update README.md.

README.md

@@ -1,30 +1,21 @@
-![Image text](https://github.com/inspur-inna/inspur-inna/blob/master/Image/inspur.png)
+![Image text](https://gitee.com/inspur-inna/inspur-inna/raw/master/Image/inspur.png)
 
-# 基于FPGA的CNN自适应映射技术---inspur-inna
-
-基于宏指令的Look-Aside Acceleration框架：
-
-- 一键式快速部署
-- 软硬件协同优化
-- 支持多种卷积
-- 执行过程无需主机干预
+# 基于FPGA的CNN自适应映射技术——inna1.0
 
+基于FPGA板卡设计深度学习加速器并进行优化，在整体性能和功耗方面拟达到业界领先水平，映射技术采用宏指令的Look-Aside Acceleration框架，实现了一键式快速部署、软硬件协同优化、支持多种卷积、执行过程无需主机干预。本项目为映射技术的软件端，拟实现CNN映射编译器和CNN量化器，首先由TensorFlow产生的模型文件解析产生CNN的计算图模型，CNN映射编译器会根据解析的计算图和现有的CNN加速库单元，选择相应的CNN库单元，生成相应的硬件结构和相应的调度器的配置参数，以达到计算、片上存储、片上带宽和片外带宽的均衡，从而达到最优的计算性能；CNN量化器可根据模型的权重文件，对各层数据进行8位定点量化，以便于FPGA的DSP计算，从而在保证精度的前提下降低存储开销，提高处理速度，降低功耗。
 
 
 ## Install
 
-### TVM source code install
-LLVM install in Ubuntu
+### inna install
+TVM need LLVM，LLVM install in Ubuntu（other system require source code compilation）
 ```bash
 apt search llvm
 apt install llvm-6.0
 apt install clang-6.0
 ```
 
-TVM Install Source<https://tvm.apache.org/docs/install/from_source.html>
-
-### inna install
-Install miniconda for python=3.6
+Install miniconda for python=3.6，install_inna.sh include TVM install script（refer to TVM <https://tvm.apache.org/docs/install/from_source.html>）
 ```bash
 conda create -n inna python=3.6 ipykernel -y
 conda activate inna

docs/Makefile

@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd

docs/source/api.rst

@@ -0,0 +1,6 @@
+Python API
+==========
+
+.. toctree::
+   
+   inna

docs/source/index.rst

@@ -0,0 +1,21 @@
+.. inna documentation master file, created by
+   sphinx-quickstart on Mon Apr  8 10:09:32 2019.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to INNA's documentation!
+================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   install
+   api
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/source/inna.compiler.rst

@@ -0,0 +1,9 @@
+inna.compiler
+==============
+
+.. autofunction:: inna.compiler.create
+
+.. autoclass:: inna.compiler.INNACompiler
+   :members:
+
+   .. automethod:: __init__

docs/source/inna.quantizer.rst

@@ -0,0 +1,5 @@
+inna.quantizer
+===============
+
+.. automodule:: inna.quantizer
+    :members:

docs/source/inna.rst

@@ -0,0 +1,8 @@
+inna
+=====
+
+.. toctree::
+
+   inna.compiler
+   inna.quantizer
+   inna.runtime

docs/source/inna.runtime.rst

@@ -0,0 +1,28 @@
+inna.runtime
+=============
+
+.. autofunction:: inna.runtime.create
+
+.. autoclass:: inna.runtime.INNARuntime
+
+   .. automethod:: __init__
+   .. automethod:: run
+
+   .. automethod:: ReloadNNModel
+   .. automethod:: SetExtendHWBatch
+   .. automethod:: SetInputFeatures
+   .. automethod:: Run
+   .. automethod:: Wait
+
+   .. automethod:: GetOutputFeatures
+   .. automethod:: WriteRegister
+   .. automethod:: ReadRegisterU8
+   .. automethod:: ReadRegisterU16
+   .. automethod:: ReadRegisterU32
+   .. automethod:: ReadRegisterU64
+   .. automethod:: WriteDMA
+   .. automethod:: ReadDMA
+   .. automethod:: WaitPcieInterupt
+
+.. autofunction:: inna.runtime.check_equal
+

docs/source/install.rst

@@ -0,0 +1,19 @@
+Installation
+============
+
+INNA use NNVM as frontend compiler. To get started, install tvm by
+reference `TVM Install from
+Source <https://docs.tvm.ai/install/from_source.html#install-from-source>`__.
+
+After install tvm, clone INNA repo from git server.
+
+.. code:: bash
+
+   git clone git@100.2.95.100:fanbaoyu/inna.git
+
+Install INNA throw ``pip install``.
+
+.. code:: bash
+
+   pip install ./inna
+

inna.egg-info/PKG-INFO

@@ -0,0 +1,10 @@
+Metadata-Version: 1.0
+Name: inna
+Version: 0.0.1
+Summary: Inspur Neural Network Accelerater
+Home-page: UNKNOWN
+Author: Fan Baoyu
+Author-email: fanbaoyu@inspur.com
+License: UNKNOWN
+Description: UNKNOWN
+Platform: UNKNOWN

inna.egg-info/SOURCES.txt

@@ -0,0 +1,30 @@
+setup.py
+inna/__init__.py
+inna/config.ini
+inna.egg-info/PKG-INFO
+inna.egg-info/SOURCES.txt
+inna.egg-info/dependency_links.txt
+inna.egg-info/not-zip-safe
+inna.egg-info/requires.txt
+inna.egg-info/top_level.txt
+inna/compiler/__init__.py
+inna/compiler/assembler.py
+inna/compiler/compiler.py
+inna/compiler/converter.py
+inna/compiler/frontend.py
+inna/compiler/scheduler.py
+inna/compiler/test.py
+inna/compiler/applications/__init__.py
+inna/compiler/applications/resnet/__init__.py
+inna/compiler/applications/resnet/keras.py
+inna/compiler/applications/resnet/mxnet.py
+inna/compiler/applications/resnet/onnx.py
+inna/compiler/applications/resnet/tensorflow.py
+inna/quantizer/__init__.py
+inna/quantizer/quantize.py
+inna/quantizer/quantize_base.py
+inna/quantizer/quantize_caffe.py
+inna/quantizer/quantize_tf.py
+inna/runtime/__init__.py
+inna/runtime/runtime.cc
+inna/runtime/runtime.py

inna.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

inna.egg-info/not-zip-safe

@@ -0,0 +1 @@
+

inna.egg-info/requires.txt

@@ -0,0 +1,2 @@
+pybind11>=2.2
+numpy>=1.16

inna.egg-info/top_level.txt

@@ -0,0 +1 @@
+inna

inna/__init__.py

@@ -0,0 +1,5 @@
+from __future__ import absolute_import
+
+from . import compiler
+from . import quantizer
+from . import runtime

inna/compiler/__init__.py

@@ -0,0 +1,3 @@
+from __future__ import absolute_import
+
+from .compiler import create, INNACompiler

inna/compiler/applications/__init__.py

@@ -0,0 +1,3 @@
+from __future__ import absolute_import
+
+from . import resnet

inna/compiler/applications/resnet/__init__.py

@@ -0,0 +1,6 @@
+from __future__ import absolute_import
+
+from . import tensorflow
+from . import keras
+from . import mxnet
+from . import onnx

inna/compiler/applications/resnet/keras.py

@@ -0,0 +1,17 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import keras
+
+
+def resnet_v1_50():
+    graph = keras.applications.resnet50.ResNet50(include_top=True, weights=None,
+                                                          input_shape=(224, 224, 3), classes=1000)
+    graph.load_weights('../models/keras/resnet/resnet50_weights.h5')
+    shape_dict = {
+        'input_1': (1, 3, 224, 224),
+    }
+    layout = 'NCHW'
+
+    return graph, shape_dict, layout

inna/compiler/applications/resnet/mxnet.py

@@ -0,0 +1,14 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from mxnet.gluon.model_zoo.vision import get_model
+
+
+def resnet_v1_50():
+    graph = get_model('resnet50_v1', pretrained=True)
+    shape_dict = {
+        'data': (1, 3, 224, 224),
+    }
+    layout = 'NCHW'
+    return graph, shape_dict, layout

inna/compiler/applications/resnet/onnx.py

@@ -0,0 +1,15 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import onnx
+
+
+def resnet_v1_50():
+    graph = onnx.load('../models/onnx/resnet/restnet50_v1.1.onnx')
+    shape_dict = {
+        'gpu_0/data_0': (1, 3, 224, 224),
+    }
+    layout = 'NCHW'
+
+    return graph, shape_dict, layout