open sources

.gitignore

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+\.idea/
+build/
+cmake-build-debug/
+cmake-build-release/
+cmake-build-debug-wsl/
+cmake-build-debug-wsl-event-trace/

.gitmodules

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+[submodule "c-network/tools/external/ncnn"]
+	path = c-network/tools/external/ncnn
+	url = https://gitee.com/mirrors/ncnn.git

CMakeLists.txt

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+
+cmake_minimum_required(VERSION 3.0)
+project(c-net C)
+
+add_definitions(-Wall -Wno-unused-function -Ofast)
+set(CMAKE_C_STANDARD 99)
+
+if(NOT DEFINED CMAKE_INSTALL_PREFIX)
+    set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Installation Directory")
+endif()
+message(STATUS "CMAKE_INSTALL_PREFIX = ${CMAKE_INSTALL_PREFIX}")
+
+if(NOT CMAKE_BUILD_TYPE)
+    set(CMAKE_BUILD_TYPE release CACHE STRING "Choose the type of build" FORCE)
+endif()
+
+add_subdirectory(c-utils)
+add_subdirectory(c-benchmark)
+add_subdirectory(c-tester)
+add_subdirectory(c-tensor)
+add_subdirectory(c-image)
+add_subdirectory(c-network)
+add_subdirectory(c-netend)

LICENSE.md

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+
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README.md

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+ <div align="center">
+<img src="images/cnet.png" width="150" alt="cnet" />
+</div>
+
+# CNET
+
+CNET 是一个C99开发的的面向iot设备设计的深度学习推理库，实现深度学习算法在iot设备上的快速部署。
+
+
+## 1 主要特点
+
+- 极简，高效的内存管理，清晰的架构设计
+- 扩展，模块化设计，轻松完成裁剪和扩展
+- 轻便，完整的二进制库不到300kb
+- 可靠，完善的内存监测测试， 无内存泄露
+- 完善，支持现有的大量操作，可按需扩展
+- 高效，纯C实现，逐步加入MCU汇编优化
+
+架构简介：
+
+```c
+----------------------------------------------------------------
+ 前      examples        推理结果校验/人脸检测及识别/车牌检测及识别
+ 后      
+ 处      c-netend        网络后处理模块：支持目标检测/人脸识别及车牌识别
+ 理      c-image         图像加载及保存，结果绘制，输入预处理
+----------------------------------------------------------------
+ 神      c-network       神经网络组织及推理
+ 经      nn-case         模型转换工具
+ 网      cilibration     量化评估工具
+ 络      ncnn            模型优化/量化支撑/模型源
+----------------------------------------------------------------
+ 基      c-utils         基础工具，包括容器，分配器，线程池等
+ 础      c-benchmark     性能评估，无外部依赖，可在MCU执行
+ 模      c-tester        单元测试支持，无外部依赖，可在MCU执行
+ 块      c-tensor        张量计算支持，基础操作及内存管理
+----------------------------------------------------------------
+```
+
+## 2 支持情况
+
+- 作为NCNN的延展，将NCNN模型直接优化并量化为部署模型，故可导入Caffe/Onnx/Mxnet/darknet模型；
+- 全面量化支持，支持后量化训练, 支持INT16推理，后续将加入INT8推理;
+- 支持ESP32 等主流芯片，理论上完全兼容支持c99的所有模块;
+- 支持分类, 目标检测，人脸识别，车牌识别等主流算法；
+- 采用直接对象定义模型，无需手动加载，扁平化结构；
+
+### 2.1支持的算子列表
+
+```c
+    /// convolution
+    convolution_operation_type,
+    /// pooling
+    pooling_operation_type,
+    /// padding
+    padding_operation_type,
+    /// crop
+    crop_operation_type,
+    /// inner product
+    inner_product_operation_type,
+    /// activation
+    activation_operation_type,
+    /// batch norm
+    batch_norm_operation_type,
+    /// slice
+    slice_operation_type,
+    /// concat
+    concat_operation_type,
+    /// reshape
+    reshape_operation_type,
+    /// sigmoid
+    sigmoid_operation_type,
+    /// softmax
+    softmax_operation_type,
+    /// permute
+    permute_operation_type,
+    /// unary
+    unary_operation_type,
+    /// binary
+    binary_operation_type,
+    /// upsample
+    upsample_operation_type,
+    /// prelu
+    prelu_operation_type,
+    /// memory data
+    memory_data_operation_type,
+    /// shuffle_channel
+    shuffle_channel_operation_type,
+```
+
+### 2.2 支持的激活函数
+
+```c
+    /// bnll
+    bnll_activate_type,
+    /// clip
+    clip_activate_type,
+    /// elu
+    elu_activate_type,
+    /// exp
+    exp_activate_type,
+    /// hard sigmoid
+    hard_sigmoid_activate_type,
+    /// hard swish
+    hard_swish_activate_type,
+    /// log
+    log_activate_type,
+    /// power
+    power_activate_type,
+    /// relu
+    relu_activate_type,
+    /// selu
+    selu_activate_type,
+    /// hard swish
+    swish_activate_type,
+    /// threshold
+    threshold_activate_type,
+```
+
+## 3 案例展示
+
+车牌识别 | 人脸识别
+---------|----------
+ ![ff](images/plates.png)|  ![ff](images/faces.png)
+ 车牌检测: retian/yolo, INT16 | 人脸检测: retian/yolo, INT16
+ 车牌矫正: 关键点检测 + 仿射变换 | 人脸矫正: 关键点+仿射变换
+ 车牌识别: 车牌+颜色 支持INT16  | 人脸验证: CNN + VECTOR， mobilenet-face
+
+
+## 4. 路线图
+
+- 支持ESP32，性能优化
+- 支持INT8
+- 支持ESP32系列INT8优化
+- 其它芯片优化
+- 操作优化支持
+- 后端模型优化支持
+
+## 5. 参与贡献
+
+1. Fork 本仓库
+2. 新建 Feat_xxx 分支
+3. 提交代码
+4. 新建 Pull Request

c-benchmark/CMakeLists.txt

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+
+
+include_directories(include)
+include_directories(${utils})
+add_library(c-benchmark src/benchmark.c)
+
+add_executable(test_benchmark test/vector_dot_int8_benchmark.c test/vector_dot_int8.c test/bench_example.c)
+target_link_libraries(test_benchmark c-benchmark c-utils)
+
+# Module definition
+set(benchmark "${CMAKE_CURRENT_SOURCE_DIR}/include" "${CMAKE_CURRENT_SOURCE_DIR}/include" PARENT_SCOPE)
+
+