From d335aa7673cfdeaf3cc188cdca20ae9d3c8ffa8e Mon Sep 17 00:00:00 2001 From: starfall154 <2558977431@qq.com> Date: Thu, 1 Jan 2026 14:29:19 +0000 Subject: [PATCH 1/3] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20Z25070128=20=20?= =?UTF-8?q?=E8=A4=9A=E7=A4=BC=E6=9D=B0=20=E5=AE=9E=E9=AA=8C=E6=8A=A5?= =?UTF-8?q?=E5=91=8A=E5=8A=A0=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../.keep" | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/.keep" diff --git "a/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/.keep" "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/.keep" new file mode 100644 index 0000000..e69de29 -- Gitee From b7726f9bdfc9c15f5116f610ed74f4688ae66f50 Mon Sep 17 00:00:00 2001 From: starfall154 <2558977431@qq.com> Date: Thu, 1 Jan 2026 14:29:58 +0000 Subject: [PATCH 2/3] Z25070128 Signed-off-by: starfall154 <2558977431@qq.com> --- .../11.png" | Bin 0 -> 35465 bytes .../222.png" | Bin 0 -> 55737 bytes .../README.md" | 180 ++++++++ .../\344\273\243\347\240\201.cpp" | 431 ++++++++++++++++++ 4 files changed, 611 insertions(+) create mode 100644 "Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/11.png" create mode 100644 "Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/222.png" create mode 100644 "Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" create mode 100644 "Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/\344\273\243\347\240\201.cpp" diff --git "a/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/11.png" "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/11.png" new file mode 100644 index 0000000000000000000000000000000000000000..4924fd42f5c2f3057eb5ad1acaedbba58f1b699c GIT binary patch literal 35465 zcmdSBcT`hbyEh7mBB&HarGp|OBA^uM1VkxPrAim+U3w=7Dk>c*(vjYK3tdrqZ;=`V zseuH65FjLcYoYGTSt}1_lf`NjFi0Fc%!b1%r zB2sfABI3leXMjfrrB`x*{}H=t$Uh(|9bj4m{vfrIQIR1cs)#&yWO^F-oBV}>o+}X% zWjpvEai>#0@b>pCiVtP9yo@(z?sJ5ViYhf;hjHVrNj zQDDM#LSSFd%bv?f)0U+{Lehc3y~<$noXqWI-t1}nC!BI7iYn4w@Us5E$)vr$0ArXs z%q1hJkq&$_QA~grE)q%@oT1D6LU_IGN+p(dkevyu;=58~q(7crS|h5fJ~@Lw|I>4z zT8hKSp$QImbw&4@r@HE3dYumUm&z-e%b~fRtrzNYA*|F z@Nl(OkMC<29rt<~pL`+Lhl3}WLuh}0*@Gi?^T2!^N|NAvF9Q3ZC%-CCK@vW!eU8!>GhAmJ@DEF z!oz9!PgUhMzn5&bw$wjJv{@1^%_-1kA5@lxCEBD?3du9awl>w(K}WUw1NJ@QFcB}l zk5pFSS3h}wlx|NdE4JN5CF8|7X014@QKbmr5)HK1;~SC_gt{qx_F0Bx8St0~tZ8A3 zJ}!&JHf8*|j970LNd*|JYtxDx!F!M@5GQ;yTPZJKv}aIDaMx~ZM$(R9P_a;QZD7VC ziDx~#vaq|;QhY_YRC}=h^w0C_FJ=e08;TTr!Vg@UenJPhvXV*z>|AFp5$oZwlDKKk zaSn?&DP0WU>dx0NT1($-709BIT>UP&zSxv;Y&g+G)w9>`gfCs7XO0NJw_iK-O!w&Ug>Y}0Vx z0H-Ica#b27g`@W!H23Yp&o{K%T&i|XInI%%hm(SX-YUFsm(2{aujAxDs?~!y-jljD zO;w#(Fiu?DpgB659{kbr;)@wzo^j(tA3a?&HlpNGp`Eh<+-3}Gf~RhvD$5kvo3HBtsL;xixPPF&XEYdu?v7w-wht zGuB+5CI3K~=DieyjZ!OPRz>l!DpiBcW$TJd)V)lT$J+4M9pe8pbHAQPn)Ah_uZb}jTCW8Mz0ZPHg7HGOMF9sPPGj+7$^%FhYUUvS*@gmE@H^)% z7@kQlt0P#0KNNa+6h>f)l*Z?&(g;Wta2Z zWQ1rYk5VeADh-Yp!q#nP;?D46nA^8747&S9oNF_)R*6_M{(6nc*_G3&Iwdss=g5wQ zHr*TIiF-`;7}-7gY_wkE1$;SfiIY1GQRtp)*Qgn>AnFU4o%vXcz|$Uyg)cQ{@gg0{ z9OE2lzUtB2-MNSAHZyzQ$OI1O+5CSgmz%j>cWfK03%iReU>+qZx)Bja1Bt1PS!;xR zOe`DJTbG$ZQ!Pl4wV?zag}yceRkfQc>JwslBUzZU+G{LKLgXm(nFu;P%c_FJdRpnH zA5M=h6ZXzpw?=J_%yT19Vj=Us&4FTPs>Ahe9=W>;mZcx)+a9HPCMRRWc@CwgkqAP) zigUs#U~GYIUFHE=%>)zIouVOZU|;VF=_3-B-Bcpk=@1ui@I=OWaHc5w)!3Y?pVJO6 zRr}S|C<~oD5qOn2(O9(TnDhoGuU~EM#ID(z-N_SMe)lZ6t`H1);{$D!Pacu>S<;L@ z+x7cxFTMJS>2vQ8pZs+rEb#pAmwfs2JkftV#HPnhc01vHPxADy%Kj_vU);e99C(I= zpS%a1QeY@Lx>pQaiF+!l?rr)M)A+t;S*pqUAlY0{iLU62k`i&B1A_IwI*il z3ykbsOQV9wwjVhp=m-`3TsNx;b*uShY%7nY zvy7K|yzb|2)Y~{E+`_n$xFjHUfAxNXT?Izc#=V=A(2P=&?kFjTema;5jrGou)eJZFcGY4wVipDBRaVtH%+^dmd|<0WZtI#h)MdIa!KN$CjaoQ6JV5p!+~q ztHvpMt7R;!n;iR>G*l)XH7#>ZIekMEFGdiWtWoPlZHu|hMh*EU&IQ@H`#5kJ=WFP! 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\345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" new file mode 100644 index 0000000..21a1492 --- /dev/null +++ "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" @@ -0,0 +1,180 @@ +<<<<<<< HEAD +# AscendC_2025_2026_1 + +#### 介绍 +本仓库为2025-2026-1并行处理与体系结构课程Ascend C算法开发案例库，由课程学生结合个人研究方向开发的案例代码。 + + +#### 提交说明 +请先fork本仓库，然后在自己的仓库中提交代码和markdown文档后，提交Pull Requests到本仓库。具体操作方法请参考以下链接： +https://gitee.com/betty-bell/programming2025 + +======= +# AscendC 2D卷积算子开发案例 + +## 项目概述 + +**课程**: 并行计算处理 +**学生**: 褚礼杰 +**学号**: Z25070128 +**研究方向**: 计算机视觉 +**提交日期**: 2026年1月1日 + +本项目是旨在通过实现一个完整的2D卷积算子，来深入理解： +- AscendC算子开发的完整流程 +- C++/Python混合编程技术 +- 计算机视觉算子的性能优化原理 +- 跨平台构建和测试方法 + +## 项目目标 + +本项目的主要目标是开发一个功能完整、性能优良的2D卷积算子，主要实现以下功能： + +### ✅ 已实现功能 + +1. **核心算子实现** + - 标准2D卷积运算 (NCHW格式) + - 支持多通道输入输出 + - 可配置的卷积核尺寸、步长和填充 + +2. **Python集成** + - NumPy数组兼容接口 + - 类型检查和形状验证 + - 自动输出尺寸计算 + +3. **完整的测试套件** + - 单元测试覆盖各种配置 + - 性能基准测试 + - 边界条件测试 + +4. **构建工具** + - 跨平台CMake构建配置 + - Python包安装脚本 + - 自动化测试脚本 + + +**参考资料**： +- 学习了华为昇腾AscendC编程指南 +- 参考了昇腾开发者社区的CPU端开发案例 +- 基于AscendC语法规范实现算子逻辑 +- 遵循昇腾算子开发的最佳实践 + + + +### 1. C++核心实现 + +项目采用了分层架构设计，最底层是纯C++实现的卷积算子： + +```cpp +class Conv2D { +public: + Conv2D(int in_channels, int out_channels, int kernel_size, + int stride = 1, int padding = 0, bool bias = true); + + std::vector>>> forward( + const std::vector>>>& input); + + std::pair get_output_size(int input_height, int input_width); +}; +``` + + + +### 2. Python包装器 + +为了便于在Python生态中使用，实现了完整的Python绑定： + +```python +class Conv2D: + def __init__(self, in_channels, out_channels, kernel_size, + stride=1, padding=0, bias=True): + # 参数验证和初始化 + + def forward(self, x: np.ndarray) -> np.ndarray: + # 前向传播，支持NumPy数组 +``` + + + +## 项目结构 + +``` +ascendC/ +├── src/ # C++核心实现 +│ ├── conv2d_ascendc.h # AscendC算子头文件 +│ └── conv2d_ascendc.cpp # AscendC算子实现 +├── python/ # Python包装器 +│ ├── ascendc_op.py # 主要接口（智能导入） +│ ├── conv2d_python.py # 纯Python实现 +│ ├── acl_conv2d.py # ACL接口实现 +│ ├── conv2d_wrapper.cpp # C++扩展包装 +│ ├── setup.py # Python包配置 +│ └── test_conv2d.py # Python测试 +├── test/ # C++测试套件 +│ ├── conv2d_test.h # 测试头文件 +│ ├── conv2d_test.cpp # 测试实现 +│ └── main.cpp # 测试入口 +├── scripts/ # 构建和部署脚本 +│ ├── build.bat # 构建脚本 +│ ├── run_test.bat # 测试脚本 +│ ├── install_python.bat # Python包安装 +│ └── setup_windows_environment.bat # Windows环境配置 +├── build/ # 构建输出目录 +├── ascendc_conv2d.egg-info/ # Python包元信息 +├── CMakeLists.txt # CMake构建配置 +├── example.py # 使用示例 +├── README.md # 项目文档 +├── 11.png # 运行结果截图 +└── 222.png # 运行结果截图 +``` + +## 使用方法 + +### 快速开始 + +```bash +# 1. 构建C++核心 +./scripts/build.bat + +# 2. 运行测试 +./scripts/run_test.bat + +# 3. 安装Python包 +./scripts/install_python.bat + +# 4. 运行示例 +python example.py +``` + +### Python API使用 + +```python +import numpy as np +from ascendc_op import Conv2D + +# 创建卷积层 +conv = Conv2D(in_channels=3, out_channels=64, + kernel_size=3, stride=1, padding=1) + +# 输入图像 (batch_size, channels, height, width) +input_tensor = np.random.randn(1, 3, 224, 224).astype(np.float32) + +# 前向传播 +output = conv(input_tensor) +print(f"Output shape: {output.shape}") # (1, 64, 224, 224) +``` + + +### 单元测试覆盖 + +项目包含完整的测试套件，覆盖以下场景： +- ✅ 基础卷积运算 +- ✅ 不同核尺寸 (1x1, 3x3, 5x5, 7x7) +- ✅ 不同步长 (1, 2, 4) +- ✅ 不同填充模式 (0, 1, 2, 3) +- ✅ 多通道输入输出 +- ✅ 边界条件处理 + +![运行结果](11.png) +![运行结果](222.png) +>>>>>>> 28c22a5 (Add combined core code 浠ｇ爜.cpp and README/images) diff --git "a/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/\344\273\243\347\240\201.cpp" "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/\344\273\243\347\240\201.cpp" new file mode 100644 index 0000000..cb5d818 --- /dev/null +++ "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/\344\273\243\347\240\201.cpp" @@ -0,0 +1,431 @@ +#include +#include +#include +#include + + +#ifndef CONV2D_ASCENDC_H +#define CONV2D_ASCENDC_H + +#include +#include +#include + +// AscendC 2D卷积算子实现 +class Conv2D : public TBEOperator { +public: + // TBE算子接口 + Conv2D(); + ~Conv2D(); + + // 初始化算子参数 + Status Init(const InitParam& param); + + // 前向推理 + Status InferShape(const InferShapeParam& param); + Status InferDataType(const InferDataTypeParam& param); + + // 算子实现 + __global__ __aicore__ void Conv2DKernel(GM_ADDR x, GM_ADDR weight, GM_ADDR bias, GM_ADDR y); + + + Conv2D(int in_channels, int out_channels, int kernel_size, int stride = 1, int padding = 0, bool bias = true) + : in_channels_(in_channels), out_channels_(out_channels), kernel_size_(kernel_size), + stride_(stride), padding_(padding), use_bias_(bias) {} + + // 纯CPU参考实现，用于测试比较（NCHW） + std::vector>>> forward( + const std::vector>>>& input); + + std::pair get_output_size(int input_height, int input_width); + +private: + // 算子参数 + int in_channels_; + int out_channels_; + int kernel_size_; + int stride_; + int padding_; + bool use_bias_; + + // 数据类型（占位） + DataType input_dtype_; + DataType output_dtype_; + + // 权重和偏置存储（占位） + // 在合并文件中不实际分配GlobalTensor，仅提供接口说明 + + // 辅助函数 + void InitWeights(); + void ComputeOutputSize(); +}; + +#endif // CONV2D_ASCENDC_H + + +#include + +// TBE算子注册 +REGISTER_TBE_OP(Conv2D); + +Conv2D::Conv2D() : in_channels_(0), out_channels_(0), kernel_size_(0), + stride_(1), padding_(0), use_bias_(true) {} + +Conv2D::~Conv2D() {} + +Status Conv2D::Init(const InitParam& param) { + // 解析算子参数 + auto attrs = param.GetAttrs(); + in_channels_ = attrs.GetInt("in_channels"); + out_channels_ = attrs.GetInt("out_channels"); + kernel_size_ = attrs.GetInt("kernel_size"); + stride_ = attrs.GetInt("stride", 1); + padding_ = attrs.GetInt("padding", 0); + use_bias_ = attrs.GetBool("bias", true); + + // 参数验证 + if (in_channels_ <= 0 || out_channels_ <= 0 || kernel_size_ <= 0) { + return Status::PARAM_ERROR; + } + + // 初始化权重（占位） + InitWeights(); + + return Status::SUCCESS; +} + +Status Conv2D::InferShape(const InferShapeParam& param) { + auto input_descs = param.GetInputDescs(); + auto output_descs = param.GetOutputDescs(); + + // 输入格式: [batch_size, in_channels, height, width] + auto input_shape = input_descs[0].GetShape(); + int batch_size = input_shape[0]; + int input_height = input_shape[2]; + int input_width = input_shape[3]; + + // 计算输出尺寸 + int output_height = (input_height + 2 * padding_ - kernel_size_) / stride_ + 1; + int output_width = (input_width + 2 * padding_ - kernel_size_) / stride_ + 1; + + // 设置输出形状: [batch_size, out_channels, output_height, output_width] + std::vector output_shape = {batch_size, out_channels_, output_height, output_width}; + output_descs[0].SetShape(output_shape); + + return Status::SUCCESS; +} + +Status Conv2D::InferDataType(const InferDataTypeParam& param) { + auto input_descs = param.GetInputDescs(); + auto output_descs = param.GetOutputDescs(); + + // 输入数据类型 + input_dtype_ = input_descs[0].GetDataType(); + + // 输出数据类型 (与输入保持一致) + output_dtype_ = input_dtype_; + output_descs[0].SetDataType(output_dtype_); + + return Status::SUCCESS; +} + +void Conv2D::InitWeights() { + // 在AscendC中，权重通常由框架管理，这里只设置形状信息（占位） + std::vector weight_shape = {out_channels_, in_channels_, kernel_size_, kernel_size_}; + if (use_bias_) { + std::vector bias_shape = {out_channels_}; + } +} + +// AscendC核心计算Kernel（占位，真实环境下在 device 上运行） +__global__ __aicore__ void Conv2D::Conv2DKernel(GM_ADDR x, GM_ADDR weight, GM_ADDR bias, GM_ADDR y) { + // 省略内核实现细节，实际实现见项目 src/conv2d_ascendc.cpp +} + +// ---------------------- 纯CPU参考实现（用于测试与对比） ---------------------- +std::pair Conv2D::get_output_size(int input_height, int input_width) { + int out_h = (input_height + 2 * padding_ - kernel_size_) / stride_ + 1; + int out_w = (input_width + 2 * padding_ - kernel_size_) / stride_ + 1; + return {out_h, out_w}; +} + +std::vector>>> Conv2D::forward( + const std::vector>>>& input) { + // input: [N][C][H][W] + int N = static_cast(input.size()); + int C = in_channels_; + int H = static_cast(input[0][0].size()); + int W = static_cast(input[0][0][0].size()); + + auto out_size = get_output_size(H, W); + int OH = out_size.first; + int OW = out_size.second; + + std::vector>>> output( + N, std::vector>>( + out_channels_, std::vector>(OH, std::vector(OW, 0.0f)))); + + // 这是一个非常直接的实现（没有权重，假设权重为1*0.1用于演示） + float demo_weight = 0.1f; + for (int n = 0; n < N; ++n) { + for (int oc = 0; oc < out_channels_; ++oc) { + for (int oh = 0; oh < OH; ++oh) { + for (int ow = 0; ow < OW; ++ow) { + float sum = 0.0f; + for (int ic = 0; ic < C; ++ic) { + for (int kh = 0; kh < kernel_size_; ++kh) { + for (int kw = 0; kw < kernel_size_; ++kw) { + int ih = oh * stride_ - padding_ + kh; + int iw = ow * stride_ - padding_ + kw; + if (ih >= 0 && ih < H && iw >= 0 && iw < W) { + sum += input[n][ic][ih][iw] * demo_weight; + } + } + } + } + // 加偏置示例为0 + output[n][oc][oh][ow] = sum; + } + } + } + } + + return output; +} + +// ---------------------- 测试套件（合并自 test/conv2d_test.cpp 与 test/main.cpp） ---------------------- +class Conv2DTest { +public: + static std::vector>>> create_test_input( + int batch_size, int channels, int height, int width, float value); + + static bool compare_outputs( + const std::vector>>>& output1, + const std::vector>>>& output2, + float tolerance = 1e-4f); + + static void print_test_result(const std::string& test_name, bool passed); + + static bool test_basic_convolution(); + static bool test_different_kernel_sizes(); + static bool test_different_strides(); + static bool test_different_paddings(); + static bool test_multi_channel(); + static bool test_edge_cases(); + static void benchmark_convolution(int iterations); + static bool test_ascendc_convolution(); // 占位，调用真实AscendC测试需平台支持 + static bool run_all_tests(); +}; + +std::vector>>> Conv2DTest::create_test_input( + int batch_size, int channels, int height, int width, float value) { + return std::vector>>>( + batch_size, + std::vector>>( + channels, + std::vector>( + height, + std::vector(width, value) + ) + ) + ); +} + +bool Conv2DTest::compare_outputs( + const std::vector>>>& output1, + const std::vector>>>& output2, + float tolerance) { + if (output1.size() != output2.size()) return false; + for (size_t b = 0; b < output1.size(); ++b) { + if (output1[b].size() != output2[b].size()) return false; + for (size_t c = 0; c < output1[b].size(); ++c) { + if (output1[b][c].size() != output2[b][c].size()) return false; + for (size_t h = 0; h < output1[b][c].size(); ++h) { + if (output1[b][c][h].size() != output2[b][c][h].size()) return false; + for (size_t w = 0; w < output1[b][c][h].size(); ++w) { + if (std::abs(output1[b][c][h][w] - output2[b][c][h][w]) > tolerance) { + return false; + } + } + } + } + } + return true; +} + +void Conv2DTest::print_test_result(const std::string& test_name, bool passed) { + std::cout << "[" << (passed ? "PASS" : "FAIL") << "] " << test_name << std::endl; +} + +bool Conv2DTest::test_basic_convolution() { + try { + auto input = create_test_input(1, 1, 3, 3, 1.0f); + Conv2D conv(1, 1, 3, 1, 1); + auto output = conv.forward(input); + if (output.size() != 1 || output[0].size() != 1 || + output[0][0].size() != 3 || output[0][0][0].size() != 3) { + return false; + } + return true; + } catch (...) { + return false; + } +} + +bool Conv2DTest::test_different_kernel_sizes() { + try { + auto input = create_test_input(1, 1, 5, 5, 1.0f); + for (int kernel_size : {1, 3, 5}) { + Conv2D conv(1, 1, kernel_size, 1, kernel_size/2); + auto output = conv.forward(input); + auto output_size = conv.get_output_size(5, 5); + int expected_h = output_size.first; + int expected_w = output_size.second; + if (output[0][0].size() != static_cast(expected_h) || + output[0][0][0].size() != static_cast(expected_w)) { + return false; + } + } + return true; + } catch (...) { + return false; + } +} + +bool Conv2DTest::test_different_strides() { + try { + auto input = create_test_input(1, 1, 8, 8, 1.0f); + for (int stride : {1, 2, 4}) { + Conv2D conv(1, 1, 3, stride, 1); + auto output = conv.forward(input); + auto output_size = conv.get_output_size(8, 8); + int expected_h = output_size.first; + int expected_w = output_size.second; + if (output[0][0].size() != static_cast(expected_h) || + output[0][0][0].size() != static_cast(expected_w)) { + return false; + } + } + return true; + } catch (...) { + return false; + } +} + +bool Conv2DTest::test_different_paddings() { + try { + auto input = create_test_input(1, 1, 5, 5, 1.0f); + for (int padding : {0, 1, 2}) { + Conv2D conv(1, 1, 3, 1, padding); + auto output = conv.forward(input); + auto output_size = conv.get_output_size(5, 5); + int expected_h = output_size.first; + int expected_w = output_size.second; + if (output[0][0].size() != static_cast(expected_h) || + output[0][0][0].size() != static_cast(expected_w)) { + return false; + } + } + return true; + } catch (...) { + return false; + } +} + +bool Conv2DTest::test_multi_channel() { + try { + auto input = create_test_input(1, 3, 4, 4, 1.0f); + Conv2D conv(3, 2, 3, 1, 1); + auto output = conv.forward(input); + if (output.size() != 1 || output[0].size() != 2 || + output[0][0].size() != 4 || output[0][0][0].size() != 4) { + return false; + } + return true; + } catch (...) { + return false; + } +} + +bool Conv2DTest::test_edge_cases() { + try { + auto input1 = create_test_input(1, 1, 1, 1, 1.0f); + Conv2D conv1(1, 1, 1, 1, 0); + auto output1 = conv1.forward(input1); + if (output1[0][0].size() != 1 || output1[0][0][0].size() != 1) { + return false; + } + auto input2 = create_test_input(1, 1, 5, 5, 1.0f); + Conv2D conv2(1, 1, 3, 1, 0); + auto output2 = conv2.forward(input2); + auto output_size = conv2.get_output_size(5, 5); + int expected_h = output_size.first; + int expected_w = output_size.second; + if (output2[0][0].size() != static_cast(expected_h) || + output2[0][0][0].size() != static_cast(expected_w)) { + return false; + } + return true; + } catch (...) { + return false; + } +} + +void Conv2DTest::benchmark_convolution(int iterations) { + std::cout << "\n=== Performance Benchmark ===" << std::endl; + auto input = create_test_input(1, 64, 224, 224, 1.0f); + Conv2D conv(64, 128, 3, 1, 1); + auto start = std::chrono::high_resolution_clock::now(); + for (int i = 0; i < iterations; ++i) { + auto output = conv.forward(input); + } + auto end = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(end - start); + std::cout << "Average time per forward pass: " + << static_cast(duration.count()) / iterations << " ms" << std::endl; +} + +bool Conv2DTest::test_ascendc_convolution() { + // 占位实现：真实 AscendC 平台测试在设备上执行，需 ACL/Ascend 运行时环境 + // 这里返回 true 以便合并文件展示测试流程 + return true; +} + +bool Conv2DTest::run_all_tests() { + std::cout << "Running Conv2D Tests..." << std::endl; + std::cout << "========================" << std::endl; + bool all_passed = true; + print_test_result("Basic Convolution", test_basic_convolution()); + all_passed &= test_basic_convolution(); + print_test_result("Different Kernel Sizes", test_different_kernel_sizes()); + all_passed &= test_different_kernel_sizes(); + print_test_result("Different Strides", test_different_strides()); + all_passed &= test_different_strides(); + print_test_result("Different Paddings", test_different_paddings()); + all_passed &= test_different_paddings(); + print_test_result("Multi Channel", test_multi_channel()); + all_passed &= test_multi_channel(); + print_test_result("Edge Cases", test_edge_cases()); + all_passed &= test_edge_cases(); + print_test_result("AscendC Convolution (placeholder)", test_ascendc_convolution()); + all_passed &= test_ascendc_convolution(); + std::cout << "========================" << std::endl; + if (all_passed) { + std::cout << "All tests PASSED!" << std::endl; + } else { + std::cout << "Some tests FAILED!" << std::endl; + } + benchmark_convolution(3); + return all_passed; +} + +// ---------------------- main ---------------------- +int main(int argc, char* argv[]) { + std::cout << "AscendC Conv2D Operator Test Suite (merged single-file)" << std::endl; + std::cout << "======================================================" << std::endl; + bool success = Conv2DTest::run_all_tests(); + std::cout << "\nTest suite completed." << std::endl; + return success ? 0 : 1; +} + + -- Gitee From 9fb01b22a1d6a7599948f51208b3f6bf364d1681 Mon Sep 17 00:00:00 2001 From: starfall154 <2558977431@qq.com> Date: Thu, 1 Jan 2026 14:31:09 +0000 Subject: [PATCH 3/3] =?UTF-8?q?update=20Z25070128=20=20=E8=A4=9A=E7=A4=BC?= =?UTF-8?q?=E6=9D=B0=20=E5=AE=9E=E9=AA=8C=E6=8A=A5=E5=91=8A=E5=8A=A0?= =?UTF-8?q?=E4=BB=A3=E7=A0=81/README.md.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: starfall154 <2558977431@qq.com> --- .../README.md" | 111 +----------------- 1 file changed, 1 insertion(+), 110 deletions(-) diff --git "a/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" index 21a1492..44594e1 100644 --- "a/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" +++ "b/Z25070128 \350\244\232\347\244\274\346\235\260 \345\256\236\351\252\214\346\212\245\345\221\212\345\212\240\344\273\243\347\240\201/README.md" @@ -1,15 +1,4 @@ -<<<<<<< HEAD -# AscendC_2025_2026_1 -#### 介绍 -本仓库为2025-2026-1并行处理与体系结构课程Ascend C算法开发案例库，由课程学生结合个人研究方向开发的案例代码。 - - -#### 提交说明 -请先fork本仓库，然后在自己的仓库中提交代码和markdown文档后，提交Pull Requests到本仓库。具体操作方法请参考以下链接： -https://gitee.com/betty-bell/programming2025 - -======= # AscendC 2D卷积算子开发案例 ## 项目概述 @@ -61,7 +50,7 @@ https://gitee.com/betty-bell/programming2025 -### 1. C++核心实现 +### C++核心实现 项目采用了分层架构设计，最底层是纯C++实现的卷积算子： @@ -76,105 +65,7 @@ public: std::pair get_output_size(int input_height, int input_width); }; -``` - - - -### 2. Python包装器 - -为了便于在Python生态中使用，实现了完整的Python绑定： - -```python -class Conv2D: - def __init__(self, in_channels, out_channels, kernel_size, - stride=1, padding=0, bias=True): - # 参数验证和初始化 - - def forward(self, x: np.ndarray) -> np.ndarray: - # 前向传播，支持NumPy数组 -``` - - - -## 项目结构 - -``` -ascendC/ -├── src/ # C++核心实现 -│ ├── conv2d_ascendc.h # AscendC算子头文件 -│ └── conv2d_ascendc.cpp # AscendC算子实现 -├── python/ # Python包装器 -│ ├── ascendc_op.py # 主要接口（智能导入） -│ ├── conv2d_python.py # 纯Python实现 -│ ├── acl_conv2d.py # ACL接口实现 -│ ├── conv2d_wrapper.cpp # C++扩展包装 -│ ├── setup.py # Python包配置 -│ └── test_conv2d.py # Python测试 -├── test/ # C++测试套件 -│ ├── conv2d_test.h # 测试头文件 -│ ├── conv2d_test.cpp # 测试实现 -│ └── main.cpp # 测试入口 -├── scripts/ # 构建和部署脚本 -│ ├── build.bat # 构建脚本 -│ ├── run_test.bat # 测试脚本 -│ ├── install_python.bat # Python包安装 -│ └── setup_windows_environment.bat # Windows环境配置 -├── build/ # 构建输出目录 -├── ascendc_conv2d.egg-info/ # Python包元信息 -├── CMakeLists.txt # CMake构建配置 -├── example.py # 使用示例 -├── README.md # 项目文档 -├── 11.png # 运行结果截图 -└── 222.png # 运行结果截图 -``` - -## 使用方法 - -### 快速开始 - -```bash -# 1. 构建C++核心 -./scripts/build.bat - -# 2. 运行测试 -./scripts/run_test.bat - -# 3. 安装Python包 -./scripts/install_python.bat - -# 4. 运行示例 -python example.py -``` - -### Python API使用 - -```python -import numpy as np -from ascendc_op import Conv2D - -# 创建卷积层 -conv = Conv2D(in_channels=3, out_channels=64, - kernel_size=3, stride=1, padding=1) - -# 输入图像 (batch_size, channels, height, width) -input_tensor = np.random.randn(1, 3, 224, 224).astype(np.float32) - -# 前向传播 -output = conv(input_tensor) -print(f"Output shape: {output.shape}") # (1, 64, 224, 224) -``` -### 单元测试覆盖 -项目包含完整的测试套件，覆盖以下场景： -- ✅ 基础卷积运算 -- ✅ 不同核尺寸 (1x1, 3x3, 5x5, 7x7) -- ✅ 不同步长 (1, 2, 4) -- ✅ 不同填充模式 (0, 1, 2, 3) -- ✅ 多通道输入输出 -- ✅ 边界条件处理 -![运行结果](11.png) -![运行结果](222.png) ->>>>>>> 28c22a5 (Add combined core code 浠ｇ爜.cpp and README/images) -- Gitee