@ Tuning for specific SoC's GPU
 
         Specify ``target_socs`` in your model deployment file(.yml):
 
-        .. code:: sh
+        .. code-block:: sh
 
             target_socs: [sdm845]
 
@@ -289,7 +350,7 @@ Tuning for specific SoC's GPU
 
     * **2. Convert model(s)**
 
-        .. code:: sh
+        .. code-block:: sh
 
             python tools/converter.py convert --config=/path/to/model_deployment_file.yml
 
@@ -303,13 +364,13 @@ Tuning for specific SoC's GPU
              You should plug in device(s) with the specific SoC(s).
 
 
-        .. code:: sh
+        .. code-block:: sh
 
             python tools/converter.py run --config=/path/to/model_deployment_file.yml --validate
 
         The command will generate two files in `build/${library_name}/opencl`, like the following dir-tree.
 
-        .. code::
+        .. code-block:: none
 
               build
               └── mobilenet-v2
@@ -336,7 +397,7 @@ Tuning for specific SoC's GPU
         * Change the names of files generated above for not collision and push them to **your own device's directory**.
         * Use like the previous procedure, below lists the key steps differently.
 
-        .. code:: cpp
+        .. code-block:: cpp
 
             // Include the headers
             #include "mace/public/mace.h"
@@ -379,7 +440,7 @@ Useful Commands
 ---------------
 * **run the model**
 
-.. code:: sh
+.. code-block:: sh
 
     # Test model run time
     python tools/converter.py run --config=/path/to/model_deployment_file.yml --round=100
@@ -403,7 +464,7 @@ Useful Commands
 
 the detailed information is in :doc:`benchmark`.
 
-.. code:: sh
+.. code-block:: sh
 
     # Benchmark model, get detailed statistics of each Op.
     python tools/converter.py run --config=/path/to/model_deployment_file.yml --benchmark
@@ -446,7 +507,7 @@ the detailed information is in :doc:`benchmark`.
 
 Use ``-h`` to get detailed help.
 
-.. code:: sh
+.. code-block:: sh
 
     python tools/converter.py -h
     python tools/converter.py build -h
@@ -480,7 +541,7 @@ Reduce Library Size
 Remove the registration of the ops unused for your models in the ``mace/ops/ops_register.cc``,
 which will reduce the library size significantly. the final binary just link the registered ops' code.
 
-.. code:: cpp
+.. code-block:: cpp
 
     #include "mace/ops/ops_register.h"
 

docs/user_guide/advanced_usage_cmake.rst

@@ -84,7 +84,7 @@ There are many advanced options supported.
 
     Some command tools:
 
-    .. code:: bash
+    .. code-block:: bash
 
         # Get device's soc info.
         adb shell getprop | grep platform
@@ -107,7 +107,7 @@ Run you model on the embedded device(ARM Linux)
 
 The way to run your model on the ARM Linux is nearly same as with android, except you need specify a device config file.
 
-  .. code:: bash
+  .. code-block:: bash
 
     python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --validate --device_yml=/path/to/devices.yml
 
@@ -117,7 +117,7 @@ There are two steps to do before run:
 
     MACE use ssh to connect embedded device, you should copy your public key to embedded device with the blow command.
 
-    .. code:: bash
+    .. code-block:: bash
 
       cat ~/.ssh/id_rsa.pub | ssh -q {user}@{ip} "cat >> ~/.ssh/authorized_keys"
 
@@ -158,14 +158,14 @@ Model Protection
 
 Model can be encrypted by obfuscation.
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/encrypt.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml
 
 It will override ``mobilenet_v1.pb`` and ``mobilenet_v1.data``. 
 If you want to compiled the model into a library, you should use options ``--gencode_model --gencode_param`` to generate model code, i.e.,
 
-    .. code:: bash
+    .. code-block:: bash
     
         python tools/python/encrypt.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --gencode_model --gencode_param
 
@@ -173,7 +173,7 @@ It will generate model code into ``mace/codegen/models`` and also generate a hel
 
 After that you can rebuild the engine. 
     
-    .. code:: bash
+    .. code-block:: bash
 
         RUNTIME=GPU RUNMODE=code bash tools/cmake/cmake-build-armeabi-v7a.sh
 
@@ -181,7 +181,7 @@ After that you can rebuild the engine.
 
 When you test the model in code format, you should specify it in the script as follows.
     
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --gencode_model --gencode_param
 
@@ -192,7 +192,7 @@ When you need to integrate the libraries into your applications, you can link `l
 
 Refer to \ ``mace/tools/mace_run.cc``\ for full usage. The following list the key steps.
 
-    .. code:: cpp
+    .. code-block:: cpp
 
         // Include the headers
         #include "mace/public/mace.h"
@@ -221,6 +221,67 @@ Refer to \ ``mace/tools/mace_run.cc``\ for full usage. The following list the ke
         // ... Same with the code in basic usage
 
 
+Transform models after conversion
+---------------------------------
+
+If ``model_graph_format`` or ``model_data_format`` is specified as `file`, the model or weight file will
+be generated as a `.pb` or `.data` file after model conversion. After that, more transformations can be
+applied to the generated files, such as compression or encryption. To achieve that, the model loading is
+split to two stages: 1) load the file from file system to memory buffer; 2) create the MACE engine from the
+model buffer. So between the two stages, transformations can be inserted to decompress or decrypt the model
+buffer. The transformations are user defined. The following lists the key steps when both ``model_graph_format``
+and ``model_data_format`` are set as `file`.
+
+    .. code-block:: cpp
+
+        // Load model graph from file system
+        std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_graph_data =
+            make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
+        if (FLAGS_model_file != "") {
+          auto fs = GetFileSystem();
+          status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_file.c_str(),
+              &model_graph_data);
+          if (status != MaceStatus::MACE_SUCCESS) {
+            // Report error or fallback
+          }
+        }
+        // Load model data from file system
+        std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_weights_data =
+            make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
+        if (FLAGS_model_data_file != "") {
+          auto fs = GetFileSystem();
+          status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_data_file.c_str(),
+              &model_weights_data);
+          if (status != MaceStatus::MACE_SUCCESS) {
+            // Report error or fallback
+          }
+        }
+        if (model_graph_data == nullptr || model_weights_data == nullptr) {
+          // Report error or fallback
+        }
+
+        // Add transformations here.
+        ...
+
+        // Create the MACE engine from the model buffer
+        std::shared_ptr<mace::MaceEngine> engine;
+        MaceStatus create_engine_status;
+        create_engine_status =
+            CreateMaceEngineFromProto(reinterpret_cast<const unsigned char *>(
+                                        model_graph_data->data()),
+                                      model_graph_data->length(),
+                                      reinterpret_cast<const unsigned char *>(
+                                        model_weights_data->data()),
+                                      model_weights_data->length(),
+                                      input_names,
+                                      output_names,
+                                      config,
+                                      &engine);
+        if (create_engine_status != MaceStatus::MACE_SUCCESS) {
+          // Report error or fallback
+        }
+
+
 Tuning for specific SoC's GPU
 ---------------------------------
 
@@ -228,13 +289,13 @@ If you want to use the GPU of a specific device, you can tune the performance fo
 
 You can specify `--tune` option when you want to run and tune the performance at the same time.
 
-    .. code:: bash
+    .. code-block:: bash
     
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --tune
 
 It will generate OpenCL tuned parameter binary file in `build/mobilenet_v1/opencl` directory.
     
-    .. code:: bash
+    .. code-block:: none
 
         └── mobilenet_v1_tuned_opencl_parameter.MIX2S.sdm845.bin
 
@@ -243,7 +304,7 @@ It specifies your test platform model and SoC. You can use it in production to r
 To deploy it, change the names of files generated above for not collision and push them to **your own device's directory**.
 Use like the previous procedure, below lists the key steps differently.
 
-    .. code:: cpp
+    .. code-block:: cpp
 
         // Include the headers
         #include "mace/public/mace.h"
@@ -275,13 +336,13 @@ Multi Model Support (optional)
 If multiple models are configured in config file. After you test it, it will generate more than one tuned parameter files.
 Then you need to merge them together.
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/gen_opencl.py
 
 After that, it will generate one set of files into `build/opencl` directory.
 
-    .. code:: bash
+    .. code-block:: none
 
         ├── compiled_opencl_kernel.bin
         └── tuned_opencl_parameter.bin
@@ -309,7 +370,7 @@ Reduce Library Size
 Remove the registration of the ops unused for your models in the ``mace/ops/ops_register.cc``,
 which will reduce the library size significantly. the final binary just link the registered ops' code.
 
-.. code:: cpp
+.. code-block:: cpp
 
     #include "mace/ops/ops_register.h"
 

docs/user_guide/basic_usage.rst

@@ -16,7 +16,7 @@ Here we use the mobilenet-v2 model as an example.
 
     1. Pull `MACE <https://github.com/XiaoMi/mace>`__ project.
 
-    .. code:: sh
+    .. code-block:: sh
 
         git clone https://github.com/XiaoMi/mace.git
         cd mace/
@@ -33,14 +33,14 @@ Here we use the mobilenet-v2 model as an example.
 
     2. Pull `MACE Model Zoo <https://github.com/XiaoMi/mace-models>`__ project.
 
-    .. code:: sh
+    .. code-block:: sh
 
         git clone https://github.com/XiaoMi/mace-models.git
 
 
     3. Build a generic MACE library.
 
-    .. code:: sh
+    .. code-block:: sh
 
         cd path/to/mace
         # Build library
@@ -59,7 +59,7 @@ Here we use the mobilenet-v2 model as an example.
 
     4. Convert the pre-trained mobilenet-v2 model to MACE format model.
 
-    .. code:: sh
+    .. code-block:: sh
 
         cd path/to/mace
         # Build library
@@ -73,7 +73,7 @@ Here we use the mobilenet-v2 model as an example.
         If you want to run on phone, please plug in at least one phone.
         Or if you want to run on embedded device, please give a :doc:`advanced_usage`.
 
-    .. code:: sh
+    .. code-block:: sh
 
         # Run
         python tools/converter.py run --config=/path/to/mace-models/mobilenet-v2/mobilenet-v2.yml
@@ -107,7 +107,7 @@ MACE now supports models from TensorFlow and Caffe (more frameworks will be supp
 
    If your model is from lower version Caffe, you need to upgrade it by using the Caffe built-in tool before converting.
 
-   .. code:: bash
+   .. code-block:: bash
 
        # Upgrade prototxt
        $CAFFE_ROOT/build/tools/upgrade_net_proto_text MODEL.prototxt MODEL.new.prototxt
@@ -123,7 +123,7 @@ MACE now supports models from TensorFlow and Caffe (more frameworks will be supp
    This tool will improve the efficiency of inference like the `Graph Transform Tool <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md>`__
    in TensorFlow.
 
-   .. code:: bash
+   .. code-block:: bash
 
        # Optimize your model
        $python MACE_ROOT/tools/onnx_optimizer.py model.onnx model_opt.onnx
@@ -165,7 +165,7 @@ More details about model deployment file are in :doc:`advanced_usage`.
 
 When the deployment file is ready, you can use MACE converter tool to convert your model(s).
 
-.. code:: bash
+.. code-block:: bash
 
     python tools/converter.py convert --config=/path/to/your/model_deployment_file.yml
 
@@ -184,7 +184,7 @@ You could Download the prebuilt MACE Library from `Github MACE release page <htt
 
 Or use bazel to build MACE source code into a library.
 
-    .. code:: sh
+    .. code-block:: sh
 
         cd path/to/mace
         # Build library
@@ -213,7 +213,7 @@ to run and validate your model.
 
     run the model.
 
-    .. code:: sh
+    .. code-block:: sh
 
     	# Test model run time
         python tools/converter.py run --config=/path/to/your/model_deployment_file.yml --round=100
@@ -230,7 +230,7 @@ to run and validate your model.
 
     benchmark and profile the model. the details are in :doc:`benchmark`.
 
-    .. code:: sh
+    .. code-block:: sh
 
         # Benchmark model, get detailed statistics of each Op.
         python tools/converter.py run --config=/path/to/your/model_deployment_file.yml --benchmark
@@ -256,7 +256,7 @@ However, there are some differences in different devices.
 
     MACE only supports Qualcomm DSP. And you need to push the hexagon nn library to the device.
 
-    .. code:: sh
+    .. code-block:: sh
 
         # For Android device
         adb root; adb remount
@@ -276,7 +276,7 @@ header files.
 
 -  The generated ``static`` library files are organized as follows,
 
-.. code::
+.. code-block:: none
 
     build
     ├── include
@@ -310,7 +310,7 @@ header files.
 
 Please refer to \ ``mace/tools/mace_run.cc``\ for full usage. The following list the key steps.
 
-.. code:: cpp
+.. code-block:: cpp
 
     // Include the headers
     #include "mace/public/mace.h"

docs/user_guide/basic_usage_cmake.rst

@@ -8,7 +8,7 @@ Clear Workspace
 
 Before you do anything, clear the workspace used by build and test process.
 
-    .. code:: sh
+    .. code-block:: sh
 
         tools/clear_workspace.sh
 
@@ -18,7 +18,7 @@ Build Engine
 
 Please make sure you have CMake installed.
 
-    .. code:: sh
+    .. code-block:: sh
 
         RUNTIME=GPU bash tools/cmake/cmake-build-armeabi-v7a.sh
 
@@ -33,7 +33,7 @@ Model Conversion
 
 When you have prepared your model, the first thing to do is write a model config in YAML format.
 
-    .. code:: yaml
+    .. code-block:: yaml
 
         models:
           mobilenet_v1:
@@ -56,13 +56,13 @@ When you have prepared your model, the first thing to do is write a model config
 The following steps generate output to ``build`` directory which is the default build and test workspace.
 Suppose you have the model config in ``../mace-models/mobilenet-v1/mobilenet-v1.yml``. Then run
 
-    .. code:: yaml
+    .. code-block:: yaml
 
         python tools/python/convert.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml
 
 which generate 4 files in ``build/mobilenet_v1/model/``
 
-    .. code:: sh
+    .. code-block:: none
 
         ├── mobilenet_v1.pb                (model file)
         ├── mobilenet_v1.data              (param file)
@@ -81,13 +81,13 @@ We provide simple tools to test and benchmark your model.
 
 After model is converted, simply run
 
-    .. code:: sh
+    .. code-block:: sh
 
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --validate
 
 Or benchmark the model
 
-    .. code:: sh
+    .. code-block:: sh
 
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --benchmark
 
@@ -104,7 +104,7 @@ Deploy your model into applications
 
 Please refer to \ ``mace/tools/mace_run.cc``\ for full usage. The following list the key steps.
 
-.. code:: cpp
+.. code-block:: cpp
 
     // Include the headers
     #include "mace/public/mace.h"

docs/user_guide/benchmark.rst

@@ -20,7 +20,7 @@ Usage
 
 For CMake users:
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/run_target.py \
             --target_abi=armeabi-v7a --target_socs=all --target_name=mace_cc_benchmark \
@@ -28,7 +28,7 @@ For CMake users:
 
 or for Bazel users:
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/bazel_adb_run.py --target="//test/ccbenchmark:mace_cc_benchmark" \
             --run_target=True  --args="--filter=.*BM_CONV.*"
@@ -37,7 +37,7 @@ or for Bazel users:
 Output
 ======
 
-    .. code:: bash
+    .. code-block:: python
 
         Benchmark                                                    Time(ns) Iterations Input(MB/s)   GMACPS
         ------------------------------------------------------------------------------------------------------
@@ -79,14 +79,14 @@ Usage
 
 For CMake users:
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/run_model.py --config=/path/to/your/model_deployment.yml --benchmark
 
 
 or for Bazel users:
 
-    .. code:: bash
+    .. code-block:: bash
 
         python tools/python/converter.py run --config=/path/to/your/model_deployment.yml --benchmark
 
@@ -94,7 +94,7 @@ or for Bazel users:
 Output
 ======
 
-    .. code:: bash
+    .. code-block:: python
 
         I statistics.cc:343 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         I statistics.cc:343                                                                                      Sort by Run Order

docs/user_guide/quantization_usage.rst

@@ -35,7 +35,7 @@ MACE provides tools to do statistics with following steps:
 	2. Log tensor range of each activation layer by inferring several samples on CPU host. Sample inputs should be
 	representative to calculate the ranges of each layer properly.
 
-	.. code:: sh
+	.. code-block:: sh
 
 		# Convert images to input tensors for MACE, see image_to_tensor.py for more arguments.
 		python tools/image/image_to_tensor.py --input /path/to/directory/of/input/images
@@ -60,7 +60,7 @@ MACE provides tools to do statistics with following steps:
 	to try different ranges and see which is better. Experimentation shows that the default `percentile` and `enhance_ratio`
 	works fine for several common models.
 
-	.. code:: sh
+	.. code-block:: sh
 
 		python tools/python/quantize/quantize_stat.py --log_file range_log > overall_range
 

docs/zh/installation/env_requirement.rst

@@ -68,7 +68,7 @@ MACE 需要安装下列依赖：
 
 对于 Python 依赖，可直接执行,
 
-	.. code:: sh
+	.. code-block:: sh
 
 		pip install -U --user -r setup/optionals.txt
 

docs/zh/user_guide/basic_usage.rst

@@ -8,7 +8,7 @@
 
 构建前，清空工作目录
 
-    .. code:: sh
+    .. code-block:: sh
 
         tools/clear_workspace.sh
 
@@ -18,7 +18,7 @@
 
 确保 CMake 已安装 
 
-    .. code:: sh
+    .. code-block:: sh
 
         RUNTIME=GPU bash tools/cmake/cmake-build-armeabi-v7a.sh
 
@@ -33,7 +33,7 @@
 
 撰写模型相关的 YAML 配置文件：
 
-    .. code:: yaml
+    .. code-block:: yaml
 
         models:
           mobilenet_v1:
@@ -55,13 +55,13 @@
 
 假设模型配置文件的路径是： ``../mace-models/mobilenet-v1/mobilenet-v1.yml``，执行：
 
-    .. code:: yaml
+    .. code-block:: yaml
 
         python tools/python/convert.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml
 
 将会在 ``build/mobilenet_v1/model/`` 中产生 4 个文件
 
-    .. code:: sh
+    .. code-block:: none
 
         ├── mobilenet_v1.pb                (模型结构文件)
         ├── mobilenet_v1.data              (模型参数文件)
@@ -80,13 +80,13 @@
 
 模型转换后，执行下面命令进行测试：
 
-    .. code:: sh
+    .. code-block:: sh
 
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --validate
 
 或下面命令进行性能评测：
 
-    .. code:: sh
+    .. code-block:: sh
 
         python tools/python/run_model.py --config ../mace-models/mobilenet-v1/mobilenet-v1.yml --benchmark
 
@@ -100,7 +100,7 @@
 
 可以查看源码 \ ``mace/tools/mace_run.cc``\ 了解更多详情。下面简要介绍相关步骤：
 
-.. code:: cpp
+.. code-block:: cpp
 
     // 添加头文件按
     #include "mace/public/mace.h"