├── .gitignore
├── CMakeLists.txt
├── INT8
├── yolov5s-int8-aciq.bin
├── yolov5s-int8-aciq.param
├── yolov5s-int8.bin
├── yolov5s-int8.param
└── yolov5s.table
├── README.md
├── build
├── CMakeCache.txt
├── CMakeFiles
│ ├── 3.5.1
│ │ ├── CMakeCCompiler.cmake
│ │ ├── CMakeCXXCompiler.cmake
│ │ ├── CMakeDetermineCompilerABI_C.bin
│ │ ├── CMakeDetermineCompilerABI_CXX.bin
│ │ ├── CMakeSystem.cmake
│ │ ├── CompilerIdC
│ │ │ ├── CMakeCCompilerId.c
│ │ │ └── a.out
│ │ └── CompilerIdCXX
│ │ │ ├── CMakeCXXCompilerId.cpp
│ │ │ └── a.out
│ ├── CMakeDirectoryInformation.cmake
│ ├── CMakeOutput.log
│ ├── Makefile.cmake
│ ├── Makefile2
│ ├── TargetDirectories.txt
│ ├── cmake.check_cache
│ ├── feature_tests.bin
│ ├── feature_tests.c
│ ├── feature_tests.cxx
│ ├── progress.marks
│ └── yolov5s.dir
│ │ ├── CXX.includecache
│ │ ├── DependInfo.cmake
│ │ ├── build.make
│ │ ├── cmake_clean.cmake
│ │ ├── depend.internal
│ │ ├── depend.make
│ │ ├── flags.make
│ │ ├── link.txt
│ │ ├── progress.make
│ │ └── yolov5.cpp.o
├── Makefile
├── cmake_install.cmake
├── yolov5.jpg
└── yolov5s
├── bus.jpg
├── images
├── Screenshot from 2021-05-21 21-01-03.png
├── Screenshot from 2021-05-21 21-01-21.png
├── Screenshot from 2021-05-22 19-24-44.png
├── Screenshot from 2021-05-22 19-26-13.png
├── Screenshot from 2021-05-22 19-47-51.png
├── Screenshot from 2021-05-22 19-49-54.png
├── Screenshot from 2021-06-08 21-47-09.png
├── Screenshot from 2021-06-08 21-48-39.png
├── bus.jpg
├── cat.jpg
├── cat.py
├── yolov5-int8-aciq.jpg
├── yolov5-int8-kl.jpg
└── yolov5_ncnn_fp16.jpg
├── layer
├── yolov5focus.cpp
└── yolov5focus.h
├── ncnn-20210525-ubuntu-1604-shared
├── bin
│ ├── caffe2ncnn
│ ├── darknet2ncnn
│ ├── mxnet2ncnn
│ ├── ncnn2int8
│ ├── ncnn2mem
│ ├── ncnn2table
│ ├── ncnnmerge
│ ├── ncnnoptimize
│ └── onnx2ncnn
├── include
│ └── ncnn
│ │ ├── allocator.h
│ │ ├── benchmark.h
│ │ ├── blob.h
│ │ ├── c_api.h
│ │ ├── command.h
│ │ ├── cpu.h
│ │ ├── datareader.h
│ │ ├── gpu.h
│ │ ├── layer.h
│ │ ├── layer_shader_type.h
│ │ ├── layer_shader_type_enum.h
│ │ ├── layer_type.h
│ │ ├── layer_type_enum.h
│ │ ├── mat.h
│ │ ├── modelbin.h
│ │ ├── ncnn_export.h
│ │ ├── net.h
│ │ ├── option.h
│ │ ├── paramdict.h
│ │ ├── pipeline.h
│ │ ├── pipelinecache.h
│ │ ├── platform.h
│ │ ├── simpleocv.h
│ │ ├── simpleomp.h
│ │ ├── simplestl.h
│ │ └── vulkan_header_fix.h
└── lib
│ ├── cmake
│ └── ncnn
│ │ ├── ncnn-release.cmake
│ │ ├── ncnn.cmake
│ │ └── ncnnConfig.cmake
│ ├── libncnn.so
│ ├── libncnn.so.1
│ └── libncnn.so.1.0.20210525
├── yolov5.cpp
├── yolov5s-int8-aciq.bin
├── yolov5s-int8-aciq.param
├── yolov5s-int8.bin
├── yolov5s-int8.param
├── yolov5s-opt.bin
├── yolov5s-opt.param
├── yolov5s.bin
└── yolov5s.param
/.gitignore:
--------------------------------------------------------------------------------
1 | images/cat.py
2 |
--------------------------------------------------------------------------------
/CMakeLists.txt:
--------------------------------------------------------------------------------
1 | cmake_minimum_required(VERSION 2.8)
2 |
3 | project(yolov5s)
4 |
5 | add_definitions(-std=c++11)
6 | add_definitions("-Wall")
7 |
8 | #set(OpenCV_DIR "/opt/intel/openvino_2019.3.334/opencv")
9 | find_package(OpenCV 3.4.8 REQUIRED)
10 |
11 | if(True)
12 | find_package(OpenMP)
13 | if(NOT TARGET OpenMP::OpenMP_CXX AND (OpenMP_CXX_FOUND OR OPENMP_FOUND))
14 | set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
15 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
16 | set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
17 | endif()
18 | endif()
19 |
20 | include_directories(${OpenCV_DIR}/include
21 | ${PROJECT_SOURCE_DIR}/ncnn-20210525-ubuntu-1604-shared/include/ncnn)
22 |
23 | link_directories(${OpenCV_DIR}/lib/
24 | ${PROJECT_SOURCE_DIR}/ncnn-20210525-ubuntu-1604-shared/lib)
25 | set(OpenCV_LIBS opencv_core
26 | opencv_highgui
27 | opencv_imgproc
28 | opencv_imgcodecs
29 | opencv_videoio)
30 |
31 | set(SRC
32 | yolov5.cpp
33 | )
34 |
35 | add_executable(yolov5s ${SRC})
36 | target_link_libraries(yolov5s ncnn ${OpenCV_LIBS})
37 | if(OpenMP_CXX_FOUND)
38 | if(NCNN_CMAKE_VERBOSE)
39 | message("Building with OpenMP")
40 | endif()
41 | target_link_libraries(yolov5s PUBLIC OpenMP::OpenMP_CXX)
42 | endif()
43 |
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/INT8/yolov5s-int8-aciq.bin:
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https://raw.githubusercontent.com/midasklr/yolov5ncnn/ac08ec886af8530e148d23d41b951d2299aa9f17/INT8/yolov5s-int8-aciq.bin
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/INT8/yolov5s-int8.bin:
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https://raw.githubusercontent.com/midasklr/yolov5ncnn/ac08ec886af8530e148d23d41b951d2299aa9f17/INT8/yolov5s-int8.bin
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/README.md:
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1 | YOLOv5转NCNN
2 |
3 | ## update20210608
4 |
5 | 新增yolov5模型剪枝和NCNN INT8量化。
6 |
7 |
8 | 项目持续更新，主要包括yolov5s模型的剪枝，int8量化，ncnn模型转换和Android移植测试。
9 |
10 |
11 | 基于YOLOv5最新[v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0)，和NCNN官方给出example的差别主要有：
12 |
13 | - 激活函数hardswish变为siLu；
14 | - 流程和[详细记录u版YOLOv5目标检测ncnn实现](https://zhuanlan.zhihu.com/p/275989233?utm_source=qq)略微不同
15 |
16 | ## 编译运行
17 |
18 | 动态库用的是官方编译好的ncnn-20210507-ubuntu-1604-shared
19 |
20 | ```
21 | mkdir build
22 | cd build
23 | cmake ..
24 | make -j8
25 | ./yolov5 ../bus.jpg
26 | ```
27 |
28 | 可以看到:
29 |
30 |
31 | 
32 |
33 |
34 |
35 |
36 | ## 安卓
37 |
38 | 参考https://github.com/nihui/ncnn-android-yolov5 ，使用这里转的v5.0分支的ncnn模型。
39 |
40 | ## Yolov5s剪枝
41 | 见https://github.com/midasklr/yolov5prune
42 | 稀疏训练+Bn层剪枝，可以获得更加紧凑的模型，这里一次稀疏训练+60%Bn层剪枝，模型从28M降低到7M。
43 |
44 | ## 流程
45 |
46 | 以下为yolov5s.pt转NCNN流程，自己训练的模型一样：
47 |
48 | ## pytorch测试和导出onnx
49 |
50 | 先测试下yolov5s效果：
51 |
52 | ```
53 | python detect.py --weights yolov5s.pt --source data/images
54 | ```
55 |
56 | 效果不错：
57 |
58 |
59 | 
60 |
61 |
62 |
63 |
64 | 导出 onnx，并用 onnx-simplifer 简化模型，这里稍微不同，如果按照[详细记录u版YOLOv5目标检测ncnn实现](https://zhuanlan.zhihu.com/p/275989233?utm_source=qq),那么直接导出来的模型可以看到输出:
65 |
66 | ```
67 | python models/export.py --weights yolov5s.pt --img 640 --batch 1
68 | ```
69 |
70 |
71 | 
72 |
73 |
74 |
75 |
76 | 可以看到后处理怎么都出来了？？？
77 |
78 | 看看models/yolo.py代码发现：
79 |
80 |
81 | 
82 |
83 |
84 | inference里面不就对应上面onnx模型那部分输出处理后然后torch.cat起来么，这部分处理我们放在代码里面做，所以可以注释这部分：
85 |
86 |
87 | 
88 |
89 |
90 | 这样导出来的模型就是三个输出了：
91 |
92 |
93 | 
94 |
95 |
96 |
97 |
98 | ok,输出和[详细记录u版YOLOv5目标检测ncnn实现](https://zhuanlan.zhihu.com/p/275989233?utm_source=qq)对应上了，同时可以看到激活函数silu：
99 |
100 |
101 | 
102 |
103 |
104 | 经过onnx-sim简化一下：
105 |
106 | ```
107 | python -m onnxsim yolov5s.onnx yolov5s-sim.onnx
108 | ```
109 |
110 | ## 转换和实现focus模块等
111 |
112 | 后续和[详细记录u版YOLOv5目标检测ncnn实现](https://zhuanlan.zhihu.com/p/275989233?utm_source=qq)一样，ncnn转化后激活函数转为swish,可swish的实现：
113 |
114 | ```c++
115 | Swish::Swish()
116 | {
117 | one_blob_only = true;
118 | support_inplace = true;
119 | }
120 |
121 | int Swish::forward_inplace(Mat& bottom_top_blob, const Option& opt) const
122 | {
123 | int w = bottom_top_blob.w;
124 | int h = bottom_top_blob.h;
125 | int channels = bottom_top_blob.c;
126 | int size = w * h;
127 |
128 | #pragma omp parallel for num_threads(opt.num_threads)
129 | for (int q = 0; q < channels; q++)
130 | {
131 | float* ptr = bottom_top_blob.channel(q);
132 |
133 | for (int i = 0; i < size; i++)
134 | {
135 | float x = ptr[i];
136 | ptr[i] = static_cast(x / (1.f + expf(-x)));
137 | }
138 | }
139 |
140 | return 0;
141 | }
142 |
143 | } // namespace ncnn
144 | ```
145 |
146 | 和silu一样，那么就可以正常进行推理了，可能需要注意的就是三个输出节点不要弄错就ok。
147 |
148 |
149 |
150 | ## INT8量化
151 |
152 | ### 1. NCNN源码编译
153 |
154 | 1. 最新ncnn新增支持int8量化
155 |
156 | ```
157 | git clone https://github.com/Tencent/ncnn.git
158 | cd ncnn
159 | git submodule update --init
160 | ```
161 |
162 | 2. 增加Focus层
163 |
164 | 将layer/yolov5focus.cpp/.h 两个文件放在ncnn/src/layer下，然后ncnn/src/CMakeList.txt增加：
165 |
166 | ```
167 | ncnn_add_layer(YoloV5Focus)
168 | ```
169 |
170 | 3. 编译
171 |
172 | 不同系统的编译见https://github.com/Tencent/ncnn/wiki/how-to-build
173 |
174 | 编译完成后会有：
175 |
176 | ncnn/build/tools/onnx/onnx2ncnn ：用于onnx转ncnn的工具
177 |
178 | /ncnn/build/tools/ncnnoptimize 优化工具
179 |
180 | ncnn/build/tools/quantize/ncnn2int8 转int8
181 |
182 | ncnn/build/tools/quantize/ncnn2table 生成校准表
183 |
184 | ### 2. 模型量化
185 |
186 | 参考https://github.com/Tencent/ncnn/wiki/quantized-int8-inference 操作，在ncnn2table工具下，准备我们的检验图片放在images文件夹下，最好是我们训练模型的验证或者测试集，这里使用coco val数据集5k张图片。
187 |
188 | 然后
189 |
190 | ```
191 | find images/ -type f > imagelist.txt
192 | ./ncnn2table yolov5s-opt.param yolov5s-opt.bin imagelist.txt yolov5s.table mean=[0,0,0] norm=[0.0039215,0.0039215,0.0039215] shape=[416,416,3] pixel=BGR thread=8 method=kl
193 | ```
194 |
195 |
196 | 
197 |
198 | 校准表见INT8/yolov5s.table。
199 |
200 | 然后转化模型：
201 |
202 | ```
203 | ./ncnn2int8 yolov5s-opt.param yolov5s-opt.bn yolov5s-int8.param yolov5s-int8.bin yolov5s.table
204 | ```
205 |
206 | 转化后的int8模型见yolov5s-int8.param和yolov5s-int8.bin。
207 |
208 | ### 3. INT8 推理
209 |
210 | 使用最新的动态库：https://github.com/Tencent/ncnn/releases/tag/20210525
211 |
212 | 根据你的系统选择。
213 |
214 | 相关设置：
215 |
216 | ```
217 | yolov5.opt.num_threads = 8;
218 | yolov5.opt.use_int8_inference = true;
219 | ```
220 |
221 | FP16模型和INT8模型对比：
222 |
223 |
224 | 
225 |
226 |
227 |
228 | kl和aciq两种量化方式，前者损失会更大。
229 |
230 |
231 | | | input | inference time | model size |
232 | | ------------- | ----- | -------------- | ---------- |
233 | | yolov5s | 416 | 22 ms | 14.6 M(fp16) |
234 | | yolov5s-prune | 416 | 18 ms | 1.7 M(fp16) |
235 | | yolov5s-int8 | 416 | 52ms | 887.5k(int8) |
236 |
237 | 目前int8量化后效果还不错，但是推理时间慢了很多，可能原因是int8不针对x86，参考https://github.com/Tencent/ncnn/issues/2974
238 | 后续测试arm上加速效果
239 |
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/build/CMakeCache.txt:
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1 | # This is the CMakeCache file.
2 | # For build in directory: /home/kong/yolov5_prune/yolov5_ncnn/build
3 | # It was generated by CMake: /usr/bin/cmake
4 | # You can edit this file to change values found and used by cmake.
5 | # If you do not want to change any of the values, simply exit the editor.
6 | # If you do want to change a value, simply edit, save, and exit the editor.
7 | # The syntax for the file is as follows:
8 | # KEY:TYPE=VALUE
9 | # KEY is the name of a variable in the cache.
10 | # TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
11 | # VALUE is the current value for the KEY.
12 |
13 | ########################
14 | # EXTERNAL cache entries
15 | ########################
16 |
17 | //Path to a program.
18 | CMAKE_AR:FILEPATH=/usr/bin/ar
19 |
20 | //Choose the type of build, options are: None(CMAKE_CXX_FLAGS or
21 | // CMAKE_C_FLAGS used) Debug Release RelWithDebInfo MinSizeRel.
22 | CMAKE_BUILD_TYPE:STRING=
23 |
24 | //Enable/Disable color output during build.
25 | CMAKE_COLOR_MAKEFILE:BOOL=ON
26 |
27 | //CXX compiler
28 | CMAKE_CXX_COMPILER:FILEPATH=/usr/bin/c++
29 |
30 | //Flags used by the compiler during all build types.
31 | CMAKE_CXX_FLAGS:STRING=
32 |
33 | //Flags used by the compiler during debug builds.
34 | CMAKE_CXX_FLAGS_DEBUG:STRING=-g
35 |
36 | //Flags used by the compiler during release builds for minimum
37 | // size.
38 | CMAKE_CXX_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
39 |
40 | //Flags used by the compiler during release builds.
41 | CMAKE_CXX_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
42 |
43 | //Flags used by the compiler during release builds with debug info.
44 | CMAKE_CXX_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
45 |
46 | //C compiler
47 | CMAKE_C_COMPILER:FILEPATH=/usr/bin/cc
48 |
49 | //Flags used by the compiler during all build types.
50 | CMAKE_C_FLAGS:STRING=
51 |
52 | //Flags used by the compiler during debug builds.
53 | CMAKE_C_FLAGS_DEBUG:STRING=-g
54 |
55 | //Flags used by the compiler during release builds for minimum
56 | // size.
57 | CMAKE_C_FLAGS_MINSIZEREL:STRING=-Os -DNDEBUG
58 |
59 | //Flags used by the compiler during release builds.
60 | CMAKE_C_FLAGS_RELEASE:STRING=-O3 -DNDEBUG
61 |
62 | //Flags used by the compiler during release builds with debug info.
63 | CMAKE_C_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
64 |
65 | //Flags used by the linker.
66 | CMAKE_EXE_LINKER_FLAGS:STRING=
67 |
68 | //Flags used by the linker during debug builds.
69 | CMAKE_EXE_LINKER_FLAGS_DEBUG:STRING=
70 |
71 | //Flags used by the linker during release minsize builds.
72 | CMAKE_EXE_LINKER_FLAGS_MINSIZEREL:STRING=
73 |
74 | //Flags used by the linker during release builds.
75 | CMAKE_EXE_LINKER_FLAGS_RELEASE:STRING=
76 |
77 | //Flags used by the linker during Release with Debug Info builds.
78 | CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFO:STRING=
79 |
80 | //Enable/Disable output of compile commands during generation.
81 | CMAKE_EXPORT_COMPILE_COMMANDS:BOOL=OFF
82 |
83 | //Install path prefix, prepended onto install directories.
84 | CMAKE_INSTALL_PREFIX:PATH=/usr/local
85 |
86 | //Path to a program.
87 | CMAKE_LINKER:FILEPATH=/usr/bin/ld
88 |
89 | //Path to a program.
90 | CMAKE_MAKE_PROGRAM:FILEPATH=/usr/bin/make
91 |
92 | //Flags used by the linker during the creation of modules.
93 | CMAKE_MODULE_LINKER_FLAGS:STRING=
94 |
95 | //Flags used by the linker during debug builds.
96 | CMAKE_MODULE_LINKER_FLAGS_DEBUG:STRING=
97 |
98 | //Flags used by the linker during release minsize builds.
99 | CMAKE_MODULE_LINKER_FLAGS_MINSIZEREL:STRING=
100 |
101 | //Flags used by the linker during release builds.
102 | CMAKE_MODULE_LINKER_FLAGS_RELEASE:STRING=
103 |
104 | //Flags used by the linker during Release with Debug Info builds.
105 | CMAKE_MODULE_LINKER_FLAGS_RELWITHDEBINFO:STRING=
106 |
107 | //Path to a program.
108 | CMAKE_NM:FILEPATH=/usr/bin/nm
109 |
110 | //Path to a program.
111 | CMAKE_OBJCOPY:FILEPATH=/usr/bin/objcopy
112 |
113 | //Path to a program.
114 | CMAKE_OBJDUMP:FILEPATH=/usr/bin/objdump
115 |
116 | //Value Computed by CMake
117 | CMAKE_PROJECT_NAME:STATIC=yolov5s
118 |
119 | //Path to a program.
120 | CMAKE_RANLIB:FILEPATH=/usr/bin/ranlib
121 |
122 | //Flags used by the linker during the creation of dll's.
123 | CMAKE_SHARED_LINKER_FLAGS:STRING=
124 |
125 | //Flags used by the linker during debug builds.
126 | CMAKE_SHARED_LINKER_FLAGS_DEBUG:STRING=
127 |
128 | //Flags used by the linker during release minsize builds.
129 | CMAKE_SHARED_LINKER_FLAGS_MINSIZEREL:STRING=
130 |
131 | //Flags used by the linker during release builds.
132 | CMAKE_SHARED_LINKER_FLAGS_RELEASE:STRING=
133 |
134 | //Flags used by the linker during Release with Debug Info builds.
135 | CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFO:STRING=
136 |
137 | //If set, runtime paths are not added when installing shared libraries,
138 | // but are added when building.
139 | CMAKE_SKIP_INSTALL_RPATH:BOOL=NO
140 |
141 | //If set, runtime paths are not added when using shared libraries.
142 | CMAKE_SKIP_RPATH:BOOL=NO
143 |
144 | //Flags used by the linker during the creation of static libraries.
145 | CMAKE_STATIC_LINKER_FLAGS:STRING=
146 |
147 | //Flags used by the linker during debug builds.
148 | CMAKE_STATIC_LINKER_FLAGS_DEBUG:STRING=
149 |
150 | //Flags used by the linker during release minsize builds.
151 | CMAKE_STATIC_LINKER_FLAGS_MINSIZEREL:STRING=
152 |
153 | //Flags used by the linker during release builds.
154 | CMAKE_STATIC_LINKER_FLAGS_RELEASE:STRING=
155 |
156 | //Flags used by the linker during Release with Debug Info builds.
157 | CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING=
158 |
159 | //Path to a program.
160 | CMAKE_STRIP:FILEPATH=/usr/bin/strip
161 |
162 | //If this value is on, makefiles will be generated without the
163 | // .SILENT directive, and all commands will be echoed to the console
164 | // during the make. This is useful for debugging only. With Visual
165 | // Studio IDE projects all commands are done without /nologo.
166 | CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE
167 |
168 | //The directory containing a CMake configuration file for OpenCV.
169 | OpenCV_DIR:PATH=/usr/local/share/OpenCV
170 |
171 | //C++ compiler flags for OpenMP parallization
172 | OpenMP_CXX_FLAGS:STRING=-fopenmp
173 |
174 | //C compiler flags for OpenMP parallization
175 | OpenMP_C_FLAGS:STRING=-fopenmp
176 |
177 | //Value Computed by CMake
178 | yolov5s_BINARY_DIR:STATIC=/home/kong/yolov5_prune/yolov5_ncnn/build
179 |
180 | //Value Computed by CMake
181 | yolov5s_SOURCE_DIR:STATIC=/home/kong/yolov5_prune/yolov5_ncnn
182 |
183 |
184 | ########################
185 | # INTERNAL cache entries
186 | ########################
187 |
188 | //ADVANCED property for variable: CMAKE_AR
189 | CMAKE_AR-ADVANCED:INTERNAL=1
190 | //This is the directory where this CMakeCache.txt was created
191 | CMAKE_CACHEFILE_DIR:INTERNAL=/home/kong/yolov5_prune/yolov5_ncnn/build
192 | //Major version of cmake used to create the current loaded cache
193 | CMAKE_CACHE_MAJOR_VERSION:INTERNAL=3
194 | //Minor version of cmake used to create the current loaded cache
195 | CMAKE_CACHE_MINOR_VERSION:INTERNAL=5
196 | //Patch version of cmake used to create the current loaded cache
197 | CMAKE_CACHE_PATCH_VERSION:INTERNAL=1
198 | //ADVANCED property for variable: CMAKE_COLOR_MAKEFILE
199 | CMAKE_COLOR_MAKEFILE-ADVANCED:INTERNAL=1
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38 | # The shell in which to execute make rules.
39 | SHELL = /bin/sh
40 |
41 | # The CMake executable.
42 | CMAKE_COMMAND = /usr/bin/cmake
43 |
44 | # The command to remove a file.
45 | RM = /usr/bin/cmake -E remove -f
46 |
47 | # Escaping for special characters.
48 | EQUALS = =
49 |
50 | # The top-level source directory on which CMake was run.
51 | CMAKE_SOURCE_DIR = /home/kong/yolov5_prune/yolov5_ncnn
52 |
53 | # The top-level build directory on which CMake was run.
54 | CMAKE_BINARY_DIR = /home/kong/yolov5_prune/yolov5_ncnn/build
55 |
56 | #=============================================================================
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118 | $(MAKE) -f CMakeFiles/Makefile2 yolov5s
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156 | @echo "... all (the default if no target is provided)"
157 | @echo "... clean"
158 | @echo "... depend"
159 | @echo "... edit_cache"
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174 | # because they might be regenerated.
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1 | # Install script for directory: /home/kong/yolov5_prune/yolov5_ncnn
2 |
3 | # Set the install prefix
4 | if(NOT DEFINED CMAKE_INSTALL_PREFIX)
5 | set(CMAKE_INSTALL_PREFIX "/usr/local")
6 | endif()
7 | string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}")
8 |
9 | # Set the install configuration name.
10 | if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)
11 | if(BUILD_TYPE)
12 | string(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
13 | CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}")
14 | else()
15 | set(CMAKE_INSTALL_CONFIG_NAME "")
16 | endif()
17 | message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
18 | endif()
19 |
20 | # Set the component getting installed.
21 | if(NOT CMAKE_INSTALL_COMPONENT)
22 | if(COMPONENT)
23 | message(STATUS "Install component: \"${COMPONENT}\"")
24 | set(CMAKE_INSTALL_COMPONENT "${COMPONENT}")
25 | else()
26 | set(CMAKE_INSTALL_COMPONENT)
27 | endif()
28 | endif()
29 |
30 | # Install shared libraries without execute permission?
31 | if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
32 | set(CMAKE_INSTALL_SO_NO_EXE "1")
33 | endif()
34 |
35 | if(CMAKE_INSTALL_COMPONENT)
36 | set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt")
37 | else()
38 | set(CMAKE_INSTALL_MANIFEST "install_manifest.txt")
39 | endif()
40 |
41 | string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT
42 | "${CMAKE_INSTALL_MANIFEST_FILES}")
43 | file(WRITE "/home/kong/yolov5_prune/yolov5_ncnn/build/${CMAKE_INSTALL_MANIFEST}"
44 | "${CMAKE_INSTALL_MANIFEST_CONTENT}")
45 |
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1 | import os
2 | import cv2
3 | import numpy as np
4 | im1 = cv2.imread("yolov5_ncnn_fp16.jpg")
5 | im2 = cv2.imread("yolov5-int8-kl.jpg")
6 | im3 = cv2.imread("yolov5-int8-aciq.jpg")
7 |
8 | im1 = cv2.putText(im1, "NCNN FP16", (100,100), cv2.FONT_HERSHEY_PLAIN, 4, (0,255,0), 3)
9 | im2 = cv2.putText(im2, "NCNN INT8 KL", (100,100), cv2.FONT_HERSHEY_PLAIN, 4, (0,255,0), 3)
10 | im3 = cv2.putText(im3, "NCNN INT8 ACIQ", (100,100), cv2.FONT_HERSHEY_PLAIN, 4, (0,255,0), 3)
11 | im = np.hstack([im1,im2,im3])
12 |
13 | cv2.imwrite("cat.jpg",im)
14 |
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #include "yolov5focus.h"
16 |
17 | #include
18 |
19 | namespace ncnn {
20 |
21 | YoloV5Focus::YoloV5Focus()
22 | {
23 | one_blob_only = true;
24 | }
25 |
26 | int YoloV5Focus::forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const
27 | {
28 | int w = bottom_blob.w;
29 | int h = bottom_blob.h;
30 | int channels = bottom_blob.c;
31 |
32 | int outw = w / 2;
33 | int outh = h / 2;
34 | int outc = channels * 4;
35 |
36 | top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);
37 | if (top_blob.empty())
38 | return -100;
39 |
40 | #pragma omp parallel for num_threads(opt.num_threads)
41 | for (int p = 0; p < outc; p++)
42 | {
43 | const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2);
44 | float* outptr = top_blob.channel(p);
45 |
46 | for (int i = 0; i < outh; i++)
47 | {
48 | for (int j = 0; j < outw; j++)
49 | {
50 | *outptr = *ptr;
51 |
52 | outptr += 1;
53 | ptr += 2;
54 | }
55 |
56 | ptr += w;
57 | }
58 | }
59 |
60 | return 0;
61 | }
62 | } // namespace ncnn
63 |
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/layer/yolov5focus.h:
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef LAYER_YOLOV5FOCUS_H
16 | #define LAYER_YOLOV5FOCUS_H
17 |
18 | #include "layer.h"
19 |
20 | namespace ncnn {
21 |
22 | class YoloV5Focus : public Layer
23 | {
24 | public:
25 | YoloV5Focus();
26 |
27 | virtual int forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const;
28 | };
29 |
30 | } // namespace ncnn
31 |
32 | #endif // LAYER_YOLOV5FOCUS_H
33 |
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_BENCHMARK_H
16 | #define NCNN_BENCHMARK_H
17 |
18 | #include "layer.h"
19 | #include "mat.h"
20 | #include "platform.h"
21 |
22 | namespace ncnn {
23 |
24 | // get now timestamp in ms
25 | NCNN_EXPORT double get_current_time();
26 |
27 | #if NCNN_BENCHMARK
28 |
29 | NCNN_EXPORT void benchmark(const Layer* layer, double start, double end);
30 | NCNN_EXPORT void benchmark(const Layer* layer, const Mat& bottom_blob, Mat& top_blob, double start, double end);
31 |
32 | #endif // NCNN_BENCHMARK
33 |
34 | } // namespace ncnn
35 |
36 | #endif // NCNN_BENCHMARK_H
37 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/blob.h:
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_BLOB_H
16 | #define NCNN_BLOB_H
17 |
18 | #include "mat.h"
19 | #include "platform.h"
20 |
21 | namespace ncnn {
22 |
23 | class NCNN_EXPORT Blob
24 | {
25 | public:
26 | // empty
27 | Blob();
28 |
29 | public:
30 | #if NCNN_STRING
31 | // blob name
32 | std::string name;
33 | #endif // NCNN_STRING
34 | // layer index which produce this blob as output
35 | int producer;
36 | // layer index which need this blob as input
37 | int consumer;
38 | // shape hint
39 | Mat shape;
40 | };
41 |
42 | } // namespace ncnn
43 |
44 | #endif // NCNN_BLOB_H
45 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/command.h:
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_COMMAND_H
16 | #define NCNN_COMMAND_H
17 |
18 | #include "platform.h"
19 |
20 | #if NCNN_VULKAN
21 |
22 | #include "mat.h"
23 |
24 | #include
25 |
26 | namespace ncnn {
27 |
28 | class Pipeline;
29 | #if NCNN_PLATFORM_API
30 | #if __ANDROID_API__ >= 26
31 | class ImportAndroidHardwareBufferPipeline;
32 | #endif // __ANDROID_API__ >= 26
33 | #endif // NCNN_PLATFORM_API
34 | class VkComputePrivate;
35 | class NCNN_EXPORT VkCompute
36 | {
37 | public:
38 | explicit VkCompute(const VulkanDevice* vkdev);
39 | virtual ~VkCompute();
40 |
41 | public:
42 | void record_upload(const Mat& src, VkMat& dst, const Option& opt);
43 |
44 | void record_upload(const Mat& src, VkImageMat& dst, const Option& opt);
45 |
46 | void record_download(const VkMat& src, Mat& dst, const Option& opt);
47 |
48 | void record_download(const VkImageMat& src, Mat& dst, const Option& opt);
49 |
50 | void record_buffer_to_image(const VkMat& src, VkImageMat& dst, const Option& opt);
51 |
52 | void record_image_to_buffer(const VkImageMat& src, VkMat& dst, const Option& opt);
53 |
54 | void record_clone(const Mat& src, VkMat& dst, const Option& opt);
55 |
56 | void record_clone(const Mat& src, VkImageMat& dst, const Option& opt);
57 |
58 | void record_clone(const VkMat& src, Mat& dst, const Option& opt);
59 |
60 | void record_clone(const VkImageMat& src, Mat& dst, const Option& opt);
61 |
62 | void record_clone(const VkMat& src, VkMat& dst, const Option& opt);
63 |
64 | void record_clone(const VkImageMat& src, VkImageMat& dst, const Option& opt);
65 |
66 | void record_clone(const VkMat& src, VkImageMat& dst, const Option& opt);
67 |
68 | void record_clone(const VkImageMat& src, VkMat& dst, const Option& opt);
69 |
70 | void record_pipeline(const Pipeline* pipeline, const std::vector& bindings, const std::vector& constants, const VkMat& dispatcher);
71 |
72 | void record_pipeline(const Pipeline* pipeline, const std::vector& bindings, const std::vector& constants, const VkImageMat& dispatcher);
73 |
74 | void record_pipeline(const Pipeline* pipeline, const std::vector& buffer_bindings, const std::vector& image_bindings, const std::vector& constants, const VkMat& dispatcher);
75 | void record_pipeline(const Pipeline* pipeline, const std::vector& buffer_bindings, const std::vector& image_bindings, const std::vector& constants, const VkImageMat& dispatcher);
76 | void record_pipeline(const Pipeline* pipeline, const std::vector& buffer_bindings, const std::vector& image_bindings, const std::vector& constants, const Mat& dispatcher);
77 |
78 | #if NCNN_BENCHMARK
79 | void record_write_timestamp(uint32_t query);
80 | #endif // NCNN_BENCHMARK
81 |
82 | #if NCNN_PLATFORM_API
83 | #if __ANDROID_API__ >= 26
84 | void record_import_android_hardware_buffer(const ImportAndroidHardwareBufferPipeline* pipeline, const VkImageMat& src, const VkMat& dst);
85 |
86 | void record_import_android_hardware_buffer(const ImportAndroidHardwareBufferPipeline* pipeline, const VkImageMat& src, const VkImageMat& dst);
87 | #endif // __ANDROID_API__ >= 26
88 | #endif // NCNN_PLATFORM_API
89 |
90 | int submit_and_wait();
91 |
92 | int reset();
93 |
94 | #if NCNN_BENCHMARK
95 | int create_query_pool(uint32_t query_count);
96 |
97 | int get_query_pool_results(uint32_t first_query, uint32_t query_count, std::vector& results);
98 | #endif // NCNN_BENCHMARK
99 |
100 | protected:
101 | const VulkanDevice* vkdev;
102 |
103 | void barrier_readwrite(const VkMat& binding);
104 | void barrier_readwrite(const VkImageMat& binding);
105 | void barrier_readonly(const VkImageMat& binding);
106 |
107 | private:
108 | VkComputePrivate* const d;
109 | };
110 |
111 | class VkTransferPrivate;
112 | class NCNN_EXPORT VkTransfer
113 | {
114 | public:
115 | explicit VkTransfer(const VulkanDevice* vkdev);
116 | virtual ~VkTransfer();
117 |
118 | public:
119 | void record_upload(const Mat& src, VkMat& dst, const Option& opt, bool flatten = true);
120 |
121 | void record_upload(const Mat& src, VkImageMat& dst, const Option& opt);
122 |
123 | int submit_and_wait();
124 |
125 | protected:
126 | const VulkanDevice* vkdev;
127 |
128 | private:
129 | VkTransferPrivate* const d;
130 | };
131 |
132 | } // namespace ncnn
133 |
134 | #endif // NCNN_VULKAN
135 |
136 | #endif // NCNN_COMMAND_H
137 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/cpu.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_CPU_H
16 | #define NCNN_CPU_H
17 |
18 | #include
19 |
20 | #if defined __ANDROID__ || defined __linux__
21 | #include // cpu_set_t
22 | #endif
23 |
24 | #include "platform.h"
25 |
26 | namespace ncnn {
27 |
28 | class NCNN_EXPORT CpuSet
29 | {
30 | public:
31 | CpuSet();
32 | void enable(int cpu);
33 | void disable(int cpu);
34 | void disable_all();
35 | bool is_enabled(int cpu) const;
36 | int num_enabled() const;
37 |
38 | public:
39 | #if defined __ANDROID__ || defined __linux__
40 | cpu_set_t cpu_set;
41 | #endif
42 | #if __APPLE__
43 | unsigned int policy;
44 | #endif
45 | };
46 |
47 | // test optional cpu features
48 | // neon = armv7 neon or aarch64 asimd
49 | NCNN_EXPORT int cpu_support_arm_neon();
50 | // vfpv4 = armv7 fp16 + fma
51 | NCNN_EXPORT int cpu_support_arm_vfpv4();
52 | // asimdhp = aarch64 asimd half precision
53 | NCNN_EXPORT int cpu_support_arm_asimdhp();
54 | // asimddp = aarch64 asimd dot product
55 | NCNN_EXPORT int cpu_support_arm_asimddp();
56 |
57 | // avx2 = x86_64 avx2 + fma + f16c
58 | NCNN_EXPORT int cpu_support_x86_avx2();
59 |
60 | // v = riscv vector
61 | NCNN_EXPORT int cpu_support_riscv_v();
62 | // zfh = riscv half-precision float
63 | NCNN_EXPORT int cpu_support_riscv_zfh();
64 | // vlenb = riscv vector length in bytes
65 | NCNN_EXPORT int cpu_riscv_vlenb();
66 |
67 | // cpu info
68 | NCNN_EXPORT int get_cpu_count();
69 | NCNN_EXPORT int get_little_cpu_count();
70 | NCNN_EXPORT int get_big_cpu_count();
71 |
72 | // bind all threads on little clusters if powersave enabled
73 | // affects HMP arch cpu like ARM big.LITTLE
74 | // only implemented on android at the moment
75 | // switching powersave is expensive and not thread-safe
76 | // 0 = all cores enabled(default)
77 | // 1 = only little clusters enabled
78 | // 2 = only big clusters enabled
79 | // return 0 if success for setter function
80 | NCNN_EXPORT int get_cpu_powersave();
81 | NCNN_EXPORT int set_cpu_powersave(int powersave);
82 |
83 | // convenient wrapper
84 | NCNN_EXPORT const CpuSet& get_cpu_thread_affinity_mask(int powersave);
85 |
86 | // set explicit thread affinity
87 | NCNN_EXPORT int set_cpu_thread_affinity(const CpuSet& thread_affinity_mask);
88 |
89 | // misc function wrapper for openmp routines
90 | NCNN_EXPORT int get_omp_num_threads();
91 | NCNN_EXPORT void set_omp_num_threads(int num_threads);
92 |
93 | NCNN_EXPORT int get_omp_dynamic();
94 | NCNN_EXPORT void set_omp_dynamic(int dynamic);
95 |
96 | NCNN_EXPORT int get_omp_thread_num();
97 |
98 | NCNN_EXPORT int get_kmp_blocktime();
99 | NCNN_EXPORT void set_kmp_blocktime(int time_ms);
100 |
101 | // need to flush denormals on Intel Chipset.
102 | // Other architectures such as ARM can be added as needed.
103 | // 0 = DAZ OFF, FTZ OFF
104 | // 1 = DAZ ON , FTZ OFF
105 | // 2 = DAZ OFF, FTZ ON
106 | // 3 = DAZ ON, FTZ ON
107 | NCNN_EXPORT int get_flush_denormals();
108 | NCNN_EXPORT int set_flush_denormals(int flush_denormals);
109 |
110 | } // namespace ncnn
111 |
112 | #endif // NCNN_CPU_H
113 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/datareader.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_DATAREADER_H
16 | #define NCNN_DATAREADER_H
17 |
18 | #include "platform.h"
19 | #if NCNN_STDIO
20 | #include
21 | #endif
22 |
23 | #if NCNN_PLATFORM_API
24 | #if __ANDROID_API__ >= 9
25 | #include
26 | #endif
27 | #endif // NCNN_PLATFORM_API
28 |
29 | namespace ncnn {
30 |
31 | // data read wrapper
32 | class NCNN_EXPORT DataReader
33 | {
34 | public:
35 | DataReader();
36 | virtual ~DataReader();
37 |
38 | #if NCNN_STRING
39 | // parse plain param text
40 | // return 1 if scan success
41 | virtual int scan(const char* format, void* p) const;
42 | #endif // NCNN_STRING
43 |
44 | // read binary param and model data
45 | // return bytes read
46 | virtual size_t read(void* buf, size_t size) const;
47 | };
48 |
49 | #if NCNN_STDIO
50 | class DataReaderFromStdioPrivate;
51 | class NCNN_EXPORT DataReaderFromStdio : public DataReader
52 | {
53 | public:
54 | explicit DataReaderFromStdio(FILE* fp);
55 | virtual ~DataReaderFromStdio();
56 |
57 | #if NCNN_STRING
58 | virtual int scan(const char* format, void* p) const;
59 | #endif // NCNN_STRING
60 | virtual size_t read(void* buf, size_t size) const;
61 |
62 | private:
63 | DataReaderFromStdio(const DataReaderFromStdio&);
64 | DataReaderFromStdio& operator=(const DataReaderFromStdio&);
65 |
66 | private:
67 | DataReaderFromStdioPrivate* const d;
68 | };
69 | #endif // NCNN_STDIO
70 |
71 | class DataReaderFromMemoryPrivate;
72 | class NCNN_EXPORT DataReaderFromMemory : public DataReader
73 | {
74 | public:
75 | explicit DataReaderFromMemory(const unsigned char*& mem);
76 | virtual ~DataReaderFromMemory();
77 |
78 | #if NCNN_STRING
79 | virtual int scan(const char* format, void* p) const;
80 | #endif // NCNN_STRING
81 | virtual size_t read(void* buf, size_t size) const;
82 |
83 | private:
84 | DataReaderFromMemory(const DataReaderFromMemory&);
85 | DataReaderFromMemory& operator=(const DataReaderFromMemory&);
86 |
87 | private:
88 | DataReaderFromMemoryPrivate* const d;
89 | };
90 |
91 | #if NCNN_PLATFORM_API
92 | #if __ANDROID_API__ >= 9
93 | class DataReaderFromAndroidAssetPrivate;
94 | class NCNN_EXPORT DataReaderFromAndroidAsset : public DataReader
95 | {
96 | public:
97 | explicit DataReaderFromAndroidAsset(AAsset* asset);
98 | virtual ~DataReaderFromAndroidAsset();
99 |
100 | #if NCNN_STRING
101 | virtual int scan(const char* format, void* p) const;
102 | #endif // NCNN_STRING
103 | virtual size_t read(void* buf, size_t size) const;
104 |
105 | private:
106 | DataReaderFromAndroidAsset(const DataReaderFromAndroidAsset&);
107 | DataReaderFromAndroidAsset& operator=(const DataReaderFromAndroidAsset&);
108 |
109 | private:
110 | DataReaderFromAndroidAssetPrivate* const d;
111 | };
112 | #endif // __ANDROID_API__ >= 9
113 | #endif // NCNN_PLATFORM_API
114 |
115 | } // namespace ncnn
116 |
117 | #endif // NCNN_DATAREADER_H
118 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/layer.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_LAYER_H
16 | #define NCNN_LAYER_H
17 |
18 | #include "mat.h"
19 | #include "modelbin.h"
20 | #include "option.h"
21 | #include "paramdict.h"
22 | #include "platform.h"
23 |
24 | #include
25 |
26 | #if NCNN_VULKAN
27 | #include "command.h"
28 | #include "pipeline.h"
29 |
30 | #include
31 | #endif // NCNN_VULKAN
32 |
33 | namespace ncnn {
34 |
35 | class NCNN_EXPORT Layer
36 | {
37 | public:
38 | // empty
39 | Layer();
40 | // virtual destructor
41 | virtual ~Layer();
42 |
43 | // load layer specific parameter from parsed dict
44 | // return 0 if success
45 | virtual int load_param(const ParamDict& pd);
46 |
47 | // load layer specific weight data from model binary
48 | // return 0 if success
49 | virtual int load_model(const ModelBin& mb);
50 |
51 | // layer implementation specific setup
52 | // return 0 if success
53 | virtual int create_pipeline(const Option& opt);
54 |
55 | // layer implementation specific clean
56 | // return 0 if success
57 | virtual int destroy_pipeline(const Option& opt);
58 |
59 | public:
60 | // one input and one output blob
61 | bool one_blob_only;
62 |
63 | // support inplace inference
64 | bool support_inplace;
65 |
66 | // support vulkan compute
67 | bool support_vulkan;
68 |
69 | // accept input blob with packed storage
70 | bool support_packing;
71 |
72 | // accept bf16
73 | bool support_bf16_storage;
74 |
75 | // accept fp16
76 | bool support_fp16_storage;
77 |
78 | // accept int8
79 | bool support_int8_storage;
80 |
81 | // shader image storage
82 | bool support_image_storage;
83 |
84 | // shader tensor storage
85 | bool support_tensor_storage;
86 |
87 | // TODO drop these fields
88 | bool support_weight_fp16_storage;
89 |
90 | bool support_reserved_0;
91 | bool support_reserved_1;
92 | bool support_reserved_2;
93 | bool support_reserved_3;
94 | bool support_reserved_4;
95 | bool support_reserved_5;
96 | bool support_reserved_6;
97 | bool support_reserved_7;
98 | bool support_reserved_8;
99 | bool support_reserved_9;
100 | bool support_reserved_10;
101 | bool support_reserved_11;
102 | bool support_reserved_12;
103 | bool support_reserved_13;
104 |
105 | public:
106 | // implement inference
107 | // return 0 if success
108 | virtual int forward(const std::vector& bottom_blobs, std::vector& top_blobs, const Option& opt) const;
109 | virtual int forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const;
110 |
111 | // implement inplace inference
112 | // return 0 if success
113 | virtual int forward_inplace(std::vector& bottom_top_blobs, const Option& opt) const;
114 | virtual int forward_inplace(Mat& bottom_top_blob, const Option& opt) const;
115 |
116 | #if NCNN_VULKAN
117 | public:
118 | // upload weight blob from host to device
119 | virtual int upload_model(VkTransfer& cmd, const Option& opt);
120 |
121 | public:
122 | // implement inference
123 | // return 0 if success
124 | virtual int forward(const std::vector& bottom_blobs, std::vector& top_blobs, VkCompute& cmd, const Option& opt) const;
125 | virtual int forward(const VkMat& bottom_blob, VkMat& top_blob, VkCompute& cmd, const Option& opt) const;
126 |
127 | // implement inference
128 | // return 0 if success
129 | virtual int forward(const std::vector& bottom_blobs, std::vector& top_blobs, VkCompute& cmd, const Option& opt) const;
130 | virtual int forward(const VkImageMat& bottom_blob, VkImageMat& top_blob, VkCompute& cmd, const Option& opt) const;
131 |
132 | // implement inplace inference
133 | // return 0 if success
134 | virtual int forward_inplace(std::vector& bottom_top_blobs, VkCompute& cmd, const Option& opt) const;
135 | virtual int forward_inplace(VkMat& bottom_top_blob, VkCompute& cmd, const Option& opt) const;
136 |
137 | // implement inplace inference
138 | // return 0 if success
139 | virtual int forward_inplace(std::vector& bottom_top_blobs, VkCompute& cmd, const Option& opt) const;
140 | virtual int forward_inplace(VkImageMat& bottom_top_blob, VkCompute& cmd, const Option& opt) const;
141 |
142 | public:
143 | // assigned immediately after creating this layer
144 | const VulkanDevice* vkdev;
145 | #endif // NCNN_VULKAN
146 |
147 | public:
148 | // custom user data
149 | void* userdata;
150 | // layer type index
151 | int typeindex;
152 | #if NCNN_STRING
153 | // layer type name
154 | std::string type;
155 | // layer name
156 | std::string name;
157 | #endif // NCNN_STRING
158 | // blob index which this layer needs as input
159 | std::vector bottoms;
160 | // blob index which this layer produces as output
161 | std::vector tops;
162 | // shape hint
163 | std::vector bottom_shapes;
164 | std::vector top_shapes;
165 | };
166 |
167 | // layer factory function
168 | typedef Layer* (*layer_creator_func)(void*);
169 | typedef void (*layer_destroyer_func)(Layer*, void*);
170 |
171 | struct layer_registry_entry
172 | {
173 | #if NCNN_STRING
174 | // layer type name
175 | const char* name;
176 | #endif // NCNN_STRING
177 | // layer factory entry
178 | layer_creator_func creator;
179 | };
180 |
181 | struct custom_layer_registry_entry
182 | {
183 | #if NCNN_STRING
184 | // layer type name
185 | const char* name;
186 | #endif // NCNN_STRING
187 | // layer factory entry
188 | layer_creator_func creator;
189 | layer_destroyer_func destroyer;
190 | void* userdata;
191 | };
192 |
193 | #if NCNN_STRING
194 | // get layer type from type name
195 | NCNN_EXPORT int layer_to_index(const char* type);
196 | // create layer from type name
197 | NCNN_EXPORT Layer* create_layer(const char* type);
198 | #endif // NCNN_STRING
199 | // create layer from layer type
200 | NCNN_EXPORT Layer* create_layer(int index);
201 |
202 | #define DEFINE_LAYER_CREATOR(name) \
203 | ::ncnn::Layer* name##_layer_creator(void* /*userdata*/) \
204 | { \
205 | return new name; \
206 | }
207 |
208 | #define DEFINE_LAYER_DESTROYER(name) \
209 | void name##_layer_destroyer(::ncnn::Layer* layer, void* /*userdata*/) \
210 | { \
211 | delete layer; \
212 | }
213 |
214 | } // namespace ncnn
215 |
216 | #endif // NCNN_LAYER_H
217 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/layer_shader_type.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_LAYER_SHADER_TYPE_H
16 | #define NCNN_LAYER_SHADER_TYPE_H
17 |
18 | namespace ncnn {
19 |
20 | namespace LayerShaderType {
21 | enum LayerShaderType
22 | {
23 | #include "layer_shader_type_enum.h"
24 | };
25 | } // namespace LayerShaderType
26 |
27 | } // namespace ncnn
28 |
29 | #endif // NCNN_LAYER_SHADER_TYPE_H
30 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/layer_shader_type_enum.h:
--------------------------------------------------------------------------------
1 | // Layer Shader Enum header
2 | //
3 | // This file is auto-generated by cmake, don't edit it.
4 |
5 | absval = 0,
6 | absval_pack4 = 1,
7 | absval_pack8 = 2,
8 | batchnorm = 3,
9 | batchnorm_pack4 = 4,
10 | batchnorm_pack8 = 5,
11 | concat = 6,
12 | concat_pack4 = 7,
13 | concat_pack4to1 = 8,
14 | concat_pack8 = 9,
15 | concat_pack8to1 = 10,
16 | concat_pack8to4 = 11,
17 | convolution = 12,
18 | convolution_1x1s1d1 = 13,
19 | convolution_pack1to4 = 14,
20 | convolution_pack1to8 = 15,
21 | convolution_pack4 = 16,
22 | convolution_pack4_1x1s1d1 = 17,
23 | convolution_pack4_3x3s1d1_winograd23_gemm = 18,
24 | convolution_pack4_3x3s1d1_winograd23_transform_input = 19,
25 | convolution_pack4_3x3s1d1_winograd23_transform_output = 20,
26 | convolution_pack4to1 = 21,
27 | convolution_pack4to8 = 22,
28 | convolution_pack8 = 23,
29 | convolution_pack8_1x1s1d1 = 24,
30 | convolution_pack8_3x3s1d1_winograd23_gemm = 25,
31 | convolution_pack8_3x3s1d1_winograd23_transform_input = 26,
32 | convolution_pack8_3x3s1d1_winograd23_transform_output = 27,
33 | convolution_pack8to1 = 28,
34 | convolution_pack8to4 = 29,
35 | crop = 30,
36 | crop_pack1to4 = 31,
37 | crop_pack1to8 = 32,
38 | crop_pack4 = 33,
39 | crop_pack4to1 = 34,
40 | crop_pack4to8 = 35,
41 | crop_pack8 = 36,
42 | crop_pack8to1 = 37,
43 | crop_pack8to4 = 38,
44 | deconvolution = 39,
45 | deconvolution_pack1to4 = 40,
46 | deconvolution_pack1to8 = 41,
47 | deconvolution_pack4 = 42,
48 | deconvolution_pack4to1 = 43,
49 | deconvolution_pack4to8 = 44,
50 | deconvolution_pack8 = 45,
51 | deconvolution_pack8to1 = 46,
52 | deconvolution_pack8to4 = 47,
53 | dropout = 48,
54 | dropout_pack4 = 49,
55 | dropout_pack8 = 50,
56 | eltwise = 51,
57 | eltwise_pack4 = 52,
58 | eltwise_pack8 = 53,
59 | flatten = 54,
60 | flatten_pack1to4 = 55,
61 | flatten_pack1to8 = 56,
62 | flatten_pack4 = 57,
63 | flatten_pack4to8 = 58,
64 | flatten_pack8 = 59,
65 | innerproduct = 60,
66 | innerproduct_gemm = 61,
67 | innerproduct_gemm_wp1to4 = 62,
68 | innerproduct_gemm_wp1to8 = 63,
69 | innerproduct_gemm_wp4 = 64,
70 | innerproduct_gemm_wp4to1 = 65,
71 | innerproduct_gemm_wp4to8 = 66,
72 | innerproduct_gemm_wp8 = 67,
73 | innerproduct_gemm_wp8to1 = 68,
74 | innerproduct_gemm_wp8to4 = 69,
75 | innerproduct_pack1to4 = 70,
76 | innerproduct_pack1to8 = 71,
77 | innerproduct_pack4 = 72,
78 | innerproduct_pack4to1 = 73,
79 | innerproduct_pack4to8 = 74,
80 | innerproduct_pack8 = 75,
81 | innerproduct_pack8to1 = 76,
82 | innerproduct_pack8to4 = 77,
83 | lrn_norm = 78,
84 | lrn_norm_across_channel_pack4 = 79,
85 | lrn_norm_across_channel_pack8 = 80,
86 | lrn_norm_within_channel_pack4 = 81,
87 | lrn_norm_within_channel_pack8 = 82,
88 | lrn_square_pad = 83,
89 | lrn_square_pad_across_channel_pack4 = 84,
90 | lrn_square_pad_across_channel_pack8 = 85,
91 | lrn_square_pad_within_channel_pack4 = 86,
92 | lrn_square_pad_within_channel_pack8 = 87,
93 | pooling = 88,
94 | pooling_adaptive = 89,
95 | pooling_adaptive_pack4 = 90,
96 | pooling_adaptive_pack8 = 91,
97 | pooling_global = 92,
98 | pooling_global_pack4 = 93,
99 | pooling_global_pack8 = 94,
100 | pooling_pack4 = 95,
101 | pooling_pack8 = 96,
102 | prelu = 97,
103 | prelu_pack4 = 98,
104 | prelu_pack8 = 99,
105 | relu = 100,
106 | relu_pack4 = 101,
107 | relu_pack8 = 102,
108 | reshape = 103,
109 | reshape_pack1to4 = 104,
110 | reshape_pack1to8 = 105,
111 | reshape_pack4 = 106,
112 | reshape_pack4to1 = 107,
113 | reshape_pack4to8 = 108,
114 | reshape_pack8 = 109,
115 | reshape_pack8to1 = 110,
116 | reshape_pack8to4 = 111,
117 | scale = 112,
118 | scale_pack4 = 113,
119 | scale_pack8 = 114,
120 | sigmoid = 115,
121 | sigmoid_pack4 = 116,
122 | sigmoid_pack8 = 117,
123 | slice = 118,
124 | slice_pack1to4 = 119,
125 | slice_pack1to8 = 120,
126 | slice_pack4 = 121,
127 | slice_pack4to8 = 122,
128 | slice_pack8 = 123,
129 | softmax_div_sum = 124,
130 | softmax_div_sum_pack4 = 125,
131 | softmax_div_sum_pack8 = 126,
132 | softmax_exp_sub_max = 127,
133 | softmax_exp_sub_max_pack4 = 128,
134 | softmax_exp_sub_max_pack8 = 129,
135 | softmax_reduce_max = 130,
136 | softmax_reduce_max_pack4 = 131,
137 | softmax_reduce_max_pack8 = 132,
138 | softmax_reduce_sum = 133,
139 | softmax_reduce_sum_pack4 = 134,
140 | softmax_reduce_sum_pack8 = 135,
141 | tanh = 136,
142 | tanh_pack4 = 137,
143 | tanh_pack8 = 138,
144 | binaryop = 139,
145 | binaryop_broadcast = 140,
146 | binaryop_broadcast_a1_pack4 = 141,
147 | binaryop_broadcast_a1_pack8 = 142,
148 | binaryop_broadcast_b1_pack4 = 143,
149 | binaryop_broadcast_b1_pack8 = 144,
150 | binaryop_broadcast_pack4 = 145,
151 | binaryop_broadcast_pack8 = 146,
152 | binaryop_pack4 = 147,
153 | binaryop_pack8 = 148,
154 | unaryop = 149,
155 | unaryop_pack4 = 150,
156 | unaryop_pack8 = 151,
157 | convolutiondepthwise = 152,
158 | convolutiondepthwise_group = 153,
159 | convolutiondepthwise_group_pack1to4 = 154,
160 | convolutiondepthwise_group_pack1to8 = 155,
161 | convolutiondepthwise_group_pack4 = 156,
162 | convolutiondepthwise_group_pack4to1 = 157,
163 | convolutiondepthwise_group_pack4to8 = 158,
164 | convolutiondepthwise_group_pack8 = 159,
165 | convolutiondepthwise_group_pack8to1 = 160,
166 | convolutiondepthwise_group_pack8to4 = 161,
167 | convolutiondepthwise_pack4 = 162,
168 | convolutiondepthwise_pack8 = 163,
169 | padding = 164,
170 | padding_pack1to4 = 165,
171 | padding_pack1to8 = 166,
172 | padding_pack4 = 167,
173 | padding_pack4to1 = 168,
174 | padding_pack4to8 = 169,
175 | padding_pack8 = 170,
176 | padding_pack8to1 = 171,
177 | padding_pack8to4 = 172,
178 | normalize_coeffs = 173,
179 | normalize_coeffs_pack4 = 174,
180 | normalize_coeffs_pack8 = 175,
181 | normalize_norm = 176,
182 | normalize_norm_pack4 = 177,
183 | normalize_norm_pack8 = 178,
184 | normalize_reduce_sum4_fp16_to_fp32 = 179,
185 | normalize_reduce_sum4_fp16_to_fp32_pack4 = 180,
186 | normalize_reduce_sum4_fp16_to_fp32_pack8 = 181,
187 | normalize_reduce_sum4_fp32 = 182,
188 | normalize_reduce_sum4_fp32_pack4 = 183,
189 | normalize_reduce_sum4_fp32_pack8 = 184,
190 | permute = 185,
191 | permute_pack1to4 = 186,
192 | permute_pack1to8 = 187,
193 | permute_pack4 = 188,
194 | permute_pack4to1 = 189,
195 | permute_pack4to8 = 190,
196 | permute_pack8 = 191,
197 | permute_pack8to1 = 192,
198 | permute_pack8to4 = 193,
199 | priorbox = 194,
200 | priorbox_mxnet = 195,
201 | interp = 196,
202 | interp_bicubic = 197,
203 | interp_bicubic_coeffs = 198,
204 | interp_bicubic_pack4 = 199,
205 | interp_bicubic_pack8 = 200,
206 | interp_pack4 = 201,
207 | interp_pack8 = 202,
208 | deconvolutiondepthwise = 203,
209 | deconvolutiondepthwise_group = 204,
210 | deconvolutiondepthwise_group_pack1to4 = 205,
211 | deconvolutiondepthwise_group_pack1to8 = 206,
212 | deconvolutiondepthwise_group_pack4 = 207,
213 | deconvolutiondepthwise_group_pack4to1 = 208,
214 | deconvolutiondepthwise_group_pack4to8 = 209,
215 | deconvolutiondepthwise_group_pack8 = 210,
216 | deconvolutiondepthwise_group_pack8to1 = 211,
217 | deconvolutiondepthwise_group_pack8to4 = 212,
218 | deconvolutiondepthwise_pack4 = 213,
219 | deconvolutiondepthwise_pack8 = 214,
220 | shufflechannel = 215,
221 | shufflechannel_pack4 = 216,
222 | shufflechannel_pack8 = 217,
223 | instancenorm_coeffs = 218,
224 | instancenorm_coeffs_pack4 = 219,
225 | instancenorm_coeffs_pack8 = 220,
226 | instancenorm_norm = 221,
227 | instancenorm_norm_pack4 = 222,
228 | instancenorm_norm_pack8 = 223,
229 | instancenorm_reduce_mean = 224,
230 | instancenorm_reduce_mean_pack4 = 225,
231 | instancenorm_reduce_mean_pack8 = 226,
232 | instancenorm_reduce_sum4_fp16_to_fp32 = 227,
233 | instancenorm_reduce_sum4_fp16_to_fp32_pack4 = 228,
234 | instancenorm_reduce_sum4_fp16_to_fp32_pack8 = 229,
235 | instancenorm_reduce_sum4_fp32 = 230,
236 | instancenorm_reduce_sum4_fp32_pack4 = 231,
237 | instancenorm_reduce_sum4_fp32_pack8 = 232,
238 | instancenorm_sub_mean_square = 233,
239 | instancenorm_sub_mean_square_pack4 = 234,
240 | instancenorm_sub_mean_square_pack8 = 235,
241 | clip = 236,
242 | clip_pack4 = 237,
243 | clip_pack8 = 238,
244 | reorg = 239,
245 | reorg_pack1to4 = 240,
246 | reorg_pack1to8 = 241,
247 | reorg_pack4 = 242,
248 | reorg_pack4to8 = 243,
249 | reorg_pack8 = 244,
250 | packing = 245,
251 | packing_fp16_to_fp32 = 246,
252 | packing_fp32_to_fp16 = 247,
253 | packing_pack1to4 = 248,
254 | packing_pack1to4_fp16_to_fp32 = 249,
255 | packing_pack1to4_fp32_to_fp16 = 250,
256 | packing_pack1to8 = 251,
257 | packing_pack1to8_fp16_to_fp32 = 252,
258 | packing_pack1to8_fp32_to_fp16 = 253,
259 | packing_pack4 = 254,
260 | packing_pack4_fp16_to_fp32 = 255,
261 | packing_pack4_fp32_to_fp16 = 256,
262 | packing_pack4to1 = 257,
263 | packing_pack4to1_fp16_to_fp32 = 258,
264 | packing_pack4to1_fp32_to_fp16 = 259,
265 | packing_pack4to8 = 260,
266 | packing_pack4to8_fp16_to_fp32 = 261,
267 | packing_pack4to8_fp32_to_fp16 = 262,
268 | packing_pack8 = 263,
269 | packing_pack8_fp16_to_fp32 = 264,
270 | packing_pack8_fp32_to_fp16 = 265,
271 | packing_pack8to1 = 266,
272 | packing_pack8to1_fp16_to_fp32 = 267,
273 | packing_pack8to1_fp32_to_fp16 = 268,
274 | packing_pack8to4 = 269,
275 | packing_pack8to4_fp16_to_fp32 = 270,
276 | packing_pack8to4_fp32_to_fp16 = 271,
277 | cast_fp16_to_fp32 = 272,
278 | cast_fp16_to_fp32_pack4 = 273,
279 | cast_fp16_to_fp32_pack8 = 274,
280 | cast_fp32_to_fp16 = 275,
281 | cast_fp32_to_fp16_pack4 = 276,
282 | cast_fp32_to_fp16_pack8 = 277,
283 | hardsigmoid = 278,
284 | hardsigmoid_pack4 = 279,
285 | hardsigmoid_pack8 = 280,
286 | hardswish = 281,
287 | hardswish_pack4 = 282,
288 | hardswish_pack8 = 283,
289 | pixelshuffle = 284,
290 | pixelshuffle_pack4 = 285,
291 | pixelshuffle_pack4to1 = 286,
292 | pixelshuffle_pack8 = 287,
293 | pixelshuffle_pack8to1 = 288,
294 | pixelshuffle_pack8to4 = 289,
295 | deepcopy = 290,
296 | deepcopy_pack4 = 291,
297 | deepcopy_pack8 = 292,
298 | mish = 293,
299 | mish_pack4 = 294,
300 | mish_pack8 = 295,
301 | swish = 296,
302 | swish_pack4 = 297,
303 | swish_pack8 = 298,
304 | convert_ycbcr = 299,
305 |
306 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/layer_type.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_LAYER_TYPE_H
16 | #define NCNN_LAYER_TYPE_H
17 |
18 | namespace ncnn {
19 |
20 | namespace LayerType {
21 | enum LayerType
22 | {
23 | #include "layer_type_enum.h"
24 | CustomBit = (1 << 8),
25 | };
26 | } // namespace LayerType
27 |
28 | } // namespace ncnn
29 |
30 | #endif // NCNN_LAYER_TYPE_H
31 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/layer_type_enum.h:
--------------------------------------------------------------------------------
1 | // Layer Type Enum header
2 | //
3 | // This file is auto-generated by cmake, don't edit it.
4 |
5 | AbsVal = 0,
6 | ArgMax = 1,
7 | BatchNorm = 2,
8 | Bias = 3,
9 | BNLL = 4,
10 | Concat = 5,
11 | Convolution = 6,
12 | Crop = 7,
13 | Deconvolution = 8,
14 | Dropout = 9,
15 | Eltwise = 10,
16 | ELU = 11,
17 | Embed = 12,
18 | Exp = 13,
19 | Flatten = 14,
20 | InnerProduct = 15,
21 | Input = 16,
22 | Log = 17,
23 | LRN = 18,
24 | MemoryData = 19,
25 | MVN = 20,
26 | Pooling = 21,
27 | Power = 22,
28 | PReLU = 23,
29 | Proposal = 24,
30 | Reduction = 25,
31 | ReLU = 26,
32 | Reshape = 27,
33 | ROIPooling = 28,
34 | Scale = 29,
35 | Sigmoid = 30,
36 | Slice = 31,
37 | Softmax = 32,
38 | Split = 33,
39 | SPP = 34,
40 | TanH = 35,
41 | Threshold = 36,
42 | Tile = 37,
43 | RNN = 38,
44 | LSTM = 39,
45 | BinaryOp = 40,
46 | UnaryOp = 41,
47 | ConvolutionDepthWise = 42,
48 | Padding = 43,
49 | Squeeze = 44,
50 | ExpandDims = 45,
51 | Normalize = 46,
52 | Permute = 47,
53 | PriorBox = 48,
54 | DetectionOutput = 49,
55 | Interp = 50,
56 | DeconvolutionDepthWise = 51,
57 | ShuffleChannel = 52,
58 | InstanceNorm = 53,
59 | Clip = 54,
60 | Reorg = 55,
61 | YoloDetectionOutput = 56,
62 | Quantize = 57,
63 | Dequantize = 58,
64 | Yolov3DetectionOutput = 59,
65 | PSROIPooling = 60,
66 | ROIAlign = 61,
67 | Packing = 62,
68 | Requantize = 63,
69 | Cast = 64,
70 | HardSigmoid = 65,
71 | SELU = 66,
72 | HardSwish = 67,
73 | Noop = 68,
74 | PixelShuffle = 69,
75 | DeepCopy = 70,
76 | Mish = 71,
77 | StatisticsPooling = 72,
78 | Swish = 73,
79 | Gemm = 74,
80 | GroupNorm = 75,
81 | LayerNorm = 76,
82 | Softplus = 77,
83 | GRU = 78,
84 | MultiHeadAttention = 79,
85 | GELU = 80,
86 |
87 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/modelbin.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_MODELBIN_H
16 | #define NCNN_MODELBIN_H
17 |
18 | #include "mat.h"
19 |
20 | namespace ncnn {
21 |
22 | class DataReader;
23 | class NCNN_EXPORT ModelBin
24 | {
25 | public:
26 | ModelBin();
27 | virtual ~ModelBin();
28 | // element type
29 | // 0 = auto
30 | // 1 = float32
31 | // 2 = float16
32 | // 3 = int8
33 | // load vec
34 | virtual Mat load(int w, int type) const = 0;
35 | // load image
36 | virtual Mat load(int w, int h, int type) const;
37 | // load dim
38 | virtual Mat load(int w, int h, int c, int type) const;
39 | };
40 |
41 | class ModelBinFromDataReaderPrivate;
42 | class NCNN_EXPORT ModelBinFromDataReader : public ModelBin
43 | {
44 | public:
45 | explicit ModelBinFromDataReader(const DataReader& dr);
46 | virtual ~ModelBinFromDataReader();
47 |
48 | virtual Mat load(int w, int type) const;
49 |
50 | private:
51 | ModelBinFromDataReader(const ModelBinFromDataReader&);
52 | ModelBinFromDataReader& operator=(const ModelBinFromDataReader&);
53 |
54 | private:
55 | ModelBinFromDataReaderPrivate* const d;
56 | };
57 |
58 | class ModelBinFromMatArrayPrivate;
59 | class NCNN_EXPORT ModelBinFromMatArray : public ModelBin
60 | {
61 | public:
62 | // construct from weight blob array
63 | explicit ModelBinFromMatArray(const Mat* weights);
64 | virtual ~ModelBinFromMatArray();
65 |
66 | virtual Mat load(int w, int type) const;
67 |
68 | private:
69 | ModelBinFromMatArray(const ModelBinFromMatArray&);
70 | ModelBinFromMatArray& operator=(const ModelBinFromMatArray&);
71 |
72 | private:
73 | ModelBinFromMatArrayPrivate* const d;
74 | };
75 |
76 | } // namespace ncnn
77 |
78 | #endif // NCNN_MODELBIN_H
79 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/ncnn_export.h:
--------------------------------------------------------------------------------
1 |
2 | #ifndef NCNN_EXPORT_H
3 | #define NCNN_EXPORT_H
4 |
5 | #ifdef NCNN_STATIC_DEFINE
6 | # define NCNN_EXPORT
7 | # define NCNN_NO_EXPORT
8 | #else
9 | # ifndef NCNN_EXPORT
10 | # ifdef ncnn_EXPORTS
11 | /* We are building this library */
12 | # define NCNN_EXPORT __attribute__((visibility("default")))
13 | # else
14 | /* We are using this library */
15 | # define NCNN_EXPORT __attribute__((visibility("default")))
16 | # endif
17 | # endif
18 |
19 | # ifndef NCNN_NO_EXPORT
20 | # define NCNN_NO_EXPORT __attribute__((visibility("hidden")))
21 | # endif
22 | #endif
23 |
24 | #ifndef NCNN_DEPRECATED
25 | # define NCNN_DEPRECATED __attribute__ ((__deprecated__))
26 | #endif
27 |
28 | #ifndef NCNN_DEPRECATED_EXPORT
29 | # define NCNN_DEPRECATED_EXPORT NCNN_EXPORT NCNN_DEPRECATED
30 | #endif
31 |
32 | #ifndef NCNN_DEPRECATED_NO_EXPORT
33 | # define NCNN_DEPRECATED_NO_EXPORT NCNN_NO_EXPORT NCNN_DEPRECATED
34 | #endif
35 |
36 | #if 0 /* DEFINE_NO_DEPRECATED */
37 | # ifndef NCNN_NO_DEPRECATED
38 | # define NCNN_NO_DEPRECATED
39 | # endif
40 | #endif
41 |
42 | #endif /* NCNN_EXPORT_H */
43 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/net.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_NET_H
16 | #define NCNN_NET_H
17 |
18 | #include "blob.h"
19 | #include "layer.h"
20 | #include "mat.h"
21 | #include "option.h"
22 | #include "platform.h"
23 |
24 | #if NCNN_PLATFORM_API
25 | #if __ANDROID_API__ >= 9
26 | #include
27 | #endif // __ANDROID_API__ >= 9
28 | #endif // NCNN_PLATFORM_API
29 |
30 | namespace ncnn {
31 |
32 | #if NCNN_VULKAN
33 | class VkCompute;
34 | #endif // NCNN_VULKAN
35 | class DataReader;
36 | class Extractor;
37 | class NetPrivate;
38 | class NCNN_EXPORT Net
39 | {
40 | public:
41 | // empty init
42 | Net();
43 | // clear and destroy
44 | virtual ~Net();
45 |
46 | public:
47 | // option can be changed before loading
48 | Option opt;
49 |
50 | #if NCNN_VULKAN
51 | // set gpu device by index
52 | void set_vulkan_device(int device_index);
53 |
54 | // set gpu device by device handle, no owner transfer
55 | void set_vulkan_device(const VulkanDevice* vkdev);
56 |
57 | const VulkanDevice* vulkan_device() const;
58 | #endif // NCNN_VULKAN
59 |
60 | #if NCNN_STRING
61 | // register custom layer by layer type name
62 | // return 0 if success
63 | int register_custom_layer(const char* type, layer_creator_func creator, layer_destroyer_func destroyer = 0, void* userdata = 0);
64 | #endif // NCNN_STRING
65 | // register custom layer by layer type
66 | // return 0 if success
67 | int register_custom_layer(int index, layer_creator_func creator, layer_destroyer_func destroyer = 0, void* userdata = 0);
68 |
69 | #if NCNN_STRING
70 | int load_param(const DataReader& dr);
71 | #endif // NCNN_STRING
72 |
73 | int load_param_bin(const DataReader& dr);
74 |
75 | int load_model(const DataReader& dr);
76 |
77 | #if NCNN_STDIO
78 | #if NCNN_STRING
79 | // load network structure from plain param file
80 | // return 0 if success
81 | int load_param(FILE* fp);
82 | int load_param(const char* protopath);
83 | int load_param_mem(const char* mem);
84 | #endif // NCNN_STRING
85 | // load network structure from binary param file
86 | // return 0 if success
87 | int load_param_bin(FILE* fp);
88 | int load_param_bin(const char* protopath);
89 |
90 | // load network weight data from model file
91 | // return 0 if success
92 | int load_model(FILE* fp);
93 | int load_model(const char* modelpath);
94 | #endif // NCNN_STDIO
95 |
96 | // load network structure from external memory
97 | // memory pointer must be 32-bit aligned
98 | // return bytes consumed
99 | int load_param(const unsigned char* mem);
100 |
101 | // reference network weight data from external memory
102 | // weight data is not copied but referenced
103 | // so external memory should be retained when used
104 | // memory pointer must be 32-bit aligned
105 | // return bytes consumed
106 | int load_model(const unsigned char* mem);
107 |
108 | #if NCNN_PLATFORM_API
109 | #if __ANDROID_API__ >= 9
110 | #if NCNN_STRING
111 | // convenient load network structure from android asset plain param file
112 | int load_param(AAsset* asset);
113 | int load_param(AAssetManager* mgr, const char* assetpath);
114 | #endif // NCNN_STRING
115 | // convenient load network structure from android asset binary param file
116 | int load_param_bin(AAsset* asset);
117 | int load_param_bin(AAssetManager* mgr, const char* assetpath);
118 |
119 | // convenient load network weight data from android asset model file
120 | int load_model(AAsset* asset);
121 | int load_model(AAssetManager* mgr, const char* assetpath);
122 | #endif // __ANDROID_API__ >= 9
123 | #endif // NCNN_PLATFORM_API
124 |
125 | // unload network structure and weight data
126 | void clear();
127 |
128 | // construct an Extractor from network
129 | Extractor create_extractor() const;
130 |
131 | // get input/output indexes/names
132 | const std::vector& input_indexes() const;
133 | const std::vector& output_indexes() const;
134 | #if NCNN_STRING
135 | const std::vector& input_names() const;
136 | const std::vector& output_names() const;
137 | #endif
138 |
139 | const std::vector& blobs() const;
140 | const std::vector& layers() const;
141 |
142 | std::vector& mutable_blobs();
143 | std::vector& mutable_layers();
144 |
145 | protected:
146 | friend class Extractor;
147 | #if NCNN_STRING
148 | int find_blob_index_by_name(const char* name) const;
149 | int find_layer_index_by_name(const char* name) const;
150 | virtual int custom_layer_to_index(const char* type);
151 | virtual Layer* create_custom_layer(const char* type);
152 | #endif // NCNN_STRING
153 | virtual Layer* create_custom_layer(int index);
154 |
155 | private:
156 | Net(const Net&);
157 | Net& operator=(const Net&);
158 |
159 | private:
160 | NetPrivate* const d;
161 | };
162 |
163 | class ExtractorPrivate;
164 | class NCNN_EXPORT Extractor
165 | {
166 | public:
167 | virtual ~Extractor();
168 |
169 | // copy
170 | Extractor(const Extractor&);
171 |
172 | // assign
173 | Extractor& operator=(const Extractor&);
174 |
175 | // clear blob mats and alloctors
176 | void clear();
177 |
178 | // enable light mode
179 | // intermediate blob will be recycled when enabled
180 | // enabled by default
181 | void set_light_mode(bool enable);
182 |
183 | // set thread count for this extractor
184 | // this will overwrite the global setting
185 | // default count is system depended
186 | void set_num_threads(int num_threads);
187 |
188 | // set blob memory allocator
189 | void set_blob_allocator(Allocator* allocator);
190 |
191 | // set workspace memory allocator
192 | void set_workspace_allocator(Allocator* allocator);
193 |
194 | #if NCNN_VULKAN
195 | void set_vulkan_compute(bool enable);
196 |
197 | void set_blob_vkallocator(VkAllocator* allocator);
198 |
199 | void set_workspace_vkallocator(VkAllocator* allocator);
200 |
201 | void set_staging_vkallocator(VkAllocator* allocator);
202 | #endif // NCNN_VULKAN
203 |
204 | #if NCNN_STRING
205 | // set input by blob name
206 | // return 0 if success
207 | int input(const char* blob_name, const Mat& in);
208 |
209 | // get result by blob name
210 | // return 0 if success
211 | // type = 0, default
212 | // type = 1, do not convert fp16/bf16 or / and packing
213 | int extract(const char* blob_name, Mat& feat, int type = 0);
214 | #endif // NCNN_STRING
215 |
216 | // set input by blob index
217 | // return 0 if success
218 | int input(int blob_index, const Mat& in);
219 |
220 | // get result by blob index
221 | // return 0 if success
222 | // type = 0, default
223 | // type = 1, do not convert fp16/bf16 or / and packing
224 | int extract(int blob_index, Mat& feat, int type = 0);
225 |
226 | #if NCNN_VULKAN
227 | #if NCNN_STRING
228 | // set input by blob name
229 | // return 0 if success
230 | int input(const char* blob_name, const VkMat& in);
231 |
232 | // get result by blob name
233 | // return 0 if success
234 | int extract(const char* blob_name, VkMat& feat, VkCompute& cmd);
235 |
236 | // set input by blob name
237 | // return 0 if success
238 | int input(const char* blob_name, const VkImageMat& in);
239 |
240 | // get result by blob name
241 | // return 0 if success
242 | int extract(const char* blob_name, VkImageMat& feat, VkCompute& cmd);
243 | #endif // NCNN_STRING
244 |
245 | // set input by blob index
246 | // return 0 if success
247 | int input(int blob_index, const VkMat& in);
248 |
249 | // get result by blob index
250 | // return 0 if success
251 | int extract(int blob_index, VkMat& feat, VkCompute& cmd);
252 |
253 | // set input by blob index
254 | // return 0 if success
255 | int input(int blob_index, const VkImageMat& in);
256 |
257 | // get result by blob index
258 | // return 0 if success
259 | int extract(int blob_index, VkImageMat& feat, VkCompute& cmd);
260 | #endif // NCNN_VULKAN
261 |
262 | protected:
263 | friend Extractor Net::create_extractor() const;
264 | Extractor(const Net* net, size_t blob_count);
265 |
266 | private:
267 | ExtractorPrivate* const d;
268 | };
269 |
270 | } // namespace ncnn
271 |
272 | #endif // NCNN_NET_H
273 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/option.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_OPTION_H
16 | #define NCNN_OPTION_H
17 |
18 | #include "platform.h"
19 |
20 | namespace ncnn {
21 |
22 | #if NCNN_VULKAN
23 | class VkAllocator;
24 | class PipelineCache;
25 | #endif // NCNN_VULKAN
26 |
27 | class Allocator;
28 | class NCNN_EXPORT Option
29 | {
30 | public:
31 | // default option
32 | Option();
33 |
34 | public:
35 | // light mode
36 | // intermediate blob will be recycled when enabled
37 | // enabled by default
38 | bool lightmode;
39 |
40 | // thread count
41 | // default value is the one returned by get_cpu_count()
42 | int num_threads;
43 |
44 | // blob memory allocator
45 | Allocator* blob_allocator;
46 |
47 | // workspace memory allocator
48 | Allocator* workspace_allocator;
49 |
50 | #if NCNN_VULKAN
51 | // blob memory allocator
52 | VkAllocator* blob_vkallocator;
53 |
54 | // workspace memory allocator
55 | VkAllocator* workspace_vkallocator;
56 |
57 | // staging memory allocator
58 | VkAllocator* staging_vkallocator;
59 |
60 | // pipeline cache
61 | PipelineCache* pipeline_cache;
62 | #endif // NCNN_VULKAN
63 |
64 | // the time openmp threads busy-wait for more work before going to sleep
65 | // default value is 20ms to keep the cores enabled
66 | // without too much extra power consumption afterwards
67 | int openmp_blocktime;
68 |
69 | // enable winograd convolution optimization
70 | // improve convolution 3x3 stride1 performance, may consume more memory
71 | // changes should be applied before loading network structure and weight
72 | // enabled by default
73 | bool use_winograd_convolution;
74 |
75 | // enable sgemm convolution optimization
76 | // improve convolution 1x1 stride1 performance, may consume more memory
77 | // changes should be applied before loading network structure and weight
78 | // enabled by default
79 | bool use_sgemm_convolution;
80 |
81 | // enable quantized int8 inference
82 | // use low-precision int8 path for quantized model
83 | // changes should be applied before loading network structure and weight
84 | // enabled by default
85 | bool use_int8_inference;
86 |
87 | // enable vulkan compute
88 | bool use_vulkan_compute;
89 |
90 | // enable bf16 data type for storage
91 | // improve most operator performance on all arm devices, may consume more memory
92 | bool use_bf16_storage;
93 |
94 | // enable options for gpu inference
95 | bool use_fp16_packed;
96 | bool use_fp16_storage;
97 | bool use_fp16_arithmetic;
98 | bool use_int8_packed;
99 | bool use_int8_storage;
100 | bool use_int8_arithmetic;
101 |
102 | // enable simd-friendly packed memory layout
103 | // improve all operator performance on all arm devices, will consume more memory
104 | // changes should be applied before loading network structure and weight
105 | // enabled by default
106 | bool use_packing_layout;
107 |
108 | bool use_shader_pack8;
109 |
110 | // subgroup option
111 | bool use_subgroup_basic;
112 | bool use_subgroup_vote;
113 | bool use_subgroup_ballot;
114 | bool use_subgroup_shuffle;
115 |
116 | // turn on for adreno
117 | bool use_image_storage;
118 | bool use_tensor_storage;
119 |
120 | // used for fp16 weight storage in AVX
121 | // TODO drop this option
122 | bool use_weight_fp16_storage;
123 |
124 | // enable DAZ(Denormals-Are-Zero) and FTZ(Flush-To-Zero)
125 | // default value is 3
126 | // 0 = DAZ OFF, FTZ OFF
127 | // 1 = DAZ ON , FTZ OFF
128 | // 2 = DAZ OFF, FTZ ON
129 | // 3 = DAZ ON, FTZ ON
130 | int flush_denormals;
131 |
132 | bool use_local_pool_allocator;
133 |
134 | bool use_reserved_1;
135 | bool use_reserved_2;
136 | bool use_reserved_3;
137 | bool use_reserved_4;
138 | bool use_reserved_5;
139 | bool use_reserved_6;
140 | bool use_reserved_7;
141 | bool use_reserved_8;
142 | bool use_reserved_9;
143 | bool use_reserved_10;
144 | bool use_reserved_11;
145 | };
146 |
147 | } // namespace ncnn
148 |
149 | #endif // NCNN_OPTION_H
150 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/paramdict.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_PARAMDICT_H
16 | #define NCNN_PARAMDICT_H
17 |
18 | #include "mat.h"
19 |
20 | // at most 32 parameters
21 | #define NCNN_MAX_PARAM_COUNT 32
22 |
23 | namespace ncnn {
24 |
25 | class DataReader;
26 | class Net;
27 | class ParamDictPrivate;
28 | class NCNN_EXPORT ParamDict
29 | {
30 | public:
31 | // empty
32 | ParamDict();
33 |
34 | virtual ~ParamDict();
35 |
36 | // copy
37 | ParamDict(const ParamDict&);
38 |
39 | // assign
40 | ParamDict& operator=(const ParamDict&);
41 |
42 | // get type
43 | int type(int id) const;
44 |
45 | // get int
46 | int get(int id, int def) const;
47 | // get float
48 | float get(int id, float def) const;
49 | // get array
50 | Mat get(int id, const Mat& def) const;
51 |
52 | // set int
53 | void set(int id, int i);
54 | // set float
55 | void set(int id, float f);
56 | // set array
57 | void set(int id, const Mat& v);
58 |
59 | protected:
60 | friend class Net;
61 |
62 | void clear();
63 |
64 | int load_param(const DataReader& dr);
65 | int load_param_bin(const DataReader& dr);
66 |
67 | private:
68 | ParamDictPrivate* const d;
69 | };
70 |
71 | } // namespace ncnn
72 |
73 | #endif // NCNN_PARAMDICT_H
74 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/pipeline.h:
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_PIPELINE_H
16 | #define NCNN_PIPELINE_H
17 |
18 | #include "mat.h"
19 | #include "platform.h"
20 | #if NCNN_VULKAN
21 | #include "gpu.h"
22 |
23 | #include
24 | #endif // NCNN_VULKAN
25 |
26 | namespace ncnn {
27 |
28 | #if NCNN_VULKAN
29 | class Option;
30 | class PipelinePrivate;
31 | class NCNN_EXPORT Pipeline
32 | {
33 | public:
34 | explicit Pipeline(const VulkanDevice* vkdev);
35 | virtual ~Pipeline();
36 |
37 | public:
38 | void set_optimal_local_size_xyz(int w = 4, int h = 4, int c = 4);
39 | void set_optimal_local_size_xyz(const Mat& local_size_xyz);
40 | void set_local_size_xyz(int w, int h, int c);
41 |
42 | int create(const uint32_t* spv_data, size_t spv_data_size, const std::vector& specializations);
43 |
44 | int create(int shader_type_index, const Option& opt, const std::vector& specializations);
45 |
46 | public:
47 | VkShaderModule shader_module() const;
48 | VkDescriptorSetLayout descriptorset_layout() const;
49 | VkPipelineLayout pipeline_layout() const;
50 | VkPipeline pipeline() const;
51 | VkDescriptorUpdateTemplateKHR descriptor_update_template() const;
52 |
53 | const ShaderInfo& shader_info() const;
54 |
55 | uint32_t local_size_x() const;
56 | uint32_t local_size_y() const;
57 | uint32_t local_size_z() const;
58 |
59 | protected:
60 | void set_shader_module(VkShaderModule shader_module);
61 | void set_descriptorset_layout(VkDescriptorSetLayout descriptorset_layout);
62 | void set_pipeline_layout(VkPipelineLayout pipeline_layout);
63 | void set_pipeline(VkPipeline pipeline);
64 | void set_descriptor_update_template(VkDescriptorUpdateTemplateKHR descriptor_update_template);
65 |
66 | void set_shader_info(const ShaderInfo& shader_info);
67 |
68 | public:
69 | const VulkanDevice* vkdev;
70 |
71 | private:
72 | Pipeline(const Pipeline&);
73 | Pipeline& operator=(const Pipeline&);
74 |
75 | private:
76 | PipelinePrivate* const d;
77 | };
78 |
79 | #if NCNN_PLATFORM_API
80 | #if __ANDROID_API__ >= 26
81 | class VkCompute;
82 | class NCNN_EXPORT ImportAndroidHardwareBufferPipeline : private Pipeline
83 | {
84 | public:
85 | explicit ImportAndroidHardwareBufferPipeline(const VulkanDevice* vkdev);
86 | virtual ~ImportAndroidHardwareBufferPipeline();
87 |
88 | int create(VkAndroidHardwareBufferImageAllocator* ahb_im_allocator, int type_to, int rotate_from, const Option& opt);
89 | int create(VkAndroidHardwareBufferImageAllocator* ahb_im_allocator, int type_to, int rotate_from, int target_width, int target_height, const Option& opt);
90 | void destroy();
91 |
92 | friend class VkCompute;
93 |
94 | protected:
95 | int create_shader_module(const Option& opt);
96 | int create_sampler(VkAndroidHardwareBufferImageAllocator* ahb_im_allocator);
97 | int create_descriptorset_layout();
98 |
99 | public:
100 | int type_to;
101 | int rotate_from;
102 | bool need_resize;
103 |
104 | VkSampler sampler;
105 | };
106 | #endif // __ANDROID_API__ >= 26
107 | #endif // NCNN_PLATFORM_API
108 |
109 | #endif // NCNN_VULKAN
110 |
111 | } // namespace ncnn
112 |
113 | #endif // NCNN_PIPELINE_H
114 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/pipelinecache.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_PIPELINECACHE_H
16 | #define NCNN_PIPELINECACHE_H
17 |
18 | #include "platform.h"
19 |
20 | #if NCNN_VULKAN
21 | #include
22 | #endif // NCNN_VULKAN
23 |
24 | #include "mat.h"
25 | #include "gpu.h"
26 |
27 | namespace ncnn {
28 |
29 | #if NCNN_VULKAN
30 |
31 | class VulkanDevice;
32 | class PipelineCachePrivate;
33 | class NCNN_EXPORT PipelineCache
34 | {
35 | public:
36 | explicit PipelineCache(const VulkanDevice* _vkdev);
37 |
38 | virtual ~PipelineCache();
39 |
40 | void clear();
41 |
42 | int get_pipeline(const uint32_t* spv_data, size_t spv_data_size, const std::vector& specializations,
43 | uint32_t local_size_x, uint32_t local_size_y, uint32_t local_size_z,
44 | VkShaderModule* shader_module,
45 | VkDescriptorSetLayout* descriptorset_layout,
46 | VkPipelineLayout* pipeline_layout,
47 | VkPipeline* pipeline,
48 | VkDescriptorUpdateTemplateKHR* descriptor_update_template,
49 | ShaderInfo& shader_info) const;
50 |
51 | int get_pipeline(int shader_type_index, const Option& opt, const std::vector& specializations,
52 | uint32_t local_size_x, uint32_t local_size_y, uint32_t local_size_z,
53 | VkShaderModule* shader_module,
54 | VkDescriptorSetLayout* descriptorset_layout,
55 | VkPipelineLayout* pipeline_layout,
56 | VkPipeline* pipeline,
57 | VkDescriptorUpdateTemplateKHR* descriptor_update_template,
58 | ShaderInfo& shader_info) const;
59 |
60 | protected:
61 | int create_shader_module(int shader_type_index, const Option& opt, uint32_t local_size_x, uint32_t local_size_y, uint32_t local_size_z,
62 | VkShaderModule* _shader_module, ShaderInfo& si) const;
63 |
64 | int new_pipeline(VkShaderModule shader_module, const ShaderInfo& shader_info, const std::vector& specializations,
65 | VkDescriptorSetLayout* descriptorset_layout,
66 | VkPipelineLayout* pipeline_layout,
67 | VkPipeline* pipeline,
68 | VkDescriptorUpdateTemplateKHR* descriptor_update_template) const;
69 |
70 | protected:
71 | const VulkanDevice* vkdev;
72 |
73 | private:
74 | PipelineCache(const PipelineCache&);
75 | PipelineCache& operator=(const PipelineCache&);
76 |
77 | private:
78 | PipelineCachePrivate* const d;
79 | };
80 |
81 | #endif // NCNN_VULKAN
82 |
83 | } // namespace ncnn
84 |
85 | #endif // NCNN_PIPELINECACHE_H
86 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/platform.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_PLATFORM_H
16 | #define NCNN_PLATFORM_H
17 |
18 | #define NCNN_STDIO 1
19 | #define NCNN_STRING 1
20 | #define NCNN_SIMPLEOCV 0
21 | #define NCNN_SIMPLEOMP 0
22 | #define NCNN_SIMPLESTL 0
23 | #define NCNN_THREADS 1
24 | #define NCNN_BENCHMARK 0
25 | #define NCNN_PLATFORM_API 1
26 | #define NCNN_PIXEL 1
27 | #define NCNN_PIXEL_ROTATE 1
28 | #define NCNN_PIXEL_AFFINE 1
29 | #define NCNN_PIXEL_DRAWING 1
30 | #define NCNN_VULKAN 1
31 | #define NCNN_RUNTIME_CPU 1
32 | #define NCNN_AVX2 1
33 | #define NCNN_ARM82 0
34 | #define NCNN_ARM82DOT 0
35 | #define NCNN_RVV 0
36 | #define NCNN_INT8 1
37 |
38 | #define NCNN_VERSION_STRING "1.0.20210525"
39 |
40 | #include "ncnn_export.h"
41 |
42 | #ifdef __cplusplus
43 |
44 | #if NCNN_THREADS
45 | #if (defined _WIN32 && !(defined __MINGW32__))
46 | #define WIN32_LEAN_AND_MEAN
47 | #include
48 | #include
49 | #else
50 | #include
51 | #endif
52 | #endif // NCNN_THREADS
53 |
54 | #if __ANDROID_API__ >= 26
55 | #define VK_USE_PLATFORM_ANDROID_KHR
56 | #endif // __ANDROID_API__ >= 26
57 |
58 | namespace ncnn {
59 |
60 | #if NCNN_THREADS
61 | #if (defined _WIN32 && !(defined __MINGW32__))
62 | class NCNN_EXPORT Mutex
63 | {
64 | public:
65 | Mutex() { InitializeSRWLock(&srwlock); }
66 | ~Mutex() {}
67 | void lock() { AcquireSRWLockExclusive(&srwlock); }
68 | void unlock() { ReleaseSRWLockExclusive(&srwlock); }
69 | private:
70 | friend class ConditionVariable;
71 | // NOTE SRWLock is available from windows vista
72 | SRWLOCK srwlock;
73 | };
74 |
75 | class NCNN_EXPORT ConditionVariable
76 | {
77 | public:
78 | ConditionVariable() { InitializeConditionVariable(&condvar); }
79 | ~ConditionVariable() {}
80 | void wait(Mutex& mutex) { SleepConditionVariableSRW(&condvar, &mutex.srwlock, INFINITE, 0); }
81 | void broadcast() { WakeAllConditionVariable(&condvar); }
82 | void signal() { WakeConditionVariable(&condvar); }
83 | private:
84 | CONDITION_VARIABLE condvar;
85 | };
86 |
87 | static unsigned __stdcall start_wrapper(void* args);
88 | class NCNN_EXPORT Thread
89 | {
90 | public:
91 | Thread(void* (*start)(void*), void* args = 0) { _start = start; _args = args; handle = (HANDLE)_beginthreadex(0, 0, start_wrapper, this, 0, 0); }
92 | ~Thread() {}
93 | void join() { WaitForSingleObject(handle, INFINITE); CloseHandle(handle); }
94 | private:
95 | friend unsigned __stdcall start_wrapper(void* args)
96 | {
97 | Thread* t = (Thread*)args;
98 | t->_start(t->_args);
99 | return 0;
100 | }
101 | HANDLE handle;
102 | void* (*_start)(void*);
103 | void* _args;
104 | };
105 |
106 | class NCNN_EXPORT ThreadLocalStorage
107 | {
108 | public:
109 | ThreadLocalStorage() { key = TlsAlloc(); }
110 | ~ThreadLocalStorage() { TlsFree(key); }
111 | void set(void* value) { TlsSetValue(key, (LPVOID)value); }
112 | void* get() { return (void*)TlsGetValue(key); }
113 | private:
114 | DWORD key;
115 | };
116 | #else // (defined _WIN32 && !(defined __MINGW32__))
117 | class NCNN_EXPORT Mutex
118 | {
119 | public:
120 | Mutex() { pthread_mutex_init(&mutex, 0); }
121 | ~Mutex() { pthread_mutex_destroy(&mutex); }
122 | void lock() { pthread_mutex_lock(&mutex); }
123 | void unlock() { pthread_mutex_unlock(&mutex); }
124 | private:
125 | friend class ConditionVariable;
126 | pthread_mutex_t mutex;
127 | };
128 |
129 | class NCNN_EXPORT ConditionVariable
130 | {
131 | public:
132 | ConditionVariable() { pthread_cond_init(&cond, 0); }
133 | ~ConditionVariable() { pthread_cond_destroy(&cond); }
134 | void wait(Mutex& mutex) { pthread_cond_wait(&cond, &mutex.mutex); }
135 | void broadcast() { pthread_cond_broadcast(&cond); }
136 | void signal() { pthread_cond_signal(&cond); }
137 | private:
138 | pthread_cond_t cond;
139 | };
140 |
141 | class NCNN_EXPORT Thread
142 | {
143 | public:
144 | Thread(void* (*start)(void*), void* args = 0) { pthread_create(&t, 0, start, args); }
145 | ~Thread() {}
146 | void join() { pthread_join(t, 0); }
147 | private:
148 | pthread_t t;
149 | };
150 |
151 | class NCNN_EXPORT ThreadLocalStorage
152 | {
153 | public:
154 | ThreadLocalStorage() { pthread_key_create(&key, 0); }
155 | ~ThreadLocalStorage() { pthread_key_delete(key); }
156 | void set(void* value) { pthread_setspecific(key, value); }
157 | void* get() { return pthread_getspecific(key); }
158 | private:
159 | pthread_key_t key;
160 | };
161 | #endif // (defined _WIN32 && !(defined __MINGW32__))
162 | #else // NCNN_THREADS
163 | class NCNN_EXPORT Mutex
164 | {
165 | public:
166 | Mutex() {}
167 | ~Mutex() {}
168 | void lock() {}
169 | void unlock() {}
170 | };
171 |
172 | class NCNN_EXPORT ConditionVariable
173 | {
174 | public:
175 | ConditionVariable() {}
176 | ~ConditionVariable() {}
177 | void wait(Mutex& /*mutex*/) {}
178 | void broadcast() {}
179 | void signal() {}
180 | };
181 |
182 | class NCNN_EXPORT Thread
183 | {
184 | public:
185 | Thread(void* (*/*start*/)(void*), void* /*args*/ = 0) {}
186 | ~Thread() {}
187 | void join() {}
188 | };
189 |
190 | class NCNN_EXPORT ThreadLocalStorage
191 | {
192 | public:
193 | ThreadLocalStorage() { data = 0; }
194 | ~ThreadLocalStorage() {}
195 | void set(void* value) { data = value; }
196 | void* get() { return data; }
197 | private:
198 | void* data;
199 | };
200 | #endif // NCNN_THREADS
201 |
202 | class NCNN_EXPORT MutexLockGuard
203 | {
204 | public:
205 | MutexLockGuard(Mutex& _mutex) : mutex(_mutex) { mutex.lock(); }
206 | ~MutexLockGuard() { mutex.unlock(); }
207 | private:
208 | Mutex& mutex;
209 | };
210 |
211 | } // namespace ncnn
212 |
213 | #if NCNN_SIMPLESTL
214 | #include "simplestl.h"
215 | #else
216 | #include
217 | #include
218 | #include
219 | #include
220 | #endif
221 |
222 | #endif // __cplusplus
223 |
224 | #if NCNN_STDIO
225 | #if NCNN_PLATFORM_API && __ANDROID_API__ >= 8
226 | #include
227 | #define NCNN_LOGE(...) do { \
228 | fprintf(stderr, ##__VA_ARGS__); fprintf(stderr, "\n"); \
229 | __android_log_print(ANDROID_LOG_WARN, "ncnn", ##__VA_ARGS__); } while(0)
230 | #else // NCNN_PLATFORM_API && __ANDROID_API__ >= 8
231 | #include
232 | #define NCNN_LOGE(...) do { \
233 | fprintf(stderr, ##__VA_ARGS__); fprintf(stderr, "\n"); } while(0)
234 | #endif // NCNN_PLATFORM_API && __ANDROID_API__ >= 8
235 | #else
236 | #define NCNN_LOGE(...)
237 | #endif
238 |
239 | #endif // NCNN_PLATFORM_H
240 |
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/ncnn-20210525-ubuntu-1604-shared/include/ncnn/simpleocv.h:
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1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_SIMPLEOCV_H
16 | #define NCNN_SIMPLEOCV_H
17 |
18 | #include "platform.h"
19 |
20 | #if NCNN_SIMPLEOCV
21 |
22 | #include
23 | #include
24 | #include "allocator.h"
25 | #include "mat.h"
26 |
27 | #if defined(_MSC_VER) || defined(__GNUC__)
28 | #pragma push_macro("min")
29 | #pragma push_macro("max")
30 | #undef min
31 | #undef max
32 | #endif
33 |
34 | #ifndef NCNN_XADD
35 | using ncnn::NCNN_XADD;
36 | #endif
37 |
38 | typedef unsigned char uchar;
39 | typedef unsigned short ushort;
40 | typedef unsigned int uint;
41 |
42 | enum
43 | {
44 | CV_LOAD_IMAGE_UNCHANGED = -1,
45 | CV_LOAD_IMAGE_GRAYSCALE = 0,
46 | CV_LOAD_IMAGE_COLOR = 1,
47 | };
48 |
49 | enum
50 | {
51 | CV_IMWRITE_JPEG_QUALITY = 1
52 | };
53 |
54 | // minimal opencv style data structure implementation
55 | namespace cv {
56 |
57 | template
58 | static inline _Tp saturate_cast(int v)
59 | {
60 | return _Tp(v);
61 | }
62 | template<>
63 | inline uchar saturate_cast(int v)
64 | {
65 | return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0);
66 | }
67 |
68 | template
69 | struct Scalar_
70 | {
71 | Scalar_()
72 | {
73 | v[0] = 0;
74 | v[1] = 0;
75 | v[2] = 0;
76 | v[3] = 0;
77 | }
78 | Scalar_(_Tp _v0)
79 | {
80 | v[0] = _v0;
81 | v[1] = 0;
82 | v[2] = 0;
83 | v[3] = 0;
84 | }
85 | Scalar_(_Tp _v0, _Tp _v1, _Tp _v2)
86 | {
87 | v[0] = _v0;
88 | v[1] = _v1;
89 | v[2] = _v2;
90 | v[3] = 0;
91 | }
92 | Scalar_(_Tp _v0, _Tp _v1, _Tp _v2, _Tp _v3)
93 | {
94 | v[0] = _v0;
95 | v[1] = _v1;
96 | v[2] = _v2;
97 | v[3] = _v3;
98 | }
99 |
100 | const _Tp operator[](const int i) const
101 | {
102 | return v[i];
103 | }
104 |
105 | _Tp operator[](const int i)
106 | {
107 | return v[i];
108 | }
109 |
110 | _Tp v[4];
111 | };
112 |
113 | typedef Scalar_ Scalar;
114 |
115 | template
116 | struct Point_
117 | {
118 | Point_()
119 | : x(0), y(0)
120 | {
121 | }
122 | Point_(_Tp _x, _Tp _y)
123 | : x(_x), y(_y)
124 | {
125 | }
126 |
127 | _Tp x;
128 | _Tp y;
129 | };
130 |
131 | typedef Point_ Point;
132 | typedef Point_ Point2f;
133 |
134 | template
135 | struct Size_
136 | {
137 | Size_()
138 | : width(0), height(0)
139 | {
140 | }
141 | Size_(_Tp _w, _Tp _h)
142 | : width(_w), height(_h)
143 | {
144 | }
145 |
146 | _Tp width;
147 | _Tp height;
148 | };
149 |
150 | typedef Size_ Size;
151 | typedef Size_ Size2f;
152 |
153 | template
154 | struct Rect_
155 | {
156 | Rect_()
157 | : x(0), y(0), width(0), height(0)
158 | {
159 | }
160 | Rect_(_Tp _x, _Tp _y, _Tp _w, _Tp _h)
161 | : x(_x), y(_y), width(_w), height(_h)
162 | {
163 | }
164 | Rect_(Point_<_Tp> _p, Size_<_Tp> _size)
165 | : x(_p.x), y(_p.y), width(_size.width), height(_size.height)
166 | {
167 | }
168 |
169 | _Tp x;
170 | _Tp y;
171 | _Tp width;
172 | _Tp height;
173 |
174 | // area
175 | _Tp area() const
176 | {
177 | return width * height;
178 | }
179 | };
180 |
181 | template
182 | static inline Rect_<_Tp>& operator&=(Rect_<_Tp>& a, const Rect_<_Tp>& b)
183 | {
184 | _Tp x1 = std::max(a.x, b.x), y1 = std::max(a.y, b.y);
185 | a.width = std::min(a.x + a.width, b.x + b.width) - x1;
186 | a.height = std::min(a.y + a.height, b.y + b.height) - y1;
187 | a.x = x1;
188 | a.y = y1;
189 | if (a.width <= 0 || a.height <= 0)
190 | a = Rect_<_Tp>();
191 | return a;
192 | }
193 |
194 | template
195 | static inline Rect_<_Tp>& operator|=(Rect_<_Tp>& a, const Rect_<_Tp>& b)
196 | {
197 | _Tp x1 = std::min(a.x, b.x), y1 = std::min(a.y, b.y);
198 | a.width = std::max(a.x + a.width, b.x + b.width) - x1;
199 | a.height = std::max(a.y + a.height, b.y + b.height) - y1;
200 | a.x = x1;
201 | a.y = y1;
202 | return a;
203 | }
204 |
205 | template
206 | static inline Rect_<_Tp> operator&(const Rect_<_Tp>& a, const Rect_<_Tp>& b)
207 | {
208 | Rect_<_Tp> c = a;
209 | return c &= b;
210 | }
211 |
212 | template
213 | static inline Rect_<_Tp> operator|(const Rect_<_Tp>& a, const Rect_<_Tp>& b)
214 | {
215 | Rect_<_Tp> c = a;
216 | return c |= b;
217 | }
218 |
219 | typedef Rect_ Rect;
220 | typedef Rect_ Rect2f;
221 |
222 | #define CV_8UC1 1
223 | #define CV_8UC3 3
224 | #define CV_8UC4 4
225 | #define CV_32FC1 4
226 |
227 | struct NCNN_EXPORT Mat
228 | {
229 | Mat()
230 | : data(0), refcount(0), rows(0), cols(0), c(0)
231 | {
232 | }
233 |
234 | Mat(int _rows, int _cols, int flags)
235 | : data(0), refcount(0)
236 | {
237 | create(_rows, _cols, flags);
238 | }
239 |
240 | // copy
241 | Mat(const Mat& m)
242 | : data(m.data), refcount(m.refcount)
243 | {
244 | if (refcount)
245 | NCNN_XADD(refcount, 1);
246 |
247 | rows = m.rows;
248 | cols = m.cols;
249 | c = m.c;
250 | }
251 |
252 | Mat(int _rows, int _cols, int flags, void* _data)
253 | : data((unsigned char*)_data), refcount(0)
254 | {
255 | rows = _rows;
256 | cols = _cols;
257 | c = flags;
258 | }
259 |
260 | ~Mat()
261 | {
262 | release();
263 | }
264 |
265 | // assign
266 | Mat& operator=(const Mat& m)
267 | {
268 | if (this == &m)
269 | return *this;
270 |
271 | if (m.refcount)
272 | NCNN_XADD(m.refcount, 1);
273 |
274 | release();
275 |
276 | data = m.data;
277 | refcount = m.refcount;
278 |
279 | rows = m.rows;
280 | cols = m.cols;
281 | c = m.c;
282 |
283 | return *this;
284 | }
285 |
286 | Mat& operator=(const Scalar& s)
287 | {
288 | if (total() > 0)
289 | {
290 | uchar* p = data;
291 | for (int i = 0; i < cols * rows; i++)
292 | {
293 | for (int j = 0; j < c; j++)
294 | {
295 | *p++ = s[j];
296 | }
297 | }
298 | }
299 |
300 | return *this;
301 | }
302 |
303 | void create(int _rows, int _cols, int flags)
304 | {
305 | release();
306 |
307 | rows = _rows;
308 | cols = _cols;
309 | c = flags;
310 |
311 | if (total() > 0)
312 | {
313 | // refcount address must be aligned, so we expand totalsize here
314 | size_t totalsize = (total() + 3) >> 2 << 2;
315 | data = (uchar*)ncnn::fastMalloc(totalsize + (int)sizeof(*refcount));
316 | refcount = (int*)(((uchar*)data) + totalsize);
317 | *refcount = 1;
318 | }
319 | }
320 |
321 | void release()
322 | {
323 | if (refcount && NCNN_XADD(refcount, -1) == 1)
324 | ncnn::fastFree(data);
325 |
326 | data = 0;
327 |
328 | rows = 0;
329 | cols = 0;
330 | c = 0;
331 |
332 | refcount = 0;
333 | }
334 |
335 | Mat clone() const
336 | {
337 | if (empty())
338 | return Mat();
339 |
340 | Mat m(rows, cols, c);
341 |
342 | if (total() > 0)
343 | {
344 | memcpy(m.data, data, total());
345 | }
346 |
347 | return m;
348 | }
349 |
350 | bool empty() const
351 | {
352 | return data == 0 || total() == 0;
353 | }
354 |
355 | int channels() const
356 | {
357 | return c;
358 | }
359 |
360 | int type() const
361 | {
362 | return c;
363 | }
364 |
365 | size_t total() const
366 | {
367 | return cols * rows * c;
368 | }
369 |
370 | const uchar* ptr(int y) const
371 | {
372 | return data + y * cols * c;
373 | }
374 |
375 | uchar* ptr(int y)
376 | {
377 | return data + y * cols * c;
378 | }
379 |
380 | template
381 | const _Tp* ptr(int y) const
382 | {
383 | return (const _Tp*)data + y * cols * c;
384 | }
385 |
386 | template
387 | _Tp* ptr(int y)
388 | {
389 | return (_Tp*)data + y * cols * c;
390 | }
391 |
392 | // roi
393 | Mat operator()(const Rect& roi) const
394 | {
395 | if (empty())
396 | return Mat();
397 |
398 | Mat m(roi.height, roi.width, c);
399 |
400 | int sy = roi.y;
401 | for (int y = 0; y < roi.height; y++)
402 | {
403 | const uchar* sptr = ptr(sy) + roi.x * c;
404 | uchar* dptr = m.ptr(y);
405 | memcpy(dptr, sptr, roi.width * c);
406 | sy++;
407 | }
408 |
409 | return m;
410 | }
411 |
412 | uchar* data;
413 |
414 | // pointer to the reference counter;
415 | // when points to user-allocated data, the pointer is NULL
416 | int* refcount;
417 |
418 | int rows;
419 | int cols;
420 |
421 | int c;
422 | };
423 |
424 | enum ImreadModes
425 | {
426 | IMREAD_UNCHANGED = -1,
427 | IMREAD_GRAYSCALE = 0,
428 | IMREAD_COLOR = 1
429 | };
430 |
431 | NCNN_EXPORT Mat imread(const std::string& path, int flags = IMREAD_COLOR);
432 |
433 | enum ImwriteFlags
434 | {
435 | IMWRITE_JPEG_QUALITY = 1
436 | };
437 |
438 | NCNN_EXPORT bool imwrite(const std::string& path, const Mat& m, const std::vector& params = std::vector());
439 |
440 | #if NCNN_PIXEL
441 | NCNN_EXPORT void resize(const Mat& src, Mat& dst, const Size& size, float sw = 0.f, float sh = 0.f, int flags = 0);
442 | #endif // NCNN_PIXEL
443 |
444 | #if NCNN_PIXEL_DRAWING
445 |
446 | enum
447 | {
448 | FILLED = -1
449 | };
450 |
451 | NCNN_EXPORT void rectangle(Mat& img, Point pt1, Point pt2, const Scalar& color, int thickness = 1);
452 |
453 | NCNN_EXPORT void rectangle(Mat& img, Rect rec, const Scalar& color, int thickness = 1);
454 |
455 | NCNN_EXPORT void circle(Mat& img, Point center, int radius, const Scalar& color, int thickness = 1);
456 |
457 | NCNN_EXPORT void line(Mat& img, Point p0, Point p1, const Scalar& color, int thickness = 1);
458 |
459 | enum
460 | {
461 | FONT_HERSHEY_SIMPLEX = 0
462 | };
463 |
464 | NCNN_EXPORT void putText(Mat& img, const std::string& text, Point org, int fontFace, double fontScale, Scalar color, int thickness = 1);
465 |
466 | NCNN_EXPORT Size getTextSize(const std::string& text, int fontFace, double fontScale, int thickness, int* baseLine);
467 |
468 | #endif // NCNN_PIXEL_DRAWING
469 |
470 | } // namespace cv
471 |
472 | #if defined(_MSC_VER) || defined(__GNUC__)
473 | #pragma pop_macro("min")
474 | #pragma pop_macro("max")
475 | #endif
476 |
477 | #endif // NCNN_SIMPLEOCV
478 |
479 | #endif // NCNN_SIMPLEOCV_H
480 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/simpleomp.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_SIMPLEOMP_H
16 | #define NCNN_SIMPLEOMP_H
17 |
18 | #include "platform.h"
19 |
20 | #if NCNN_SIMPLEOMP
21 |
22 | #include
23 |
24 | // This minimal openmp runtime implementation only supports the llvm openmp abi
25 | // and only supports #pragma omp parallel for num_threads(X)
26 |
27 | #ifdef __cplusplus
28 | extern "C" {
29 | #endif
30 |
31 | NCNN_EXPORT int omp_get_max_threads();
32 |
33 | NCNN_EXPORT void omp_set_num_threads(int num_threads);
34 |
35 | NCNN_EXPORT int omp_get_dynamic();
36 |
37 | NCNN_EXPORT void omp_set_dynamic(int dynamic);
38 |
39 | NCNN_EXPORT int omp_get_num_threads();
40 |
41 | NCNN_EXPORT int omp_get_thread_num();
42 |
43 | NCNN_EXPORT int kmp_get_blocktime();
44 |
45 | NCNN_EXPORT void kmp_set_blocktime(int blocktime);
46 |
47 | #ifdef __cplusplus
48 | }
49 | #endif
50 |
51 | #endif // NCNN_SIMPLEOMP
52 |
53 | #endif // NCNN_SIMPLEOMP_H
54 |
--------------------------------------------------------------------------------
/ncnn-20210525-ubuntu-1604-shared/include/ncnn/vulkan_header_fix.h:
--------------------------------------------------------------------------------
1 | // Tencent is pleased to support the open source community by making ncnn available.
2 | //
3 | // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
4 | //
5 | // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 | // in compliance with the License. You may obtain a copy of the License at
7 | //
8 | // https://opensource.org/licenses/BSD-3-Clause
9 | //
10 | // Unless required by applicable law or agreed to in writing, software distributed
11 | // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 | // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 | // specific language governing permissions and limitations under the License.
14 |
15 | #ifndef NCNN_VULKAN_HEADER_FIX_H
16 | #define NCNN_VULKAN_HEADER_FIX_H
17 |
18 | #include
19 |
20 | // This header contains new structure and function declearation to fix build with old vulkan sdk
21 |
22 | #if VK_HEADER_VERSION < 70
23 | #define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_PROPERTIES (VkStructureType)1000094000
24 | typedef enum VkSubgroupFeatureFlagBits
25 | {
26 | VK_SUBGROUP_FEATURE_BASIC_BIT = 0x00000001,
27 | VK_SUBGROUP_FEATURE_VOTE_BIT = 0x00000002,
28 | VK_SUBGROUP_FEATURE_ARITHMETIC_BIT = 0x00000004,
29 | VK_SUBGROUP_FEATURE_BALLOT_BIT = 0x00000008,
30 | VK_SUBGROUP_FEATURE_SHUFFLE_BIT = 0x00000010,
31 | VK_SUBGROUP_FEATURE_SHUFFLE_RELATIVE_BIT = 0x00000020,
32 | VK_SUBGROUP_FEATURE_CLUSTERED_BIT = 0x00000040,
33 | VK_SUBGROUP_FEATURE_QUAD_BIT = 0x00000080,
34 | VK_SUBGROUP_FEATURE_PARTITIONED_BIT_NV = 0x00000100,
35 | VK_SUBGROUP_FEATURE_FLAG_BITS_MAX_ENUM = 0x7FFFFFFF
36 | } VkSubgroupFeatureFlagBits;
37 | typedef VkFlags VkSubgroupFeatureFlags;
38 | typedef struct VkPhysicalDeviceSubgroupProperties
39 | {
40 | VkStructureType sType;
41 | void* pNext;
42 | uint32_t subgroupSize;
43 | VkShaderStageFlags supportedStages;
44 | VkSubgroupFeatureFlags supportedOperations;
45 | VkBool32 quadOperationsInAllStages;
46 | } VkPhysicalDeviceSubgroupProperties;
47 | #define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MAINTENANCE_3_PROPERTIES (VkStructureType)1000168000
48 | #define VK_STRUCTURE_TYPE_DESCRIPTOR_SET_LAYOUT_SUPPORT (VkStructureType)1000168001
49 | typedef struct VkPhysicalDeviceMaintenance3Properties
50 | {
51 | VkStructureType sType;
52 | void* pNext;
53 | uint32_t maxPerSetDescriptors;
54 | VkDeviceSize maxMemoryAllocationSize;
55 | } VkPhysicalDeviceMaintenance3Properties;
56 | typedef struct VkDescriptorSetLayoutSupport
57 | {
58 | VkStructureType sType;
59 | void* pNext;
60 | VkBool32 supported;
61 | } VkDescriptorSetLayoutSupport;
62 | typedef VkPhysicalDeviceMaintenance3Properties VkPhysicalDeviceMaintenance3PropertiesKHR;
63 | typedef VkDescriptorSetLayoutSupport VkDescriptorSetLayoutSupportKHR;
64 | typedef void(VKAPI_PTR* PFN_vkGetDescriptorSetLayoutSupportKHR)(VkDevice device, const VkDescriptorSetLayoutCreateInfo* pCreateInfo, VkDescriptorSetLayoutSupport* pSupport);
65 | #endif // VK_HEADER_VERSION < 70
66 |
67 | #if VK_HEADER_VERSION < 80
68 | #define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_8BIT_STORAGE_FEATURES_KHR (VkStructureType)1000177000
69 | typedef struct VkPhysicalDevice8BitStorageFeaturesKHR
70 | {
71 | VkStructureType sType;
72 | void* pNext;
73 | VkBool32 storageBuffer8BitAccess;
74 | VkBool32 uniformAndStorageBuffer8BitAccess;
75 | VkBool32 storagePushConstant8;
76 | } VkPhysicalDevice8BitStorageFeaturesKHR;
77 | #define VK_STRUCTURE_TYPE_ATTACHMENT_DESCRIPTION_2_KHR (VkStructureType)1000109000
78 | #define VK_STRUCTURE_TYPE_ATTACHMENT_REFERENCE_2_KHR (VkStructureType)1000109001
79 | #define VK_STRUCTURE_TYPE_SUBPASS_DESCRIPTION_2_KHR (VkStructureType)1000109002
80 | #define VK_STRUCTURE_TYPE_SUBPASS_DEPENDENCY_2_KHR (VkStructureType)1000109003
81 | #define VK_STRUCTURE_TYPE_RENDER_PASS_CREATE_INFO_2_KHR (VkStructureType)1000109004
82 | #define VK_STRUCTURE_TYPE_SUBPASS_BEGIN_INFO_KHR (VkStructureType)1000109005
83 | #define VK_STRUCTURE_TYPE_SUBPASS_END_INFO_KHR (VkStructureType)1000109006
84 | typedef struct VkAttachmentDescription2KHR
85 | {
86 | VkStructureType sType;
87 | const void* pNext;
88 | VkAttachmentDescriptionFlags flags;
89 | VkFormat format;
90 | VkSampleCountFlagBits samples;
91 | VkAttachmentLoadOp loadOp;
92 | VkAttachmentStoreOp storeOp;
93 | VkAttachmentLoadOp stencilLoadOp;
94 | VkAttachmentStoreOp stencilStoreOp;
95 | VkImageLayout initialLayout;
96 | VkImageLayout finalLayout;
97 | } VkAttachmentDescription2KHR;
98 | typedef struct VkAttachmentReference2KHR
99 | {
100 | VkStructureType sType;
101 | const void* pNext;
102 | uint32_t attachment;
103 | VkImageLayout layout;
104 | VkImageAspectFlags aspectMask;
105 | } VkAttachmentReference2KHR;
106 | typedef struct VkSubpassDescription2KHR
107 | {
108 | VkStructureType sType;
109 | const void* pNext;
110 | VkSubpassDescriptionFlags flags;
111 | VkPipelineBindPoint pipelineBindPoint;
112 | uint32_t viewMask;
113 | uint32_t inputAttachmentCount;
114 | const VkAttachmentReference2KHR* pInputAttachments;
115 | uint32_t colorAttachmentCount;
116 | const VkAttachmentReference2KHR* pColorAttachments;
117 | const VkAttachmentReference2KHR* pResolveAttachments;
118 | const VkAttachmentReference2KHR* pDepthStencilAttachment;
119 | uint32_t preserveAttachmentCount;
120 | const uint32_t* pPreserveAttachments;
121 | } VkSubpassDescription2KHR;
122 | typedef struct VkSubpassDependency2KHR
123 | {
124 | VkStructureType sType;
125 | const void* pNext;
126 | uint32_t srcSubpass;
127 | uint32_t dstSubpass;
128 | VkPipelineStageFlags srcStageMask;
129 | VkPipelineStageFlags dstStageMask;
130 | VkAccessFlags srcAccessMask;
131 | VkAccessFlags dstAccessMask;
132 | VkDependencyFlags dependencyFlags;
133 | int32_t viewOffset;
134 | } VkSubpassDependency2KHR;
135 | typedef struct VkRenderPassCreateInfo2KHR
136 | {
137 | VkStructureType sType;
138 | const void* pNext;
139 | VkRenderPassCreateFlags flags;
140 | uint32_t attachmentCount;
141 | const VkAttachmentDescription2KHR* pAttachments;
142 | uint32_t subpassCount;
143 | const VkSubpassDescription2KHR* pSubpasses;
144 | uint32_t dependencyCount;
145 | const VkSubpassDependency2KHR* pDependencies;
146 | uint32_t correlatedViewMaskCount;
147 | const uint32_t* pCorrelatedViewMasks;
148 | } VkRenderPassCreateInfo2KHR;
149 | typedef struct VkSubpassBeginInfoKHR
150 | {
151 | VkStructureType sType;
152 | const void* pNext;
153 | VkSubpassContents contents;
154 | } VkSubpassBeginInfoKHR;
155 |
156 | typedef struct VkSubpassEndInfoKHR
157 | {
158 | VkStructureType sType;
159 | const void* pNext;
160 | } VkSubpassEndInfoKHR;
161 | typedef VkResult(VKAPI_PTR* PFN_vkCreateRenderPass2KHR)(VkDevice device, const VkRenderPassCreateInfo2KHR* pCreateInfo, const VkAllocationCallbacks* pAllocator, VkRenderPass* pRenderPass);
162 | typedef void(VKAPI_PTR* PFN_vkCmdBeginRenderPass2KHR)(VkCommandBuffer commandBuffer, const VkRenderPassBeginInfo* pRenderPassBegin, const VkSubpassBeginInfoKHR* pSubpassBeginInfo);
163 | typedef void(VKAPI_PTR* PFN_vkCmdNextSubpass2KHR)(VkCommandBuffer commandBuffer, const VkSubpassBeginInfoKHR* pSubpassBeginInfo, const VkSubpassEndInfoKHR* pSubpassEndInfo);
164 | typedef void(VKAPI_PTR* PFN_vkCmdEndRenderPass2KHR)(VkCommandBuffer commandBuffer, const VkSubpassEndInfoKHR* pSubpassEndInfo);
165 | #endif // VK_HEADER_VERSION < 80
166 |
167 | #if VK_HEADER_VERSION < 95
168 | #define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FLOAT16_INT8_FEATURES_KHR (VkStructureType)1000082000
169 | typedef struct VkPhysicalDeviceFloat16Int8FeaturesKHR
170 | {
171 | VkStructureType sType;
172 | void* pNext;
173 | VkBool32 shaderFloat16;
174 | VkBool32 shaderInt8;
175 | } VkPhysicalDeviceFloat16Int8FeaturesKHR;
176 | #endif // VK_HEADER_VERSION < 95
177 |
178 | #if VK_HEADER_VERSION < 97
179 | #define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MEMORY_BUDGET_PROPERTIES_EXT (VkStructureType)1000237000
180 | typedef struct VkPhysicalDeviceMemoryBudgetPropertiesEXT
181 | {
182 | VkStructureType sType;
183 | void* pNext;
184 | VkDeviceSize heapBudget[VK_MAX_MEMORY_HEAPS];
185 | VkDeviceSize heapUsage[VK_MAX_MEMORY_HEAPS];
186 | } VkPhysicalDeviceMemoryBudgetPropertiesEXT;
187 | #endif // VK_HEADER_VERSION < 97
188 |
189 | #endif // NCNN_VULKAN_HEADER_FIX_H
190 |
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/ncnn-20210525-ubuntu-1604-shared/lib/cmake/ncnn/ncnn-release.cmake:
--------------------------------------------------------------------------------
1 | #----------------------------------------------------------------
2 | # Generated CMake target import file for configuration "Release".
3 | #----------------------------------------------------------------
4 |
5 | # Commands may need to know the format version.
6 | set(CMAKE_IMPORT_FILE_VERSION 1)
7 |
8 | # Import target "ncnn" for configuration "Release"
9 | set_property(TARGET ncnn APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
10 | set_target_properties(ncnn PROPERTIES
11 | IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libncnn.so.1.0.20210525"
12 | IMPORTED_SONAME_RELEASE "libncnn.so.1"
13 | )
14 |
15 | list(APPEND _IMPORT_CHECK_TARGETS ncnn )
16 | list(APPEND _IMPORT_CHECK_FILES_FOR_ncnn "${_IMPORT_PREFIX}/lib/libncnn.so.1.0.20210525" )
17 |
18 | # Commands beyond this point should not need to know the version.
19 | set(CMAKE_IMPORT_FILE_VERSION)
20 |
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/ncnn-20210525-ubuntu-1604-shared/lib/cmake/ncnn/ncnn.cmake:
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1 | # Generated by CMake
2 |
3 | if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5)
4 | message(FATAL_ERROR "CMake >= 2.6.0 required")
5 | endif()
6 | cmake_policy(PUSH)
7 | cmake_policy(VERSION 2.6...3.18)
8 | #----------------------------------------------------------------
9 | # Generated CMake target import file.
10 | #----------------------------------------------------------------
11 |
12 | # Commands may need to know the format version.
13 | set(CMAKE_IMPORT_FILE_VERSION 1)
14 |
15 | # Protect against multiple inclusion, which would fail when already imported targets are added once more.
16 | set(_targetsDefined)
17 | set(_targetsNotDefined)
18 | set(_expectedTargets)
19 | foreach(_expectedTarget ncnn)
20 | list(APPEND _expectedTargets ${_expectedTarget})
21 | if(NOT TARGET ${_expectedTarget})
22 | list(APPEND _targetsNotDefined ${_expectedTarget})
23 | endif()
24 | if(TARGET ${_expectedTarget})
25 | list(APPEND _targetsDefined ${_expectedTarget})
26 | endif()
27 | endforeach()
28 | if("${_targetsDefined}" STREQUAL "${_expectedTargets}")
29 | unset(_targetsDefined)
30 | unset(_targetsNotDefined)
31 | unset(_expectedTargets)
32 | set(CMAKE_IMPORT_FILE_VERSION)
33 | cmake_policy(POP)
34 | return()
35 | endif()
36 | if(NOT "${_targetsDefined}" STREQUAL "")
37 | message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n")
38 | endif()
39 | unset(_targetsDefined)
40 | unset(_targetsNotDefined)
41 | unset(_expectedTargets)
42 |
43 |
44 | # Compute the installation prefix relative to this file.
45 | get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
46 | get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
47 | get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
48 | get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
49 | if(_IMPORT_PREFIX STREQUAL "/")
50 | set(_IMPORT_PREFIX "")
51 | endif()
52 |
53 | # Create imported target ncnn
54 | add_library(ncnn SHARED IMPORTED)
55 |
56 | set_target_properties(ncnn PROPERTIES
57 | INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include/ncnn"
58 | INTERFACE_LINK_LIBRARIES "OpenMP::OpenMP_CXX;Threads::Threads;Vulkan::Vulkan"
59 | INTERFACE_POSITION_INDEPENDENT_CODE "ON"
60 | )
61 |
62 | if(CMAKE_VERSION VERSION_LESS 2.8.12)
63 | message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
64 | endif()
65 |
66 | # Load information for each installed configuration.
67 | get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
68 | file(GLOB CONFIG_FILES "${_DIR}/ncnn-*.cmake")
69 | foreach(f ${CONFIG_FILES})
70 | include(${f})
71 | endforeach()
72 |
73 | # Cleanup temporary variables.
74 | set(_IMPORT_PREFIX)
75 |
76 | # Loop over all imported files and verify that they actually exist
77 | foreach(target ${_IMPORT_CHECK_TARGETS} )
78 | foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} )
79 | if(NOT EXISTS "${file}" )
80 | message(FATAL_ERROR "The imported target \"${target}\" references the file
81 | \"${file}\"
82 | but this file does not exist. Possible reasons include:
83 | * The file was deleted, renamed, or moved to another location.
84 | * An install or uninstall procedure did not complete successfully.
85 | * The installation package was faulty and contained
86 | \"${CMAKE_CURRENT_LIST_FILE}\"
87 | but not all the files it references.
88 | ")
89 | endif()
90 | endforeach()
91 | unset(_IMPORT_CHECK_FILES_FOR_${target})
92 | endforeach()
93 | unset(_IMPORT_CHECK_TARGETS)
94 |
95 | # This file does not depend on other imported targets which have
96 | # been exported from the same project but in a separate export set.
97 |
98 | # Commands beyond this point should not need to know the version.
99 | set(CMAKE_IMPORT_FILE_VERSION)
100 | cmake_policy(POP)
101 |
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/ncnn-20210525-ubuntu-1604-shared/lib/cmake/ncnn/ncnnConfig.cmake:
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1 | set(NCNN_OPENMP ON)
2 | set(NCNN_THREADS ON)
3 | set(NCNN_VULKAN ON)
4 | set(NCNN_SHARED_LIB ON)
5 | set(NCNN_SYSTEM_GLSLANG OFF)
6 |
7 | if(NCNN_OPENMP)
8 | find_package(OpenMP)
9 | endif()
10 |
11 | if(NCNN_THREADS)
12 | set(CMAKE_THREAD_PREFER_PTHREAD TRUE)
13 | set(THREADS_PREFER_PTHREAD_FLAG TRUE)
14 | find_package(Threads REQUIRED)
15 | endif()
16 |
17 | if(NCNN_VULKAN)
18 | find_package(Vulkan REQUIRED)
19 |
20 | if(NOT NCNN_SHARED_LIB)
21 | if(NCNN_SYSTEM_GLSLANG)
22 | set(GLSLANG_TARGET_DIR "")
23 | else()
24 | set(GLSLANG_TARGET_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../lib/cmake")
25 | endif(NCNN_SYSTEM_GLSLANG)
26 |
27 | include(${GLSLANG_TARGET_DIR}/OSDependentTargets.cmake)
28 | include(${GLSLANG_TARGET_DIR}/OGLCompilerTargets.cmake)
29 | if(EXISTS "${GLSLANG_TARGET_DIR}/HLSLTargets.cmake")
30 | # hlsl support can be optional
31 | include("${GLSLANG_TARGET_DIR}/HLSLTargets.cmake")
32 | endif()
33 | include(${GLSLANG_TARGET_DIR}/glslangTargets.cmake)
34 | include(${GLSLANG_TARGET_DIR}/SPIRVTargets.cmake)
35 | endif()
36 | endif(NCNN_VULKAN)
37 |
38 | include(${CMAKE_CURRENT_LIST_DIR}/ncnn.cmake)
39 |
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1 | 7767517
2 | 176 200
3 | Input images 0 1 images
4 | YoloV5Focus focus 1 1 images 167
5 | Convolution Conv_41 1 1 167 168 0=32 1=3 4=1 5=1 6=3456
6 | Swish Mul_43 1 1 168 170
7 | Convolution Conv_44 1 1 170 171 0=64 1=3 3=2 4=1 5=1 6=18432
8 | Swish Mul_46 1 1 171 173
9 | Split splitncnn_0 1 2 173 173_splitncnn_0 173_splitncnn_1
10 | Convolution Conv_47 1 1 173_splitncnn_1 174 0=32 1=1 5=1 6=2048
11 | Swish Mul_49 1 1 174 176
12 | Split splitncnn_1 1 2 176 176_splitncnn_0 176_splitncnn_1
13 | Convolution Conv_50 1 1 176_splitncnn_1 177 0=32 1=1 5=1 6=1024
14 | Swish Mul_52 1 1 177 179
15 | Convolution Conv_53 1 1 179 180 0=32 1=3 4=1 5=1 6=9216
16 | Swish Mul_55 1 1 180 182
17 | BinaryOp Add_56 2 1 176_splitncnn_0 182 183
18 | Convolution Conv_57 1 1 173_splitncnn_0 184 0=32 1=1 5=1 6=2048
19 | Swish Mul_59 1 1 184 186
20 | Concat Concat_60 2 1 183 186 187
21 | Convolution Conv_61 1 1 187 188 0=64 1=1 5=1 6=4096
22 | Swish Mul_63 1 1 188 190
23 | Convolution Conv_64 1 1 190 191 0=128 1=3 3=2 4=1 5=1 6=73728
24 | Swish Mul_66 1 1 191 193
25 | Split splitncnn_2 1 2 193 193_splitncnn_0 193_splitncnn_1
26 | Convolution Conv_67 1 1 193_splitncnn_1 194 0=64 1=1 5=1 6=8192
27 | Swish Mul_69 1 1 194 196
28 | Split splitncnn_3 1 2 196 196_splitncnn_0 196_splitncnn_1
29 | Convolution Conv_70 1 1 196_splitncnn_1 197 0=64 1=1 5=1 6=4096
30 | Swish Mul_72 1 1 197 199
31 | Convolution Conv_73 1 1 199 200 0=64 1=3 4=1 5=1 6=36864
32 | Swish Mul_75 1 1 200 202
33 | BinaryOp Add_76 2 1 196_splitncnn_0 202 203
34 | Split splitncnn_4 1 2 203 203_splitncnn_0 203_splitncnn_1
35 | Convolution Conv_77 1 1 203_splitncnn_1 204 0=64 1=1 5=1 6=4096
36 | Swish Mul_79 1 1 204 206
37 | Convolution Conv_80 1 1 206 207 0=64 1=3 4=1 5=1 6=36864
38 | Swish Mul_82 1 1 207 209
39 | BinaryOp Add_83 2 1 203_splitncnn_0 209 210
40 | Split splitncnn_5 1 2 210 210_splitncnn_0 210_splitncnn_1
41 | Convolution Conv_84 1 1 210_splitncnn_1 211 0=64 1=1 5=1 6=4096
42 | Swish Mul_86 1 1 211 213
43 | Convolution Conv_87 1 1 213 214 0=64 1=3 4=1 5=1 6=36864
44 | Swish Mul_89 1 1 214 216
45 | BinaryOp Add_90 2 1 210_splitncnn_0 216 217
46 | Convolution Conv_91 1 1 193_splitncnn_0 218 0=64 1=1 5=1 6=8192
47 | Swish Mul_93 1 1 218 220
48 | Concat Concat_94 2 1 217 220 221
49 | Convolution Conv_95 1 1 221 222 0=128 1=1 5=1 6=16384
50 | Swish Mul_97 1 1 222 224
51 | Split splitncnn_6 1 2 224 224_splitncnn_0 224_splitncnn_1
52 | Convolution Conv_98 1 1 224_splitncnn_1 225 0=256 1=3 3=2 4=1 5=1 6=294912
53 | Swish Mul_100 1 1 225 227
54 | Split splitncnn_7 1 2 227 227_splitncnn_0 227_splitncnn_1
55 | Convolution Conv_101 1 1 227_splitncnn_1 228 0=128 1=1 5=1 6=32768
56 | Swish Mul_103 1 1 228 230
57 | Split splitncnn_8 1 2 230 230_splitncnn_0 230_splitncnn_1
58 | Convolution Conv_104 1 1 230_splitncnn_1 231 0=128 1=1 5=1 6=16384
59 | Swish Mul_106 1 1 231 233
60 | Convolution Conv_107 1 1 233 234 0=128 1=3 4=1 5=1 6=147456
61 | Swish Mul_109 1 1 234 236
62 | BinaryOp Add_110 2 1 230_splitncnn_0 236 237
63 | Split splitncnn_9 1 2 237 237_splitncnn_0 237_splitncnn_1
64 | Convolution Conv_111 1 1 237_splitncnn_1 238 0=128 1=1 5=1 6=16384
65 | Swish Mul_113 1 1 238 240
66 | Convolution Conv_114 1 1 240 241 0=128 1=3 4=1 5=1 6=147456
67 | Swish Mul_116 1 1 241 243
68 | BinaryOp Add_117 2 1 237_splitncnn_0 243 244
69 | Split splitncnn_10 1 2 244 244_splitncnn_0 244_splitncnn_1
70 | Convolution Conv_118 1 1 244_splitncnn_1 245 0=128 1=1 5=1 6=16384
71 | Swish Mul_120 1 1 245 247
72 | Convolution Conv_121 1 1 247 248 0=128 1=3 4=1 5=1 6=147456
73 | Swish Mul_123 1 1 248 250
74 | BinaryOp Add_124 2 1 244_splitncnn_0 250 251
75 | Convolution Conv_125 1 1 227_splitncnn_0 252 0=128 1=1 5=1 6=32768
76 | Swish Mul_127 1 1 252 254
77 | Concat Concat_128 2 1 251 254 255
78 | Convolution Conv_129 1 1 255 256 0=256 1=1 5=1 6=65536
79 | Swish Mul_131 1 1 256 258
80 | Split splitncnn_11 1 2 258 258_splitncnn_0 258_splitncnn_1
81 | Convolution Conv_132 1 1 258_splitncnn_1 259 0=512 1=3 3=2 4=1 5=1 6=1179648
82 | Swish Mul_134 1 1 259 261
83 | Convolution Conv_135 1 1 261 262 0=256 1=1 5=1 6=131072
84 | Swish Mul_137 1 1 262 264
85 | Split splitncnn_12 1 4 264 264_splitncnn_0 264_splitncnn_1 264_splitncnn_2 264_splitncnn_3
86 | Pooling MaxPool_138 1 1 264_splitncnn_3 265 1=5 3=2 5=1
87 | Pooling MaxPool_139 1 1 264_splitncnn_2 266 1=9 3=4 5=1
88 | Pooling MaxPool_140 1 1 264_splitncnn_1 267 1=13 3=6 5=1
89 | Concat Concat_141 4 1 264_splitncnn_0 265 266 267 268
90 | Convolution Conv_142 1 1 268 269 0=512 1=1 5=1 6=524288
91 | Swish Mul_144 1 1 269 271
92 | Split splitncnn_13 1 2 271 271_splitncnn_0 271_splitncnn_1
93 | Convolution Conv_145 1 1 271_splitncnn_1 272 0=256 1=1 5=1 6=131072
94 | Swish Mul_147 1 1 272 274
95 | Convolution Conv_148 1 1 274 275 0=256 1=1 5=1 6=65536
96 | Swish Mul_150 1 1 275 277
97 | Convolution Conv_151 1 1 277 278 0=256 1=3 4=1 5=1 6=589824
98 | Swish Mul_153 1 1 278 280
99 | Convolution Conv_154 1 1 271_splitncnn_0 281 0=256 1=1 5=1 6=131072
100 | Swish Mul_156 1 1 281 283
101 | Concat Concat_157 2 1 280 283 284
102 | Convolution Conv_158 1 1 284 285 0=512 1=1 5=1 6=262144
103 | Swish Mul_160 1 1 285 287
104 | Convolution Conv_161 1 1 287 288 0=256 1=1 5=1 6=131072
105 | Swish Mul_163 1 1 288 290
106 | Split splitncnn_14 1 2 290 290_splitncnn_0 290_splitncnn_1
107 | Interp Resize_165 1 1 290_splitncnn_1 295 0=1 1=2.000000e+00 2=2.000000e+00
108 | Concat Concat_166 2 1 295 258_splitncnn_0 296
109 | Split splitncnn_15 1 2 296 296_splitncnn_0 296_splitncnn_1
110 | Convolution Conv_167 1 1 296_splitncnn_1 297 0=128 1=1 5=1 6=65536
111 | Swish Mul_169 1 1 297 299
112 | Convolution Conv_170 1 1 299 300 0=128 1=1 5=1 6=16384
113 | Swish Mul_172 1 1 300 302
114 | Convolution Conv_173 1 1 302 303 0=128 1=3 4=1 5=1 6=147456
115 | Swish Mul_175 1 1 303 305
116 | Convolution Conv_176 1 1 296_splitncnn_0 306 0=128 1=1 5=1 6=65536
117 | Swish Mul_178 1 1 306 308
118 | Concat Concat_179 2 1 305 308 309
119 | Convolution Conv_180 1 1 309 310 0=256 1=1 5=1 6=65536
120 | Swish Mul_182 1 1 310 312
121 | Convolution Conv_183 1 1 312 313 0=128 1=1 5=1 6=32768
122 | Swish Mul_185 1 1 313 315
123 | Split splitncnn_16 1 2 315 315_splitncnn_0 315_splitncnn_1
124 | Interp Resize_187 1 1 315_splitncnn_1 320 0=1 1=2.000000e+00 2=2.000000e+00
125 | Concat Concat_188 2 1 320 224_splitncnn_0 321
126 | Split splitncnn_17 1 2 321 321_splitncnn_0 321_splitncnn_1
127 | Convolution Conv_189 1 1 321_splitncnn_1 322 0=64 1=1 5=1 6=16384
128 | Swish Mul_191 1 1 322 324
129 | Convolution Conv_192 1 1 324 325 0=64 1=1 5=1 6=4096
130 | Swish Mul_194 1 1 325 327
131 | Convolution Conv_195 1 1 327 328 0=64 1=3 4=1 5=1 6=36864
132 | Swish Mul_197 1 1 328 330
133 | Convolution Conv_198 1 1 321_splitncnn_0 331 0=64 1=1 5=1 6=16384
134 | Swish Mul_200 1 1 331 333
135 | Concat Concat_201 2 1 330 333 334
136 | Convolution Conv_202 1 1 334 335 0=128 1=1 5=1 6=16384
137 | Swish Mul_204 1 1 335 337
138 | Split splitncnn_18 1 2 337 337_splitncnn_0 337_splitncnn_1
139 | Convolution Conv_205 1 1 337_splitncnn_1 338 0=128 1=3 3=2 4=1 5=1 6=147456
140 | Swish Mul_207 1 1 338 340
141 | Concat Concat_208 2 1 340 315_splitncnn_0 341
142 | Split splitncnn_19 1 2 341 341_splitncnn_0 341_splitncnn_1
143 | Convolution Conv_209 1 1 341_splitncnn_1 342 0=128 1=1 5=1 6=32768
144 | Swish Mul_211 1 1 342 344
145 | Convolution Conv_212 1 1 344 345 0=128 1=1 5=1 6=16384
146 | Swish Mul_214 1 1 345 347
147 | Convolution Conv_215 1 1 347 348 0=128 1=3 4=1 5=1 6=147456
148 | Swish Mul_217 1 1 348 350
149 | Convolution Conv_218 1 1 341_splitncnn_0 351 0=128 1=1 5=1 6=32768
150 | Swish Mul_220 1 1 351 353
151 | Concat Concat_221 2 1 350 353 354
152 | Convolution Conv_222 1 1 354 355 0=256 1=1 5=1 6=65536
153 | Swish Mul_224 1 1 355 357
154 | Split splitncnn_20 1 2 357 357_splitncnn_0 357_splitncnn_1
155 | Convolution Conv_225 1 1 357_splitncnn_1 358 0=256 1=3 3=2 4=1 5=1 6=589824
156 | Swish Mul_227 1 1 358 360
157 | Concat Concat_228 2 1 360 290_splitncnn_0 361
158 | Split splitncnn_21 1 2 361 361_splitncnn_0 361_splitncnn_1
159 | Convolution Conv_229 1 1 361_splitncnn_1 362 0=256 1=1 5=1 6=131072
160 | Swish Mul_231 1 1 362 364
161 | Convolution Conv_232 1 1 364 365 0=256 1=1 5=1 6=65536
162 | Swish Mul_234 1 1 365 367
163 | Convolution Conv_235 1 1 367 368 0=256 1=3 4=1 5=1 6=589824
164 | Swish Mul_237 1 1 368 370
165 | Convolution Conv_238 1 1 361_splitncnn_0 371 0=256 1=1 5=1 6=131072
166 | Swish Mul_240 1 1 371 373
167 | Concat Concat_241 2 1 370 373 374
168 | Convolution Conv_242 1 1 374 375 0=512 1=1 5=1 6=262144
169 | Swish Mul_244 1 1 375 377
170 | Convolution Conv_245 1 1 337_splitncnn_0 378 0=255 1=1 5=1 6=32640
171 | Reshape Reshape_259 1 1 378 396 0=-1 1=85 2=3
172 | Permute Transpose_260 1 1 396 397 0=1
173 | Convolution Conv_261 1 1 357_splitncnn_0 398 0=255 1=1 5=1 6=65280
174 | Reshape Reshape_275 1 1 398 416 0=-1 1=85 2=3
175 | Permute Transpose_276 1 1 416 417 0=1
176 | Convolution Conv_277 1 1 377 418 0=255 1=1 5=1 6=130560
177 | Reshape Reshape_291 1 1 418 436 0=-1 1=85 2=3
178 | Permute Transpose_292 1 1 436 437 0=1
179 |
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