Merge branch 'docs' into 'master'

Update docs

See merge request !468

docs/conf.py

@@ -10,9 +10,6 @@ project = u'MiAI Compute Engine'
 author = u'%s Developers' % project
 copyright = u'2018, %s' % author
 
-version = u'0.6'
-release = u'0.6.0 alpha'
-
 source_parsers = {
     '.md': recommonmark.parser.CommonMarkParser,
 }

docs/development/adding_a_new_op.md

@@ -3,7 +3,7 @@ Adding a new Op
 
 You can create a custom op if it is not supported yet.
 
-To add a custom op, you need to finish the following steps.
+To add a custom op, you need to finish the following steps:
 
 Define the Op class
 --------------------
@@ -95,3 +95,7 @@ Add test and benchmark
 ----------------------
 It's strongly recommended to add unit test and micro benchmark for your
 new Op. If you wish to contribute back, it's required.
+
+Document the new Op
+---------------------
+Finally, add an entry in operator table in the document.

docs/development/contributing.md

@@ -34,7 +34,8 @@ clang-format -style="{BasedOnStyle: google,            \
 C++ logging guideline
 ---------------------
 
-The rule of VLOG level:
+VLOG is used for verbose logging, which is configured by environment variable
+`MACE_CPP_MIN_VLOG_LEVEL`. The guideline of VLOG level is as follows:
 
 ```
 0. Ad hoc debug logging, should only be added in test or temporary ad hoc

docs/development/memory_layout.rst

@@ -5,17 +5,21 @@ CPU runtime memory layout
 -------------------------
 The CPU tensor buffer is organized in the following order:
 
-+-----------------------------+--------------+
-| Tensor type                 | Buffer       |
-+=============================+==============+
-| Intermediate input/output   | NCHW         |
-+-----------------------------+--------------+
-| Convolution Filter          | OIHW         |
-+-----------------------------+--------------+
-| Depthwise Convolution Filter| MIHW         |
-+-----------------------------+--------------+
-| 1-D Argument, length = W    | W            |
-+-----------------------------+--------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Buffer
+    * - Intermediate input/output
+      - NCHW
+    * - Convolution Filter
+      - OIHW
+    * - Depthwise Convolution Filter
+      - MIHW
+    * - 1-D Argument, length = W
+      - W
 
 OpenCL runtime memory layout
 -----------------------------
@@ -34,66 +38,117 @@ Input/Output Tensor
 
 The Input/Output Tensor is stored in NHWC format:
 
-+---------------------------+--------+----------------------------+-----------------------------+
-|Tensor type                | Buffer | Image size [width, height] | Explanation                 |
-+===========================+========+============================+=============================+
-|Channel-Major Input/Output | NHWC   | [W * (C+3)/4, N * H]       | Default Input/Output format |
-+---------------------------+--------+----------------------------+-----------------------------+
-|Height-Major Input/Output  | NHWC   | [W * C, N * (H+3)/4]       | Winograd Convolution format | 
-+---------------------------+--------+----------------------------+-----------------------------+
-|Width-Major Input/Output   | NHWC   | [(W+3)/4 * C, N * H]       | Winograd Convolution format |
-+---------------------------+--------+----------------------------+-----------------------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Buffer
+      - Image size [width, height]
+      - Explanation
+    * - Channel-Major Input/Output
+      - NHWC
+      - [W * (C+3)/4, N * H]
+      - Default Input/Output format
+    * - Height-Major Input/Output
+      - NHWC
+      - [W * C, N * (H+3)/4
+      - Winograd Convolution format
+    * - Width-Major Input/Output
+      - NHWC
+      - [(W+3)/4 * C, N * H]
+      - Winograd Convolution format
 
 Each Pixel of **Image** contains 4 elements. The below table list the
 coordination relation between **Image** and **Buffer**.
 
-+---------------------------+-------------------------------------------------------------------------+-------------+
-|Tensor type                | Pixel coordinate relationship                                           | Explanation |
-+===========================+=========================================================================+=============+
-|Channel-Major Input/Output | P[i, j] = {E[n, h, w, c] | (n=j/H, h=j%H, w=i%W, c=[i/W * 4 + k])} | k=[0, 4)    |
-+---------------------------+-------------------------------------------------------------------------+-------------+
-|Height-Major Input/Output  | P[i, j] = {E[n, h, w, c] | (n=j%N, h=[j/H*4 + k], w=i%W, c=i/W)}   | k=[0, 4)    |
-+---------------------------+-------------------------------------------------------------------------+-------------+
-|Width-Major Input/Output   | P[i, j] = {E[n, h, w, c] | (n=j/H, h=j%H, w=[i%W*4 + k], c=i/W)}   | k=[0, 4)    |
-+---------------------------+-------------------------------------------------------------------------+-------------+
-
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Pixel coordinate relationship
+      - Explanation
+    * - Channel-Major Input/Output
+      - P[i, j] = {E[n, h, w, c] | (n=j/H, h=j%H, w=i%W, c=[i/W * 4 + k])}
+      - k=[0, 4)
+    * - Height-Major Input/Output
+      - P[i, j] = {E[n, h, w, c] | (n=j%N, h=[j/H*4 + k], w=i%W, c=i/W)}
+      - k=[0, 4)
+    * - Width-Major Input/Output
+      - P[i, j] = {E[n, h, w, c] | (n=j/H, h=j%H, w=[i%W*4 + k], c=i/W)}
+      - k=[0, 4)
 
 Filter Tensor
 ~~~~~~~~~~~~~
 
-+----------------------------+------+---------------------------------+------------------------------------------------------------------------------+
-| Tensor                     |Buffer| Image size [width, height]      | Explanation                                                                  |
-+============================+======+=================================+==============================================================================+
-|Convolution Filter          | HWOI | [RoundUp<4>(I), H * W * (O+3)/4]|Convolution filter format，There is no difference compared to [H*w*I, (O+3)/4]|
-+----------------------------+------+---------------------------------+------------------------------------------------------------------------------+
-|Depthwise Convlution Filter | HWIM | [H * W * M, (I+3)/4]            |Depthwise-Convolution filter format                                           |
-+----------------------------+------+---------------------------------+------------------------------------------------------------------------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor
+      - Buffer
+      - Image size [width, height]
+      - Explanation
+    * - Convolution Filter
+      - HWOI
+      - [RoundUp<4>(I), H * W * (O+3)/4]
+      - Convolution filter format，There is no difference compared to [H*w*I, (O+3)/4]
+    * - Depthwise Convlution Filter
+      - HWIM
+      - [H * W * M, (I+3)/4]
+      - Depthwise-Convolution filter format
 
 Each Pixel of **Image** contains 4 elements. The below table list the
 coordination relation between **Image** and **Buffer**.
 
-+----------------------------+-------------------------------------------------------------------+---------------------------------------+
-|Tensor type                 | Pixel coordinate relationship                                     | Explanation                           |
-+============================+===================================================================+=======================================+
-|Convolution Filter          | P[m, n] = {E[h, w, o, i] &#124; (h=T/W, w=T%W, o=[n/HW*4+k], i=m)}| HW= H * W, T=n%HW, k=[0, 4)           |
-+----------------------------+-------------------------------------------------------------------+---------------------------------------+
-|Depthwise Convlution Filter | P[m, n] = {E[h, w, i, 0] &#124; (h=m/W, w=m%W, i=[n*4+k])}        | only support multiplier == 1, k=[0, 4)| 
-+----------------------------+-------------------------------------------------------------------+---------------------------------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Pixel coordinate relationship
+      - Explanation
+    * - Convolution Filter
+      - P[m, n] = {E[h, w, o, i] | (h=T/W, w=T%W, o=[n/HW*4+k], i=m)}
+      - HW= H * W, T=n%HW, k=[0, 4)
+    * - Depthwise Convlution Filter
+      - P[m, n] = {E[h, w, i, 0] | (h=m/W, w=m%W, i=[n*4+k])}
+      - only support multiplier == 1, k=[0, 4)
 
 1-D Argument Tensor
 ~~~~~~~~~~~~~~~~~~~
 
-+----------------+----------+------------------------------+---------------------------------+
-| Tensor type    | Buffer   | Image size [width, height]   | Explanation                     |
-+================+==========+==============================+=================================+
-| 1-D Argument   | W        | [(W+3)/4, 1]                 | 1D argument format, e.g. Bias   |
-+----------------+----------+------------------------------+---------------------------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Buffer
+      - Image size [width, height]
+      - Explanation
+    * - 1-D Argument
+      - W
+      - [(W+3)/4, 1]
+      - 1D argument format, e.g. Bias
 
 Each Pixel of **Image** contains 4 elements. The below table list the
 coordination relation between **Image** and **Buffer**.
 
-+--------------+---------------------------------+-------------+
-| Tensor type  | Pixel coordinate relationship   | Explanation |
-+==============+=================================+=============+
-|1-D Argument  | P[i, 0] = {E[w] &#124; w=i*4+k} | k=[0, 4)    |
-+--------------+---------------------------------+-------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Tensor type
+      - Pixel coordinate relationship
+      - Explanation
+    * - 1-D Argument
+      - P[i, 0] = {E[w] | w=i*4+k}
+      - k=[0, 4)
+

docs/faq.md

-Frequently Asked Questions
+Frequently asked questions
 ==========================
 
 Why is the generated static library file size so huge?

docs/getting_started/create_a_model_deployment.rst

-Create a model deployment
-=========================
+Create a model deployment file
+==============================
 
-Each YAML deployment script describes a case of deployments (for example,
-a smart camera application may contains face recognition, object recognition,
-and voice recognition models, which can be defined in one deployment file),
-which will generate one static library (if more than one ABIs specified,
-there will be one static library for each). Each YAML scripts can contains one
-or more models.
+The first step to deploy you models is to create a YAML model deployment
+file.
 
+One deployment file describes a case of model deployment,
+each file will generate one static library (if more than one ABIs specified,
+there will be one static library for each). The deployment file can contains
+one or more models, for example, a smart camera application may contains face
+recognition, object recognition, and voice recognition models, which can be
+defined in one deployment file),
 
-Model deployment file example
--------------------------------
-TODO: change to a link to a standalone file with comments.
 
-.. code:: yaml
+Example
+----------
+Here is an deployment file example used by Android demo application.
 
-    # 配置文件名会被用作生成库的名称：libmace-${filename}.a
-    target_abis: [armeabi-v7a, arm64-v8a]
-    # 具体机型的soc编号，可以使用`adb shell getprop | grep ro.board.platform | cut -d [ -f3 | cut -d ] -f1`获取
-    target_socs: [msm8998]
-    embed_model_data: 1
-    vlog_level: 0
-    models: # 一个配置文件可以包含多个模型的配置信息，最终生成的库中包含多个模型
-      first_net: # 模型的标签，在调度模型的时候，会用这个变量
-        platform: tensorflow
-        model_file_path: path/to/model64.pb # also support http:// and https://
-        model_sha256_checksum: 7f7462333406e7dea87222737590ebb7d94490194d2f21a7d72bafa87e64e9f9
-        input_nodes: input_node
-        output_nodes: output_node
-        input_shapes: 1,64,64,3
-        output_shapes: 1,64,64,2
-        runtime: gpu
-        limit_opencl_kernel_time: 0
-        dsp_mode: 0
-        obfuscate: 1
-        fast_conv: 0
-        input_files:
-          - path/to/input_files # support http://
-      second_net:
-        platform: caffe
-        model_file_path: path/to/model.prototxt
-        weight_file_path: path/to/weight.caffemodel
-        model_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
-        weight_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
-        input_nodes:
-          - input_node0
-          - input_node1
-        output_nodes:
-          - output_node0
-          - output_node1
-        input_shapes:
-          - 1,256,256,3
-          - 1,128,128,3
-        output_shapes:
-          - 1,256,256,2
-          - 1,1,1,2
-        runtime: cpu
-        limit_opencl_kernel_time: 1
-        dsp_mode: 0
-        obfuscate: 1
-        fast_conv: 0
-        input_files:
-          - path/to/input_files # support http://
+TODO: change this example file to the demo deployment file
+(reuse the same file) and rename to a reasonable name.
+
+.. literalinclude:: models/demo_app_models.yaml
+   :language: yaml
 
 Configurations
 --------------------
 
-+--------------------------+----------------------------------------------------------------------------------------+
-| Configuration key        | Description                                                                            |
-+==========================+========================================================================================+
-| target_abis              | The target ABI to build, can be one or more of 'host', 'armeabi-v7a' or 'arm64-v8a'    |
-+--------------------------+----------------------------------------------------------------------------------------+
-| embed_model_data         | Whether embedding model weights as the code, default to 1                              |
-+--------------------------+----------------------------------------------------------------------------------------+
-| platform                 | The source framework, tensorflow or caffe                                              |
-+--------------------------+----------------------------------------------------------------------------------------+
-| model_file_path          | The path of the model file, can be local or remote                                     |
-+--------------------------+----------------------------------------------------------------------------------------+
-| weight_file_path         | The path of the model weights file, used by Caffe model                                |
-+--------------------------+----------------------------------------------------------------------------------------+
-| model_sha256_checksum    | The SHA256 checksum of the model file                                                  |
-+--------------------------+----------------------------------------------------------------------------------------+
-| weight_sha256_checksum   | The SHA256 checksum of the weight file, used by Caffe model                            |
-+--------------------------+----------------------------------------------------------------------------------------+
-| input_nodes              | The input node names, one or more strings                                              |
-+--------------------------+----------------------------------------------------------------------------------------+
-| output_nodes             | The output node names, one or more strings                                             |
-+--------------------------+----------------------------------------------------------------------------------------+
-| input_shapes             | The shapes of the input nodes, in NHWC order                                           |
-+--------------------------+----------------------------------------------------------------------------------------+
-| output_shapes            | The shapes of the output nodes, in NHWC order                                          |
-+--------------------------+----------------------------------------------------------------------------------------+
-| runtime                  | The running device, one of CPU, GPU or DSP                                             |
-+--------------------------+----------------------------------------------------------------------------------------+
-| limit_opencl_kernel_time | Whether splitting the OpenCL kernel within 1 ms to keep UI responsiveness, default to 0|
-+--------------------------+----------------------------------------------------------------------------------------+
-| dsp_mode                 | Control the DSP precision and performance, default to 0 usually works for most cases   |
-+--------------------------+----------------------------------------------------------------------------------------+
-| obfuscate                | Whether to obfuscate the model operator name, default to 0                             |
-+--------------------------+----------------------------------------------------------------------------------------+
-| fast_conv                | Whether to enable Winograd convolution, **will increase memory consumption**           |
-+--------------------------+----------------------------------------------------------------------------------------+
-| input_files              | Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used |
-+--------------------------+----------------------------------------------------------------------------------------+
+.. list-table::
+    :widths: auto
+    :header-rows: 1
+    :align: left
+
+    * - Configuration key
+      - Description
+    * - target_abis
+      - The target ABI to build, can be one or more of 'host', 'armeabi-v7a' or 'arm64-v8a'
+    * - embed_model_data
+      - Whether embedding model weights as the code, default to 1
+    * - platform
+      - The source framework, tensorflow or caffe
+    * - model_file_path
+      - The path of the model file, can be local or remote
+    * - weight_file_path
+      - The path of the model weights file, used by Caffe model
+    * - model_sha256_checksum
+      - The SHA256 checksum of the model file
+    * - weight_sha256_checksum
+      - The SHA256 checksum of the weight file, used by Caffe model
+    * - input_nodes
+      - The input node names, one or more strings
+    * - output_nodes
+      - The output node names, one or more strings
+    * - input_shapes
+      - The shapes of the input nodes, in NHWC order
+    * - output_shapes
+      - The shapes of the output nodes, in NHWC order
+    * - runtime
+      - The running device, one of CPU, GPU or DSP
+    * - limit_opencl_kernel_time
+      - Whether splitting the OpenCL kernel within 1 ms to keep UI responsiveness, default to 0
+    * - dsp_mode
+      - Control the DSP precision and performance, default to 0 usually works for most cases
+    * - obfuscate
+      - Whether to obfuscate the model operator name, default to 0
+    * - fast_conv
+      - Whether to enable Winograd convolution, **will increase memory consumption**
+    * - input_files
+      - Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used

docs/getting_started/docker.md

+Docker Images
+=============
+
+* Login in [Xiaomi Docker Registry](http://docs.api.xiaomi.net/docker-registry/)
+
+```
+docker login cr.d.xiaomi.net
+```
+
+* Build with `Dockerfile`
+
+```
+docker build -t cr.d.xiaomi.net/mace/mace-dev
+```
+
+* Pull image from docker registry
+
+```
+docker pull cr.d.xiaomi.net/mace/mace-dev
+```
+
+* Create container
+
+```
+# Set 'host' network to use ADB
+docker run -it --rm -v /local/path:/container/path --net=host cr.d.xiaomi.net/mace/mace-dev /bin/bash
+```

docs/getting_started/docker.rst

-Docker Images
-=============
-
--  Login in `Xiaomi Docker
-   Registry <http://docs.api.xiaomi.net/docker-registry/>`__
-
-``docker login cr.d.xiaomi.net``
-
--  Build with ``Dockerfile``
-
-``docker build -t cr.d.xiaomi.net/mace/mace-dev .``
-
--  Pull image from docker registry
-
-``docker pull cr.d.xiaomi.net/mace/mace-dev``
-
--  Create container
-
-``# Set 'host' network to use ADB   docker run -it --rm -v /local/path:/container/path --net=host cr.d.xiaomi.net/mace/mace-dev /bin/bash``

docs/getting_started/models/demo_app_models.yaml

+# 配置文件名会被用作生成库的名称：libmace-${filename}.a
+target_abis: [armeabi-v7a, arm64-v8a]
+# 具体机型的soc编号，可以使用`adb shell getprop | grep ro.board.platform | cut -d [ -f3 | cut -d ] -f1`获取
+target_socs: [msm8998]
+embed_model_data: 1
+models: # 一个配置文件可以包含多个模型的配置信息，最终生成的库中包含多个模型
+  first_net: # 模型的标签，在调度模型的时候，会用这个变量
+    platform: tensorflow
+    model_file_path: path/to/model64.pb # also support http:// and https://
+    model_sha256_checksum: 7f7462333406e7dea87222737590ebb7d94490194d2f21a7d72bafa87e64e9f9
+    input_nodes: input_node
+    output_nodes: output_node
+    input_shapes: 1,64,64,3
+    output_shapes: 1,64,64,2
+    runtime: gpu
+    limit_opencl_kernel_time: 0
+    dsp_mode: 0
+    obfuscate: 1
+    fast_conv: 0
+    input_files:
+      - path/to/input_files # support http://
+  second_net:
+    platform: caffe
+    model_file_path: path/to/model.prototxt
+    weight_file_path: path/to/weight.caffemodel
+    model_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
+    weight_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
+    input_nodes:
+      - input_node0
+      - input_node1
+    output_nodes:
+      - output_node0
+      - output_node1
+    input_shapes:
+      - 1,256,256,3
+      - 1,128,128,3
+    output_shapes:
+      - 1,256,256,2
+      - 1,1,1,2
+    runtime: cpu
+    limit_opencl_kernel_time: 1
+    dsp_mode: 0
+    obfuscate: 1
+    fast_conv: 0
+    input_files:
+      - path/to/input_files # support http://

docs/getting_started/op_lists.rst

 Operator lists
 ==============
 
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| Operator                         | Android NN   | Status | Remark                                                |
-+==================================+==============+========+=======================================================+
-| ADD                              | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| AVERAGE\_POOL\_2D                | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| BATCH\_NORM                      |              | Y      | Fusion with activation is supported                   |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| BIAS\_ADD                        |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| CHANNEL\_SHUFFLE                 |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| CONCATENATION                    | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| CONV\_2D                         | Y            | Y      | Fusion with BN and activation layer is supported      |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| DEPTHWISE\_CONV\_2D              | Y            | Y      | Only multiplier = 1 is supported; Fusion is supported |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| DEPTH\_TO\_SPACE                 | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| DEQUANTIZE                       | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| EMBEDDING\_LOOKUP                | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| FLOOR                            | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| FULLY\_CONNECTED                 | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| GROUP\_CONV\_2D                  |              |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| HASHTABLE\_LOOKUP                | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| L2\_NORMALIZATION                | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| L2\_POOL\_2D                     | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| LOCAL\_RESPONSE\_NORMALIZATION   | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| LOGISTIC                         | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| LSH\_PROJECTION                  | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| LSTM                             | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| MATMUL                           |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| MAX\_POOL\_2D                    | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| MUL                              | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| PSROI\_ALIGN                     |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| PRELU                            |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RELU                             | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RELU1                            | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RELU6                            | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RELUX                            |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RESHAPE                          | Y            | Y      | Limited support                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RESIZE\_BILINEAR                 | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RNN                              | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| RPN\_PROPOSAL\_LAYER             |              | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| SOFTMAX                          | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| SPACE\_TO\_DEPTH                 | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| SVDF                             | Y            |        |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
-| TANH                             | Y            | Y      |                                                       |
-+----------------------------------+--------------+--------+-------------------------------------------------------+
+.. Please keep in chronological order when editing
+.. csv-table::
+    :widths: auto
+    :header: "Operator","Android NN","Supported","Remark"
+
+    "ADD","Y","Y",""
+    "AVERAGE_POOL_2D","Y","Y",""
+    "BATCH_NORM","","Y","Fusion with activation is supported"
+    "BIAS_ADD","","Y",""
+    "CHANNEL_SHUFFLE","","Y",""
+    "CONCATENATION","Y","Y",""
+    "CONV_2D","Y","Y","Fusion with BN and activation layer is supported"
+    "DEPTHWISE_CONV_2D","Y","Y","Only multiplier = 1 is supported; Fusion is supported"
+    "DEPTH_TO_SPACE","Y","Y",""
+    "DEQUANTIZE","Y","",""
+    "EMBEDDING_LOOKUP","Y","",""
+    "FLOOR","Y","",""
+    "FULLY_CONNECTED","Y","Y",""
+    "GROUP_CONV_2D","","",""
+    "HASHTABLE_LOOKUP","Y","",""
+    "L2_NORMALIZATION","Y","",""
+    "L2_POOL_2D","Y","",""
+    "LOCAL_RESPONSE_NORMALIZATION","Y","Y",""
+    "LOGISTIC","Y","Y",""
+    "LSH_PROJECTION","Y","",""
+    "LSTM","Y","",""
+    "MATMUL","","Y",""
+    "MAX_POOL_2D","Y","Y",""
+    "MUL","Y","",""
+    "PSROI_ALIGN","","Y",""
+    "PRELU","","Y",""
+    "RELU","Y","Y",""
+    "RELU1","Y","Y",""
+    "RELU6","Y","Y",""
+    "RELUX","","Y",""
+    "RESHAPE","Y","Y","Limited support"
+    "RESIZE_BILINEAR","Y","Y",""
+    "RNN","Y","",""
+    "RPN_PROPOSAL_LAYER","","Y",""
+    "SOFTMAX","Y","Y",""
+    "SPACE_TO_DEPTH","Y","Y",""
+    "SVDF","Y","",""
+    "TANH","Y","Y",""

docs/index.rst

@@ -11,8 +11,8 @@ The main documentation is organized into the following sections:
 
    getting_started/introduction
    getting_started/create_a_model_deployment
-   getting_started/how_to_build
    getting_started/docker
+   getting_started/how_to_build
    getting_started/op_lists
 
 .. toctree::