Merge pull request #34 from majianjia/dev update documentation

Dev

Author: majianjia

LICENSE

@@ -1,181 +1,5 @@
-======================================================================
-Nerual Netowrk on Microcontroller (NNoM) License
-======================================================================
-                  GNU LESSER GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
 
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-
-  This version of the GNU Lesser General Public License incorporates
-the terms and conditions of version 3 of the GNU General Public
-License, supplemented by the additional permissions listed below.
-
-  0. Additional Definitions.
-
-  As used herein, "this License" refers to version 3 of the GNU Lesser
-General Public License, and the "GNU GPL" refers to version 3 of the GNU
-General Public License.
-
-  "The Library" refers to a covered work governed by this License,
-other than an Application or a Combined Work as defined below.
-
-  An "Application" is any work that makes use of an interface provided
-by the Library, but which is not otherwise based on the Library.
-Defining a subclass of a class defined by the Library is deemed a mode
-of using an interface provided by the Library.
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-  A "Combined Work" is a work produced by combining or linking an
-Application with the Library.  The particular version of the Library
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-Corresponding Source for the Combined Work, excluding any source code
-for portions of the Combined Work that, considered in isolation, are
-based on the Application, and not on the Linked Version.
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-object code and/or source code for the Application, including any data
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-Application, but excluding the System Libraries of the Combined Work.
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-without being bound by section 3 of the GNU GPL.
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-facility refers to a function or data to be supplied by an Application
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-facility is invoked), then you may convey a copy of the modified
-version:
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-   ensure that, in the event an Application does not supply the
-   function or data, the facility still operates, and performs
-   whatever part of its purpose remains meaningful, or
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-   b) under the GNU GPL, with none of the additional permissions of
-   this License applicable to that copy.
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-  3. Object Code Incorporating Material from Library Header Files.
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-   the Library is used in it and that the Library and its use are
-   covered by this License.
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-   document.
-
-   c) For a Combined Work that displays copyright notices during
-   execution, include the copyright notice for the Library among
-   these notices, as well as a reference directing the user to the
-   copies of the GNU GPL and this license document.
-
-   d) Do one of the following:
-
-       0) Convey the Minimal Corresponding Source under the terms of this
-       License, and the Corresponding Application Code in a form
-       suitable for, and under terms that permit, the user to
-       recombine or relink the Application with a modified version of
-       the Linked Version to produce a modified Combined Work, in the
-       manner specified by section 6 of the GNU GPL for conveying
-       Corresponding Source.
-
-       1) Use a suitable shared library mechanism for linking with the
-       Library.  A suitable mechanism is one that (a) uses at run time
-       a copy of the Library already present on the user's computer
-       system, and (b) will operate properly with a modified version
-       of the Library that is interface-compatible with the Linked
-       Version.
-
-   e) Provide Installation Information, but only if you would otherwise
-   be required to provide such information under section 6 of the
-   GNU GPL, and only to the extent that such information is
-   necessary to install and execute a modified version of the
-   Combined Work produced by recombining or relinking the
-   Application with a modified version of the Linked Version. (If
-   you use option 4d0, the Installation Information must accompany
-   the Minimal Corresponding Source and Corresponding Application
-   Code. If you use option 4d1, you must provide the Installation
-   Information in the manner specified by section 6 of the GNU GPL
-   for conveying Corresponding Source.)
-
-  5. Combined Libraries.
-
-  You may place library facilities that are a work based on the
-Library side by side in a single library together with other library
-facilities that are not Applications and are not covered by this
-License, and convey such a combined library under terms of your
-choice, if you do both of the following:
-
-   a) Accompany the combined library with a copy of the same work based
-   on the Library, uncombined with any other library facilities,
-   conveyed under the terms of this License.
-
-   b) Give prominent notice with the combined library that part of it
-   is a work based on the Library, and explaining where to find the
-   accompanying uncombined form of the same work.
-
-  6. Revised Versions of the GNU Lesser General Public License.
-
-  The Free Software Foundation may publish revised and/or new versions
-of the GNU Lesser General Public License from time to time. Such new
-versions will be similar in spirit to the present version, but may
-differ in detail to address new problems or concerns.
-
-  Each version is given a distinguishing version number. If the
-Library as you received it specifies that a certain numbered version
-of the GNU Lesser General Public License "or any later version"
-applies to it, you have the option of following the terms and
-conditions either of that published version or of any later version
-published by the Free Software Foundation. If the Library as you
-received it does not specify a version number of the GNU Lesser
-General Public License, you may choose any version of the GNU Lesser
-General Public License ever published by the Free Software Foundation.
-
-  If the Library as you received it specifies that a proxy can decide
-whether future versions of the GNU Lesser General Public License shall
-apply, that proxy's public statement of acceptance of any version is
-permanent authorization for you to choose that version for the
-Library.
-
-End of license for NNoM.
-==========================================================================
-
-==========================================================================
-A part of codes in src/nnom_local.c & src/nnom_local.h was modified from
-CMSIS lib https://github.com/ARM-software/CMSIS_5 which is relased under 
-the license below:
-
-    Apache License
+                                 Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
 
@@ -355,15 +179,15 @@ the license below:
    APPENDIX: How to apply the Apache License to your work.
 
       To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "{}"
+      boilerplate notice, with the fields enclosed by brackets "[]"
       replaced with your own identifying information. (Don't include
       the brackets!)  The text should be enclosed in the appropriate
       comment syntax for the file format. We also recommend that a
       file or class name and description of purpose be included on the
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright {yyyy} {name of copyright owner}
+   Copyright [yyyy] [name of copyright owner]
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
@@ -375,4 +199,5 @@ the license below:
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
-limitations under the License.
+   limitations under the License.
+   

README.md

@@ -12,6 +12,11 @@ NNoM is a higher-level layer-based Neural Network library specifically for micro
 - High-performance backend selections.
 - Onboard (MCU) evaluation tools; Runtime analysis, Top-k, Confusion matrix... 
 
+**Licenses**
+
+NNoM is released under Apache License 2.0 since nnom-V0.2.0. 
+License and copyright information can be found within the code.
+
 ## Why NNoM?
 The aims of NNoM is to provide a light-weight, user-friendly and flexible interface for fast deploying.
 
@@ -27,14 +32,12 @@ After 2014, the development of Neural Networks are more focus on structure optim
 However, the available NN libs for MCU are too low-level which make it sooooo difficult to use with these complex strucures. 
 
 Therefore, we build NNoM to help embedded developers to faster and simpler deploying NN model directly to MCU. 
-> NNoM will manage the strucutre, memory and everything else for developer. All you need is feeding your measurements then get the results. 
+> NNoM will manage the strucutre, memory and everything else for the developer. All you need to do is feeding your new measurements and getting the results. 
 
 **NNoM is now working closely with Keras (You can easily learn [**Keras**](https://keras.io/) in 30 seconds!).**
 There is no need to learn TensorFlow/Lite or other libs.  
 
 
----
-
 ## Documentations
 API manuals are available in **[API Manual](https://majianjia.github.io/nnom/)**
 
@@ -50,8 +53,6 @@ API manuals are available in **[API Manual](https://majianjia.github.io/nnom/)**
 
 [RT-Thread-MNIST example (中文例子)](docs/example_mnist_simple_cn.md)
 
----
-
 ## Examples
 
 **Documented examples**
@@ -64,7 +65,6 @@ API manuals are available in **[API Manual](https://majianjia.github.io/nnom/)**
 
 Please check [examples](https://github.com/majianjia/nnom/tree/master/examples) for more applications. 
 
----
 
 
 ## Available Operations
@@ -116,21 +116,16 @@ Activation can be used by itself as layer, or can be attached to the previous la
 | Substraction  | Beta|Sub()||
 | Dot  | Under Dev. |||
 
-
-
 ## Dependencies
 
 NNoM now use the local pure C backend implementation by default. Thus, there is no special dependency needed. 
 
 
-
 ## Optimization
 You can select [CMSIS-NN/DSP](https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN) as the backend for about 5x performance with ARM-Cortex-M4/7/33/35P. 
 
 Check [Porting and optimising Guide](docs/Porting_and_Optimisation_Guide.md) for detail. 
 
-
-
 ## Contacts
 Jianjia Ma
 
@@ -139,4 +134,3 @@ [email protected] or [email protected]
 ## Citation Required
 Please contact us using above details. 
 
-

docs/index.md

@@ -12,6 +12,11 @@ NNoM is a higher-level layer-based Neural Network library specifically for micro
 - High-performance backend selections.
 - Onboard (MCU) evaluation tools; Runtime analysis, Top-k, Confusion matrix... 
 
+**Licenses**
+
+NNoM is released under Apache License 2.0 since nnom-V0.2.0. 
+License and copyright information can be found within the code.
+
 ---
 
 ## Why NNoM?

examples/keyword_spotting/README.md

@@ -0,0 +1,5 @@
+## Keyword Spotting example
+
+> This example is under development, the codes are supposed to be changed during the period.
+
+Documentation under writing. 

examples/keyword_spotting/mcu/main.c

@@ -1,7 +1,7 @@
 /*
- * Copyright (c) 2006-2018, RT-Thread Development Team
+ * Copyright (c) 2018-2019, Jianjia Ma
  *
- * SPDX-License-Identifier: LGPL-3.0
+ * SPDX-License-Identifier: Apache-2.0
  *
  * Change Logs:
  * Date           Author       Notes

examples/keyword_spotting/mcu/mfcc.c

@@ -14,6 +14,9 @@
  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
+ *
+ * Modified by Jianjia Ma for C implementation.
+ *
  */
 
 /*

examples/keyword_spotting/mcu/mfcc.h

@@ -14,6 +14,9 @@
  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
+ *
+ * Modified by Jianjia Ma for C implementation
+ *
  */
 
 #ifndef __KWS_MFCC_H__

examples/keyword_spotting/model/kws.py

@@ -2,16 +2,19 @@
 
 import matplotlib.pyplot as plt
 from matplotlib import cm
-import os
-
 import keras
 from keras.models import Sequential, load_model
 from keras.preprocessing.image import ImageDataGenerator
 from keras.models import Model
 from keras.layers import *
 from keras.callbacks import ModelCheckpoint
-from nnom_utils import *
 
+import os
+import sys
+nnscript = os.path.abspath('../../scripts')
+sys.path.append(nnscript)
+
+from nnom_utils import *
 from mfcc import *
 
 model_path = 'model.h5'

examples/mnist-cnn/README.md

@@ -0,0 +1,3 @@
+This is a very simple convolution example following the Keras tutorial:
+
+https://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/

examples/mnist-cnn/SConstruct

@@ -0,0 +1,40 @@
+
+import os
+
+if(not os.path.exists('CMSIS_5')):
+    os.system('git clone https://github.com/ARM-software/CMSIS_5.git')
+
+ROOT=os.path.abspath('../..')
+
+env = Environment()
+env.Replace(
+    ARCOMSTR = 'AR $SOURCE',
+    ASCOMSTR = 'AS $SOURCE',
+    ASPPCOMSTR = 'AS $SOURCE',
+    CCCOMSTR = 'CC $SOURCE',
+    CXXCOMSTR = 'CXX $SOURCE',
+    LINKCOMSTR = 'LINK $TARGET'
+)
+
+objs = []
+
+objs += Glob('CMSIS_5/CMSIS/NN/Source/*/*.c')
+objs += Glob('CMSIS_5/CMSIS/DSP/Source/BasicMathFunctions/arm_*.c')
+objs += Glob('mcu/main_cnn.c')
+
+
+env.Append(CPPPATH=['CMSIS_5/CMSIS/NN/Include',
+                      'CMSIS_5/CMSIS/DSP/Include',
+                      'CMSIS_5/CMSIS/Core/Include'])
+env.Append(CPPDEFINES=['__ARM_ARCH_8M_BASE__'])
+env.Append(CCFLAGS=['-g','-O0','-std=gnu99'])
+
+objs +=Glob('%s/src/*.c'%(ROOT))
+env.Append(CPPPATH=['%s/inc'%(ROOT),'%s/port'%(ROOT)])
+env.Append(CPPDEFINES=['USE_NNOM_OUTPUT_SAVE'])
+
+if(os.getenv('USE_CMSIS_NN') == 'YES'):
+	env.Append(CPPDEFINES=['NNOM_USING_CMSIS_NN'])
+
+env.Program('mnist',objs)
+