Merge from branch 'develop'

.gitignore

@@ -258,11 +258,13 @@ paket-files/
 # Python Tools for Visual Studio (PTVS)
 __pycache__/
 *.pyc
-src/**/*.txt
 
 # vscode
 .vscode/
 
 *.diagsession
 /src/nntest
 /src/NnCase.Cli/Properties/launchSettings.json
+
+out/
+out_2/

.gitmodules

@@ -0,0 +1,9 @@
+[submodule "third_party/clipp"]
+	path = third_party/clipp
+	url = https://github.com/muellan/clipp.git
+[submodule "third_party/xtl"]
+	path = third_party/xtl
+	url = https://github.com/QuantStack/xtl.git
+[submodule "third_party/xtensor"]
+	path = third_party/xtensor
+	url = https://github.com/QuantStack/xtensor.git

CMakeLists.txt

@@ -0,0 +1,52 @@
+cmake_minimum_required(VERSION 3.8)
+project("nncase")
+
+# Download automatically, you can also just copy the conan.cmake file
+if(NOT EXISTS "${CMAKE_BINARY_DIR}/conan.cmake")
+   message(STATUS "Downloading conan.cmake from https://github.com/conan-io/cmake-conan")
+   file(DOWNLOAD "https://github.com/conan-io/cmake-conan/raw/v0.14/conan.cmake"
+                 "${CMAKE_BINARY_DIR}/conan.cmake")
+endif()
+
+include(${CMAKE_BINARY_DIR}/conan.cmake)
+
+if (NOT NNCASE_TARGET)
+    message(FATAL_ERROR "Please define NNCASE_TARGET")
+else()
+    if (WIN32)
+        add_definitions(/DNNCASE_TARGET=${NNCASE_TARGET})
+    else()
+        add_definitions(-DNNCASE_TARGET=${NNCASE_TARGET})
+    endif()
+endif()
+
+conan_check()
+conan_add_remote(NAME bincrafts URL https://api.bintray.com/conan/bincrafters/public-conan)
+conan_cmake_run(CONANFILE conanfile.txt
+                BASIC_SETUP CMAKE_TARGETS
+                BUILD missing)
+include(ExternalProject)
+
+set(THIRD_PARTY ${CMAKE_CURRENT_LIST_DIR}/third_party)
+
+if (MSVC_VERSION GREATER_EQUAL "1900")
+    include(CheckCXXCompilerFlag)
+    CHECK_CXX_COMPILER_FLAG("/std:c++latest" _cpp_latest_flag_supported)
+    if (_cpp_latest_flag_supported)
+        add_compile_options("/std:c++latest")
+        add_definitions(/D_SILENCE_ALL_CXX17_DEPRECATION_WARNINGS)
+    endif()
+else()
+    add_compile_options(-Wno-multichar -std=c++17)
+endif()
+
+add_subdirectory(src/cli)
+add_subdirectory(src/common)
+add_subdirectory(src/ir)
+add_subdirectory(src/transforms)
+add_subdirectory(src/importer)
+add_subdirectory(src/data)
+add_subdirectory(src/scheduler)
+add_subdirectory(src/evaluator)
+add_subdirectory(src/codegen)
+add_subdirectory(src/runtime)

CMakeSettings.json

@@ -0,0 +1,38 @@
+{
+  "configurations": [
+    {
+      "name": "x64-Debug",
+      "generator": "Ninja",
+      "configurationType": "Debug",
+      "inheritEnvironments": [ "msvc_x64_x64" ],
+      "buildRoot": "${projectDir}\\out\\build\\${name}",
+      "installRoot": "${projectDir}\\out\\install\\${name}",
+      "cmakeCommandArgs": "",
+      "buildCommandArgs": "-v",
+      "ctestCommandArgs": "",
+      "variables": [
+        {
+          "name": "NNCASE_TARGET",
+          "value": "k210"
+        }
+      ]
+    },
+    {
+      "name": "x64-Release",
+      "generator": "Ninja",
+      "configurationType": "RelWithDebInfo",
+      "buildRoot": "${projectDir}\\out\\build\\${name}",
+      "installRoot": "${projectDir}\\out\\install\\${name}",
+      "cmakeCommandArgs": "",
+      "buildCommandArgs": "-v",
+      "ctestCommandArgs": "",
+      "inheritEnvironments": [ "msvc_x64_x64" ],
+      "variables": [
+        {
+          "name": "NNCASE_TARGET",
+          "value": "k210"
+        }
+      ]
+    }
+  ]
+}

README.md

@@ -1,99 +1,140 @@
-nncase
-=========================================
-[![Build status](https://ci.appveyor.com/api/projects/status/cybsf4av9e2ms447/branch/master?svg=true)](https://ci.appveyor.com/project/sunnycase/nncase/branch/master)
-
-`nncase` is a cross-platform neural network optimization toolkit for fast inference.
-
-## Usage
-Download prebuilt binaries from [Release](https://github.com/kendryte/nncase/releases).
-
-`ncc -i <input format> -o <output format> [--dataset <dataset path>] [--postprocess <dataset postprocess>] [--weights-bits <weights quantization bits>] <input path> <output path>`
-
-- `-i` Input format
-
-| value | description |
-|-------|------------------ |
-|tflite|`.tflite` TFLite model
-|paddle|`__model__` PaddlePaddle model
-|caffe|`.caffemodel` Caffe model
-|k210model|`.kmodel` K210 model (Only supported in inference mode)
-
-- `-o` Output format
+<div align="center">
+<img src="docs/logo.png" width="400" alt="nncase" />
+</div>
 
-| value | description |
-|-------|------------------ |
-|k210model|`.kmodel` K210 model
-|tf|`.pb` TensorFlow model
-|tflite|`.tflite` TFLite model
-|inference|`.bin` Model's raw output (Only support k210model input)
-
-- `--inference-type` Inference type
-
-| value | description |
-|-------|------------------ |
-|uint8| Use quantized kernels (default)
-|float| Use float kernels
-
-- `--dataset` Dataset path, **required** when the output format is `inference` or `k210model` with inference type equals `uint8`.
+[![License](https://img.shields.io/badge/license-Apache%202-blue)](https://raw.githubusercontent.com/kendryte/nncase/master/LICENSE) 
+[![Build status](https://ci.appveyor.com/api/projects/status/cybsf4av9e2ms447/branch/master?svg=true)](https://ci.appveyor.com/project/sunnycase/nncase/branch/master)
 
-- `--postprocess` Dataset postprocess method
+`nncase` is a neural network compiler for AI accelerators.
 
-| value | description |
-|-------|------------------ |
-|0to1|normalize images to [0, 1]
-|n1to1|normalize images to [-1, 1]
+`nncase` 是一个为 AI 加速器设计的神经网络编译器。
 
-- `--weights-bits` Weights quantization bits
+## Install
+Download prebuilt binaries from [Release](https://github.com/kendryte/nncase/releases).
 
-| value | description |
-|-------|------------------ |
-|8|8bit quantization [0, 255]
-|16|16bit quantization [0, 65535]
+## 安装
+下载预编译的二进制文件 [Release](https://github.com/kendryte/nncase/releases)。
 
-- `--float-fc` Use float fullyconnected kernels.
+---
 
-- `--channelwise-output` Use channelwise quantization for output layers.
+### Support commonly used CNN networks
+### 支持常用的 CNN 网络
 
-## Examples
-- Convert TFLite model to K210 model.
+- MobileNetV1/V2
+- YOLOV1 YOLOV3
 
-  `ncc -i tflite -o k210model --dataset ./images ./mbnetv1.tflite ./mbnetv1.kmodel`
+## Features
 
-- Convert PaddlePaddle model to TensorFlow model.
+- Supports multiple inputs and outputs and multi-branch structure
+- Static memory allocation, no heap memory acquired
+- Operators fusion and optimizations
+- Support float and quantized uint8 inference
+- Support post quantization from float model with calibration dataset
+- Flat model with zero copy loading
 
-  `ncc -i paddle -o tf ./MobileNetV1_pretrained ./mbnetv1.pb`
+## 功能
 
-- Inference K210 model and get output binaries.
+- 支持多输入输出网络，支持多分支结构
+- 静态内存分配，不需要堆内存
+- 算子合并和优化
+- 支持 float 和量化 uint8 推理
+- 支持训练后量化，使用浮点模型和量化校准集
+- 平坦模型，支持零拷贝加载
 
-  `ncc -i k210model -o inference --dataset ./images ./mbnetv1.kmodel ./output`
+## Usage
+## 使用方法
 
-- Tutorials
-  - 20 classes object detection
-  - Iris flowers classification
+[Usage 使用方法](USAGE.md)
 
-  See https://github.com/kendryte/nncase/tree/master/examples
+[Examples 例子](./examples)
 
-## Supported layers
+## Supported operators
+## 支持的算子
 
-| layer | parameters |
+| Operator | Is Supported |
 |-------|------------------ |
-| Conv2d | kernel={3x3,1x1} stride={1,2} padding=same *|
-| DepthwiseConv2d | kernel={3x3,1x1} stride={1,2} padding=same *|
-| FullyConnected | |
-| Add | |
-| MaxPool2d | |
-| AveragePool2d | |
-| GlobalAveragePool2d | |
-| BatchNormalization | |
-| BiasAdd | |
-| Relu | |
-| Relu6 | |
-| LeakyRelu | |
-| Concatenation | |
-| L2Normalization | |
-| Sigmoid | |
-| Softmax | |
-| Flatten | |
-| ResizeNearestNeighbor | |
-
-\* When using TensorFlow Conv2d/DepthwiseConv2d kernel=3x3 stride=2 padding=same, you must first use tf.pad([[0,0],[1,1],[1,1],[0,0]]) to pad the input and then use Conv2d/DepthwiseConv2d with `valid` padding.
+| Add |✅|
+| ArgMax |❌|
+| ArgMin |❌|
+| AveragePool2D |✅|
+| BatchToSpaceND |❌|
+| Cast |❌|
+| Concatenation |✅|
+| Conv2D |✅|
+| DepthwiseConv2D |✅|
+| Div |✅|
+| Equal |❌|
+| Exp |✅|
+| ExpandDims |❌|
+| Floor |✅|
+| FullyConnected |✅|
+| Gather |❌|
+| Greater |❌|
+| GreaterEqual |❌|
+| MaxPool2D |✅|
+| Mean |✅|
+| Mul |✅|
+| L2Normalization |✅|
+| L2Pool2D |❌|
+| LessEqual |❌|
+| Log |✅|
+| Logistic |❌|
+| LogSoftmax |❌|
+| Maximum |✅|
+| Minimum |✅|
+| Neg |✅|
+| NotEqual |❌|
+| Pack |❌|
+| Pad |✅|
+| Pow |❌|
+| PRelu |❌|
+| ReduceMax |✅|
+| ReduceProd |❌|
+| Reshape |✅|
+| ResizeBilinear |✅|
+| Rsqrt |✅|
+| Select |❌|
+| Shape |❌|
+| Sin |✅|
+| Slice |❌|
+| Softmax |✅|
+| SpaceToDepth |❌|
+| SpaceToBatchND |❌|
+| SparseToDense |❌|
+| Split |❌|
+| Sqrt |✅|
+| Square |✅|
+| Squeeze |❌|
+| Sub |✅|
+| Sum |✅|
+| Tile |❌|
+| TopK |❌|
+| Transpose |✅|
+| TransposeConv |❌|
+| LogicalOr |❌|
+| OneHot |❌|
+| LogicalAnd |❌|
+| LogicalNot |❌|
+| UnPack |❌|
+| ReduceMin |✅|
+| FloorDiv |❌|
+| ReduceAny |❌|
+| ZerosLike |❌|
+| Fill |❌|
+| FloorMod |❌|
+| Range |❌|
+| ResizeNearesetNeighbor |✅|
+| LeakyRelu |✅|
+| MirrorPad |❌|
+| Abs |✅|
+| SplitV |❌|
+| Unique |❌|
+| Ceil |✅|
+| Reverse |❌|
+| AddN |❌|
+| GatherND |❌|
+| Cos |✅|
+| Where |❌|
+| Rank |❌|
+| Elu |❌|
+| ReverseSequence |❌|

USAGE.md

@@ -0,0 +1,35 @@
+```
+DESCRIPTION
+NNCASE model compiler and inference tool.
+
+SYNOPSIS
+    ncc compile <input file> <output file> -i <input format> [-o <output
+        format>] [-t <target>] [--dataset <dataset path>] [--inference-type
+        <inference type>] [--input-mean <input mean>] [--input-std <input std>]
+        [--dump-ir] [-v]
+
+    ncc infer <input file> <output path> --dataset <dataset path> [--input-mean
+        <input mean>] [--input-std <input std>] [-v]
+
+OPTIONS
+    compile
+        <input file>        input file
+        <output file>       output file
+        -i, --input-format  input file format: e.g. tflite
+        -o, --output-format output file format: e.g. kmodel, default is kmodel
+        -t, --target        target arch: e.g. cpu, k210, default is k210
+        --dataset           calibration dataset, used in post quantization
+        --inference-type    inference type: e.g. float, uint8 default is uint8
+        --input-mean        input mean, default is 0.000000
+        --input-std         input std, default is 1.000000
+        --dump-ir           dump nncase ir to .dot files
+
+    infer
+        <input file>        input kmodel
+        <output path>       inference result output directory
+        --dataset           input dataset to inference
+        --input-mean        input mean, default is 0.000000
+        --input-std         input std, default is 1.000000
+
+    -v, --version           show version
+```