CMakeCache.txt

@@ -0,0 +1,622 @@
+# This is the CMakeCache file.
+# For build in directory: /home/cl/onnx_run/yolo_crowd_run
+# It was generated by CMake: /usr/bin/cmake
+# You can edit this file to change values found and used by cmake.
+# If you do not want to change any of the values, simply exit the editor.
+# If you do want to change a value, simply edit, save, and exit the editor.
+# The syntax for the file is as follows:
+# KEY:TYPE=VALUE
+# KEY is the name of a variable in the cache.
+# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!.
+# VALUE is the current value for the KEY.
+
+########################
+# EXTERNAL cache entries
+########################
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+// MinSizeRel ...
+CMAKE_BUILD_TYPE:STRING=
+
+//Enable/Disable color output during build.
+CMAKE_COLOR_MAKEFILE:BOOL=ON
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+//CXX compiler
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+//A wrapper around 'ar' adding the appropriate '--plugin' option
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+//A wrapper around 'ranlib' adding the appropriate '--plugin' option
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+//Flags used by the CXX compiler during RELEASE builds.
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+//Flags used by the CXX compiler during RELWITHDEBINFO builds.
+CMAKE_CXX_FLAGS_RELWITHDEBINFO:STRING=-O2 -g -DNDEBUG
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+//C compiler
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+//A wrapper around 'ar' adding the appropriate '--plugin' option
+// for the GCC compiler
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+//A wrapper around 'ranlib' adding the appropriate '--plugin' option
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+//Flags used by the linker during the creation of modules during
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+//Flags used by the linker during the creation of modules during
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+//Path to a program.
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+//Path to a program.
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+//Path to a program.
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+//Value Computed by CMake
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+//Value Computed by CMake
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+//Flags used by the linker during the creation of shared libraries
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+//Flags used by the linker during the creation of shared libraries
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+//Flags used by the linker during the creation of shared libraries
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+//Flags used by the linker during the creation of shared libraries
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+//Flags used by the linker during the creation of shared libraries
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+
+//If set, runtime paths are not added when installing shared libraries,
+// but are added when building.
+CMAKE_SKIP_INSTALL_RPATH:BOOL=NO
+
+//If set, runtime paths are not added when using shared libraries.
+CMAKE_SKIP_RPATH:BOOL=NO
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+//Flags used by the linker during the creation of static libraries
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+//Flags used by the linker during the creation of static libraries
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+//Flags used by the linker during the creation of static libraries
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+//Flags used by the linker during the creation of static libraries
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+//Path to a file.
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+//Compile CUDA objects with separable compilation enabled.  Requires
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+
+#elif defined(__IBMC__) && !defined(__COMPILER_VER__) && __IBMC__ < 800
+# define COMPILER_ID "VisualAge"
+  /* __IBMC__ = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100)
+# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__IBMC__    % 10)
+
+#elif defined(__PGI)
+# define COMPILER_ID "PGI"
+# define COMPILER_VERSION_MAJOR DEC(__PGIC__)
+# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__)
+# if defined(__PGIC_PATCHLEVEL__)
+#  define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__)
+# endif
+
+#elif defined(_CRAYC)
+# define COMPILER_ID "Cray"
+# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR)
+# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR)
+
+#elif defined(__TI_COMPILER_VERSION__)
+# define COMPILER_ID "TI"
+  /* __TI_COMPILER_VERSION__ = VVVRRRPPP */
+# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000)
+# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000   % 1000)
+# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__        % 1000)
+
+#elif defined(__FUJITSU) || defined(__FCC_VERSION) || defined(__fcc_version)
+# define COMPILER_ID "Fujitsu"
+
+#elif defined(__ghs__)
+# define COMPILER_ID "GHS"
+/* __GHS_VERSION_NUMBER = VVVVRP */
+# ifdef __GHS_VERSION_NUMBER
+# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100)
+# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10)
+# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER      % 10)
+# endif
+
+#elif defined(__TINYC__)
+# define COMPILER_ID "TinyCC"
+
+#elif defined(__BCC__)
+# define COMPILER_ID "Bruce"
+
+#elif defined(__SCO_VERSION__)
+# define COMPILER_ID "SCO"
+
+#elif defined(__ARMCC_VERSION) && !defined(__clang__)
+# define COMPILER_ID "ARMCC"
+#if __ARMCC_VERSION >= 1000000
+  /* __ARMCC_VERSION = VRRPPPP */
+  # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000)
+  # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100)
+  # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION     % 10000)
+#else
+  /* __ARMCC_VERSION = VRPPPP */
+  # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000)
+  # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10)
+  # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION    % 10000)
+#endif
+
+
+#elif defined(__clang__) && defined(__apple_build_version__)
+# define COMPILER_ID "AppleClang"
+# if defined(_MSC_VER)
+#  define SIMULATE_ID "MSVC"
+# endif
+# define COMPILER_VERSION_MAJOR DEC(__clang_major__)
+# define COMPILER_VERSION_MINOR DEC(__clang_minor__)
+# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+#  define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__)
+
+#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION)
+# define COMPILER_ID "ARMClang"
+  # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000)
+  # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100)
+  # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION     % 10000)
+# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION)
+
+#elif defined(__clang__)
+# define COMPILER_ID "Clang"
+# if defined(_MSC_VER)
+#  define SIMULATE_ID "MSVC"
+# endif
+# define COMPILER_VERSION_MAJOR DEC(__clang_major__)
+# define COMPILER_VERSION_MINOR DEC(__clang_minor__)
+# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__)
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+#  define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+
+#elif defined(__GNUC__)
+# define COMPILER_ID "GNU"
+# define COMPILER_VERSION_MAJOR DEC(__GNUC__)
+# if defined(__GNUC_MINOR__)
+#  define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+#  define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+# endif
+
+#elif defined(_MSC_VER)
+# define COMPILER_ID "MSVC"
+  /* _MSC_VER = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100)
+# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100)
+# if defined(_MSC_FULL_VER)
+#  if _MSC_VER >= 1400
+    /* _MSC_FULL_VER = VVRRPPPPP */
+#   define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000)
+#  else
+    /* _MSC_FULL_VER = VVRRPPPP */
+#   define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000)
+#  endif
+# endif
+# if defined(_MSC_BUILD)
+#  define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD)
+# endif
+
+#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__)
+# define COMPILER_ID "ADSP"
+#if defined(__VISUALDSPVERSION__)
+  /* __VISUALDSPVERSION__ = 0xVVRRPP00 */
+# define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24)
+# define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF)
+# define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8  & 0xFF)
+#endif
+
+#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC)
+# define COMPILER_ID "IAR"
+# if defined(__VER__) && defined(__ICCARM__)
+#  define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000)
+#  define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000)
+#  define COMPILER_VERSION_PATCH DEC((__VER__) % 1000)
+#  define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__)
+# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__))
+#  define COMPILER_VERSION_MAJOR DEC((__VER__) / 100)
+#  define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100))
+#  define COMPILER_VERSION_PATCH DEC(__SUBVERSION__)
+#  define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__)
+# endif
+
+#elif defined(__SDCC_VERSION_MAJOR) || defined(SDCC)
+# define COMPILER_ID "SDCC"
+# if defined(__SDCC_VERSION_MAJOR)
+#  define COMPILER_VERSION_MAJOR DEC(__SDCC_VERSION_MAJOR)
+#  define COMPILER_VERSION_MINOR DEC(__SDCC_VERSION_MINOR)
+#  define COMPILER_VERSION_PATCH DEC(__SDCC_VERSION_PATCH)
+# else
+  /* SDCC = VRP */
+#  define COMPILER_VERSION_MAJOR DEC(SDCC/100)
+#  define COMPILER_VERSION_MINOR DEC(SDCC/10 % 10)
+#  define COMPILER_VERSION_PATCH DEC(SDCC    % 10)
+# endif
+
+
+/* These compilers are either not known or too old to define an
+  identification macro.  Try to identify the platform and guess that
+  it is the native compiler.  */
+#elif defined(__hpux) || defined(__hpua)
+# define COMPILER_ID "HP"
+
+#else /* unknown compiler */
+# define COMPILER_ID ""
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+   getting matched.  Store it in a pointer rather than an array
+   because some compilers will just produce instructions to fill the
+   array rather than assigning a pointer to a static array.  */
+char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]";
+#ifdef SIMULATE_ID
+char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]";
+#endif
+
+#ifdef __QNXNTO__
+char const* qnxnto = "INFO" ":" "qnxnto[]";
+#endif
+
+#if defined(__CRAYXE) || defined(__CRAYXC)
+char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]";
+#endif
+
+#define STRINGIFY_HELPER(X) #X
+#define STRINGIFY(X) STRINGIFY_HELPER(X)
+
+/* Identify known platforms by name.  */
+#if defined(__linux) || defined(__linux__) || defined(linux)
+# define PLATFORM_ID "Linux"
+
+#elif defined(__CYGWIN__)
+# define PLATFORM_ID "Cygwin"
+
+#elif defined(__MINGW32__)
+# define PLATFORM_ID "MinGW"
+
+#elif defined(__APPLE__)
+# define PLATFORM_ID "Darwin"
+
+#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32)
+# define PLATFORM_ID "Windows"
+
+#elif defined(__FreeBSD__) || defined(__FreeBSD)
+# define PLATFORM_ID "FreeBSD"
+
+#elif defined(__NetBSD__) || defined(__NetBSD)
+# define PLATFORM_ID "NetBSD"
+
+#elif defined(__OpenBSD__) || defined(__OPENBSD)
+# define PLATFORM_ID "OpenBSD"
+
+#elif defined(__sun) || defined(sun)
+# define PLATFORM_ID "SunOS"
+
+#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__)
+# define PLATFORM_ID "AIX"
+
+#elif defined(__hpux) || defined(__hpux__)
+# define PLATFORM_ID "HP-UX"
+
+#elif defined(__HAIKU__)
+# define PLATFORM_ID "Haiku"
+
+#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS)
+# define PLATFORM_ID "BeOS"
+
+#elif defined(__QNX__) || defined(__QNXNTO__)
+# define PLATFORM_ID "QNX"
+
+#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__)
+# define PLATFORM_ID "Tru64"
+
+#elif defined(__riscos) || defined(__riscos__)
+# define PLATFORM_ID "RISCos"
+
+#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__)
+# define PLATFORM_ID "SINIX"
+
+#elif defined(__UNIX_SV__)
+# define PLATFORM_ID "UNIX_SV"
+
+#elif defined(__bsdos__)
+# define PLATFORM_ID "BSDOS"
+
+#elif defined(_MPRAS) || defined(MPRAS)
+# define PLATFORM_ID "MP-RAS"
+
+#elif defined(__osf) || defined(__osf__)
+# define PLATFORM_ID "OSF1"
+
+#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv)
+# define PLATFORM_ID "SCO_SV"
+
+#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX)
+# define PLATFORM_ID "ULTRIX"
+
+#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX)
+# define PLATFORM_ID "Xenix"
+
+#elif defined(__WATCOMC__)
+# if defined(__LINUX__)
+#  define PLATFORM_ID "Linux"
+
+# elif defined(__DOS__)
+#  define PLATFORM_ID "DOS"
+
+# elif defined(__OS2__)
+#  define PLATFORM_ID "OS2"
+
+# elif defined(__WINDOWS__)
+#  define PLATFORM_ID "Windows3x"
+
+# else /* unknown platform */
+#  define PLATFORM_ID
+# endif
+
+#elif defined(__INTEGRITY)
+# if defined(INT_178B)
+#  define PLATFORM_ID "Integrity178"
+
+# else /* regular Integrity */
+#  define PLATFORM_ID "Integrity"
+# endif
+
+#else /* unknown platform */
+# define PLATFORM_ID
+
+#endif
+
+/* For windows compilers MSVC and Intel we can determine
+   the architecture of the compiler being used.  This is because
+   the compilers do not have flags that can change the architecture,
+   but rather depend on which compiler is being used
+*/
+#if defined(_WIN32) && defined(_MSC_VER)
+# if defined(_M_IA64)
+#  define ARCHITECTURE_ID "IA64"
+
+# elif defined(_M_X64) || defined(_M_AMD64)
+#  define ARCHITECTURE_ID "x64"
+
+# elif defined(_M_IX86)
+#  define ARCHITECTURE_ID "X86"
+
+# elif defined(_M_ARM64)
+#  define ARCHITECTURE_ID "ARM64"
+
+# elif defined(_M_ARM)
+#  if _M_ARM == 4
+#   define ARCHITECTURE_ID "ARMV4I"
+#  elif _M_ARM == 5
+#   define ARCHITECTURE_ID "ARMV5I"
+#  else
+#   define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM)
+#  endif
+
+# elif defined(_M_MIPS)
+#  define ARCHITECTURE_ID "MIPS"
+
+# elif defined(_M_SH)
+#  define ARCHITECTURE_ID "SHx"
+
+# else /* unknown architecture */
+#  define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__WATCOMC__)
+# if defined(_M_I86)
+#  define ARCHITECTURE_ID "I86"
+
+# elif defined(_M_IX86)
+#  define ARCHITECTURE_ID "X86"
+
+# else /* unknown architecture */
+#  define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC)
+# if defined(__ICCARM__)
+#  define ARCHITECTURE_ID "ARM"
+
+# elif defined(__ICCRX__)
+#  define ARCHITECTURE_ID "RX"
+
+# elif defined(__ICCRH850__)
+#  define ARCHITECTURE_ID "RH850"
+
+# elif defined(__ICCRL78__)
+#  define ARCHITECTURE_ID "RL78"
+
+# elif defined(__ICCRISCV__)
+#  define ARCHITECTURE_ID "RISCV"
+
+# elif defined(__ICCAVR__)
+#  define ARCHITECTURE_ID "AVR"
+
+# elif defined(__ICC430__)
+#  define ARCHITECTURE_ID "MSP430"
+
+# elif defined(__ICCV850__)
+#  define ARCHITECTURE_ID "V850"
+
+# elif defined(__ICC8051__)
+#  define ARCHITECTURE_ID "8051"
+
+# else /* unknown architecture */
+#  define ARCHITECTURE_ID ""
+# endif
+
+#elif defined(__ghs__)
+# if defined(__PPC64__)
+#  define ARCHITECTURE_ID "PPC64"
+
+# elif defined(__ppc__)
+#  define ARCHITECTURE_ID "PPC"
+
+# elif defined(__ARM__)
+#  define ARCHITECTURE_ID "ARM"
+
+# elif defined(__x86_64__)
+#  define ARCHITECTURE_ID "x64"
+
+# elif defined(__i386__)
+#  define ARCHITECTURE_ID "X86"
+
+# else /* unknown architecture */
+#  define ARCHITECTURE_ID ""
+# endif
+#else
+#  define ARCHITECTURE_ID
+#endif
+
+/* Convert integer to decimal digit literals.  */
+#define DEC(n)                   \
+  ('0' + (((n) / 10000000)%10)), \
+  ('0' + (((n) / 1000000)%10)),  \
+  ('0' + (((n) / 100000)%10)),   \
+  ('0' + (((n) / 10000)%10)),    \
+  ('0' + (((n) / 1000)%10)),     \
+  ('0' + (((n) / 100)%10)),      \
+  ('0' + (((n) / 10)%10)),       \
+  ('0' +  ((n) % 10))
+
+/* Convert integer to hex digit literals.  */
+#define HEX(n)             \
+  ('0' + ((n)>>28 & 0xF)), \
+  ('0' + ((n)>>24 & 0xF)), \
+  ('0' + ((n)>>20 & 0xF)), \
+  ('0' + ((n)>>16 & 0xF)), \
+  ('0' + ((n)>>12 & 0xF)), \
+  ('0' + ((n)>>8  & 0xF)), \
+  ('0' + ((n)>>4  & 0xF)), \
+  ('0' + ((n)     & 0xF))
+
+/* Construct a string literal encoding the version number components. */
+#ifdef COMPILER_VERSION_MAJOR
+char const info_version[] = {
+  'I', 'N', 'F', 'O', ':',
+  'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[',
+  COMPILER_VERSION_MAJOR,
+# ifdef COMPILER_VERSION_MINOR
+  '.', COMPILER_VERSION_MINOR,
+#  ifdef COMPILER_VERSION_PATCH
+   '.', COMPILER_VERSION_PATCH,
+#   ifdef COMPILER_VERSION_TWEAK
+    '.', COMPILER_VERSION_TWEAK,
+#   endif
+#  endif
+# endif
+  ']','\0'};
+#endif
+
+/* Construct a string literal encoding the internal version number. */
+#ifdef COMPILER_VERSION_INTERNAL
+char const info_version_internal[] = {
+  'I', 'N', 'F', 'O', ':',
+  'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_',
+  'i','n','t','e','r','n','a','l','[',
+  COMPILER_VERSION_INTERNAL,']','\0'};
+#endif
+
+/* Construct a string literal encoding the version number components. */
+#ifdef SIMULATE_VERSION_MAJOR
+char const info_simulate_version[] = {
+  'I', 'N', 'F', 'O', ':',
+  's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[',
+  SIMULATE_VERSION_MAJOR,
+# ifdef SIMULATE_VERSION_MINOR
+  '.', SIMULATE_VERSION_MINOR,
+#  ifdef SIMULATE_VERSION_PATCH
+   '.', SIMULATE_VERSION_PATCH,
+#   ifdef SIMULATE_VERSION_TWEAK
+    '.', SIMULATE_VERSION_TWEAK,
+#   endif
+#  endif
+# endif
+  ']','\0'};
+#endif
+
+/* Construct the string literal in pieces to prevent the source from
+   getting matched.  Store it in a pointer rather than an array
+   because some compilers will just produce instructions to fill the
+   array rather than assigning a pointer to a static array.  */
+char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]";
+char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]";
+
+
+
+
+#if !defined(__STDC__)
+# if (defined(_MSC_VER) && !defined(__clang__)) \
+  || (defined(__ibmxl__) || defined(__IBMC__))
+#  define C_DIALECT "90"
+# else
+#  define C_DIALECT
+# endif
+#elif __STDC_VERSION__ >= 201000L
+# define C_DIALECT "11"
+#elif __STDC_VERSION__ >= 199901L
+# define C_DIALECT "99"
+#else
+# define C_DIALECT "90"
+#endif
+const char* info_language_dialect_default =
+  "INFO" ":" "dialect_default[" C_DIALECT "]";
+
+/*--------------------------------------------------------------------------*/
+
+#ifdef ID_VOID_MAIN
+void main() {}
+#else
+# if defined(__CLASSIC_C__)
+int main(argc, argv) int argc; char *argv[];
+# else
+int main(int argc, char* argv[])
+# endif
+{
+  int require = 0;
+  require += info_compiler[argc];
+  require += info_platform[argc];
+  require += info_arch[argc];
+#ifdef COMPILER_VERSION_MAJOR
+  require += info_version[argc];
+#endif
+#ifdef COMPILER_VERSION_INTERNAL
+  require += info_version_internal[argc];
+#endif
+#ifdef SIMULATE_ID
+  require += info_simulate[argc];
+#endif
+#ifdef SIMULATE_VERSION_MAJOR
+  require += info_simulate_version[argc];
+#endif
+#if defined(__CRAYXE) || defined(__CRAYXC)
+  require += info_cray[argc];
+#endif
+  require += info_language_dialect_default[argc];
+  (void)argv;
+  return require;
+}
+#endif

CMakeFiles/3.16.3/CompilerIdCXX/CMakeCXXCompilerId.cpp

@@ -0,0 +1,660 @@
+/* This source file must have a .cpp extension so that all C++ compilers
+   recognize the extension without flags.  Borland does not know .cxx for
+   example.  */
+#ifndef __cplusplus
+# error "A C compiler has been selected for C++."
+#endif
+
+
+/* Version number components: V=Version, R=Revision, P=Patch
+   Version date components:   YYYY=Year, MM=Month,   DD=Day  */
+
+#if defined(__COMO__)
+# define COMPILER_ID "Comeau"
+  /* __COMO_VERSION__ = VRR */
+# define COMPILER_VERSION_MAJOR DEC(__COMO_VERSION__ / 100)
+# define COMPILER_VERSION_MINOR DEC(__COMO_VERSION__ % 100)
+
+#elif defined(__INTEL_COMPILER) || defined(__ICC)
+# define COMPILER_ID "Intel"
+# if defined(_MSC_VER)
+#  define SIMULATE_ID "MSVC"
+# endif
+# if defined(__GNUC__)
+#  define SIMULATE_ID "GNU"
+# endif
+  /* __INTEL_COMPILER = VRP */
+# define COMPILER_VERSION_MAJOR DEC(__INTEL_COMPILER/100)
+# define COMPILER_VERSION_MINOR DEC(__INTEL_COMPILER/10 % 10)
+# if defined(__INTEL_COMPILER_UPDATE)
+#  define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER_UPDATE)
+# else
+#  define COMPILER_VERSION_PATCH DEC(__INTEL_COMPILER   % 10)
+# endif
+# if defined(__INTEL_COMPILER_BUILD_DATE)
+  /* __INTEL_COMPILER_BUILD_DATE = YYYYMMDD */
+#  define COMPILER_VERSION_TWEAK DEC(__INTEL_COMPILER_BUILD_DATE)
+# endif
+# if defined(_MSC_VER)
+   /* _MSC_VER = VVRR */
+#  define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100)
+#  define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100)
+# endif
+# if defined(__GNUC__)
+#  define SIMULATE_VERSION_MAJOR DEC(__GNUC__)
+# elif defined(__GNUG__)
+#  define SIMULATE_VERSION_MAJOR DEC(__GNUG__)
+# endif
+# if defined(__GNUC_MINOR__)
+#  define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__)
+# endif
+# if defined(__GNUC_PATCHLEVEL__)
+#  define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__)
+# endif
+
+#elif defined(__PATHCC__)
+# define COMPILER_ID "PathScale"
+# define COMPILER_VERSION_MAJOR DEC(__PATHCC__)
+# define COMPILER_VERSION_MINOR DEC(__PATHCC_MINOR__)
+# if defined(__PATHCC_PATCHLEVEL__)
+#  define COMPILER_VERSION_PATCH DEC(__PATHCC_PATCHLEVEL__)
+# endif
+
+#elif defined(__BORLANDC__) && defined(__CODEGEARC_VERSION__)
+# define COMPILER_ID "Embarcadero"
+# define COMPILER_VERSION_MAJOR HEX(__CODEGEARC_VERSION__>>24 & 0x00FF)
+# define COMPILER_VERSION_MINOR HEX(__CODEGEARC_VERSION__>>16 & 0x00FF)
+# define COMPILER_VERSION_PATCH DEC(__CODEGEARC_VERSION__     & 0xFFFF)
+
+#elif defined(__BORLANDC__)
+# define COMPILER_ID "Borland"
+  /* __BORLANDC__ = 0xVRR */
+# define COMPILER_VERSION_MAJOR HEX(__BORLANDC__>>8)
+# define COMPILER_VERSION_MINOR HEX(__BORLANDC__ & 0xFF)
+
+#elif defined(__WATCOMC__) && __WATCOMC__ < 1200
+# define COMPILER_ID "Watcom"
+   /* __WATCOMC__ = VVRR */
+# define COMPILER_VERSION_MAJOR DEC(__WATCOMC__ / 100)
+# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10)
+# if (__WATCOMC__ % 10) > 0
+#  define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10)
+# endif
+
+#elif defined(__WATCOMC__)
+# define COMPILER_ID "OpenWatcom"
+   /* __WATCOMC__ = VVRP + 1100 */
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+
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+.PHONY : yolov5_trt.cpp.s
+
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+	@echo "... all (the default if no target is provided)"
+	@echo "... clean"
+	@echo "... depend"
+	@echo "... rebuild_cache"
+	@echo "... edit_cache"
+	@echo "... yolo-crowd"
+	@echo "... yolov5_trt.o"
+	@echo "... yolov5_trt.i"
+	@echo "... yolov5_trt.s"
+.PHONY : help
+
+
+
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+

cmake_install.cmake

@@ -0,0 +1,49 @@
+# Install script for directory: /home/cl/onnx_run/yolo_crowd_run
+
+# Set the install prefix
+if(NOT DEFINED CMAKE_INSTALL_PREFIX)
+  set(CMAKE_INSTALL_PREFIX "/usr/local")
+endif()
+string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}")
+
+# Set the install configuration name.
+if(NOT DEFINED CMAKE_INSTALL_CONFIG_NAME)
+  if(BUILD_TYPE)
+    string(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
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+  else()
+    set(CMAKE_INSTALL_CONFIG_NAME "")
+  endif()
+  message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
+endif()
+
+# Set the component getting installed.
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+  if(COMPONENT)
+    message(STATUS "Install component: \"${COMPONENT}\"")
+    set(CMAKE_INSTALL_COMPONENT "${COMPONENT}")
+  else()
+    set(CMAKE_INSTALL_COMPONENT)
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+  set(CMAKE_CROSSCOMPILING "FALSE")
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+if(CMAKE_INSTALL_COMPONENT)
+  set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt")
+else()
+  set(CMAKE_INSTALL_MANIFEST "install_manifest.txt")
+endif()
+
+string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT
+       "${CMAKE_INSTALL_MANIFEST_FILES}")
+file(WRITE "/home/cl/onnx_run/yolo_crowd_run/${CMAKE_INSTALL_MANIFEST}"
+     "${CMAKE_INSTALL_MANIFEST_CONTENT}")

create_trt.cpp

@@ -0,0 +1,119 @@
+#include <fstream>
+
+#include <iostream>
+
+
+
+#include <NvInfer.h>
+
+#include </home/cl/package/TensorRT-8.6.1.6/samples/common/logger.h>
+
+
+
+using namespace nvinfer1;
+
+using namespace sample;
+
+
+
+const char* IN_NAME = "input";
+
+const char* OUT_NAME = "output";
+
+static const int IN_H = 224;
+
+static const int IN_W = 224;
+
+static const int BATCH_SIZE = 1;
+
+static const int EXPLICIT_BATCH = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+
+
+
+int main(int argc, char** argv)
+
+{
+
+        // Create builder
+
+        Logger m_logger;
+
+        IBuilder* builder = createInferBuilder(m_logger);
+
+        IBuilderConfig* config = builder->createBuilderConfig();
+
+
+
+        // Create model to populate the network
+
+        INetworkDefinition* network = builder->createNetworkV2(EXPLICIT_BATCH);
+
+        ITensor* input_tensor = network->addInput(IN_NAME, DataType::kFLOAT, Dims4{ BATCH_SIZE, 3, IN_H, IN_W });
+
+        IPoolingLayer* pool = network->addPoolingNd(*input_tensor, PoolingType::kMAX, DimsHW{ 2, 2 });
+
+        pool->setStrideNd(DimsHW{ 2, 2 });
+
+        pool->getOutput(0)->setName(OUT_NAME);
+
+        network->markOutput(*pool->getOutput(0));
+
+
+
+        // Build engine
+
+        IOptimizationProfile* profile = builder->createOptimizationProfile();
+
+        profile->setDimensions(IN_NAME, OptProfileSelector::kMIN, Dims4(BATCH_SIZE, 3, IN_H, IN_W));
+
+        profile->setDimensions(IN_NAME, OptProfileSelector::kOPT, Dims4(BATCH_SIZE, 3, IN_H, IN_W));
+
+        profile->setDimensions(IN_NAME, OptProfileSelector::kMAX, Dims4(BATCH_SIZE, 3, IN_H, IN_W));
+
+        config->setMaxWorkspaceSize(1 << 20);
+
+        ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
+
+
+
+        // Serialize the model to engine file
+
+        IHostMemory* modelStream{ nullptr };
+
+        assert(engine != nullptr);
+
+        modelStream = engine->serialize();
+
+
+
+        std::ofstream p("model.engine", std::ios::binary);
+
+        if (!p) {
+
+                std::cerr << "could not open output file to save model" << std::endl;
+
+                return -1;
+
+        }
+
+        p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
+
+        std::cout << "generating file done!" << std::endl;
+
+
+
+        // Release resources
+
+        modelStream->destroy();
+
+        network->destroy();
+
+        engine->destroy();
+
+        builder->destroy();
+
+        config->destroy();
+
+        return 0;
+
+}

cy.log

@@ -0,0 +1,131 @@
+hello world
+net loaded
+Image size: 564 x 959, Channels: 3
+Image depth: 0, Type: 16
+netouput_size:3 
+postprocess done
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onnx2trt.py

@@ -0,0 +1,39 @@
+import tensorrt as trt
+import onnx
+import torch
+
+device = torch.device('cuda:0')
+
+onnx = "yolo-crowd-n.onnx"
+
+print(f"starting export of onnx to tensorrt engine... with TensorRT version: {trt.__version__}\n")
+f = "yolov5-crowd-n.engine"
+logger = trt.Logger(trt.Logger.INFO)
+
+builder = trt.Builder(logger)
+config = builder.create_builder_config()
+config.max_workspace_size = 1 << 30
+
+flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+network = builder.create_network(flag)
+parser = trt.OnnxParser(network, logger)
+if not parser.parse_from_file(onnx):
+    print('ERROR: Failed to parse the ONNX file.')
+    for error in range(parser.num_errors):
+        print(parser.get_error(error))
+    exit(1)
+
+inputs = [network.get_input(i) for i in range(network.num_inputs)]
+outputs = [network.get_output(i) for i in range(network.num_outputs)]
+for inp in inputs:
+    print(f'input: {inp.name}, shape: {inp.shape} {inp.dtype}')
+for out in outputs:
+    print(f'output: {out.name}, shape: {out.shape} {out.dtype}')
+
+if builder.platform_has_fast_fp16:
+    config.set_flag(trt.BuilderFlag.FP16)
+
+build = builder.build_serialized_network
+
+with build(network, config) as engine, open(f, "wb") as t:
+    t.write(engine)

test_trt.cpp

@@ -0,0 +1,222 @@
+#include <iostream>
+#include <fstream>
+
+#include <NvInfer.h>
+#include </home/cl/package/TensorRT-8.6.1.6/samples/common/logger.h>
+
+
+void CHECK(int status) {
+    if (status != 0) {
+        std::cerr << "Cuda failure: " << status << std::endl;
+        std::abort();
+    }
+}
+
+
+
+using namespace nvinfer1;
+
+using namespace sample;
+
+
+
+const char* IN_NAME = "images";
+
+const char* OUT_NAME1 = "output";
+const char* OUT_NAME2 = "947";
+const char* OUT_NAME3 = "961";
+
+static const int IN_H = 640;
+
+static const int IN_W = 640;
+
+static const int BATCH_SIZE = 1;
+
+static const int EXPLICIT_BATCH = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+
+
+
+
+
+void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
+
+{
+
+        const ICudaEngine& engine = context.getEngine();
+
+
+
+        // Pointers to input and output device buffers to pass to engine.
+
+        // Engine requires exactly IEngine::getNbBindings() number of buffers.
+
+        assert(engine.getNbBindings() == 4);
+
+        void* buffers[4];
+
+
+
+        // In order to bind the buffers, we need to know the names of the input and output tensors.
+
+        // Note that indices are guaranteed to be less than IEngine::getNbBindings()
+
+        const int inputIndex = engine.getBindingIndex(IN_NAME);
+
+        const int outputIndex1 = engine.getBindingIndex(OUT_NAME1);
+        const int outputIndex2 = engine.getBindingIndex(OUT_NAME2);
+        const int outputIndex3 = engine.getBindingIndex(OUT_NAME3);
+        printf("outputIndex1: %d, outputIndex2: %d, outputIndex3: %d\n", outputIndex1, outputIndex2, outputIndex3);
+
+
+
+        // Create GPU buffers on device
+
+        CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * IN_H * IN_W * sizeof(float)));
+
+        CHECK(cudaMalloc(&buffers[outputIndex1], batchSize * 3 * IN_H/4 * IN_W /4 * 6 * sizeof(float)));
+        CHECK(cudaMalloc(&buffers[outputIndex2], batchSize * 3 * IN_H/8 * IN_W /8 * 6 * sizeof(float)));
+        CHECK(cudaMalloc(&buffers[outputIndex3], batchSize * 3 * IN_H/16 * IN_W /16 * 6 * sizeof(float)));
+
+
+
+        // Create stream
+
+        cudaStream_t stream;
+
+        CHECK(cudaStreamCreate(&stream));
+
+
+
+        // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
+
+        CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * IN_H * IN_W * sizeof(float), cudaMemcpyHostToDevice, stream));
+
+        context.enqueue(batchSize, buffers, stream, nullptr);
+
+        CHECK(cudaMemcpyAsync(output, buffers[outputIndex1], batchSize * 3 * IN_H/4 * IN_W /4 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+        CHECK(cudaMemcpyAsync(output + 3 * (IN_H/4 * IN_W/4) * 6, buffers[outputIndex2], batchSize * 3 * IN_H/8 * IN_W /8 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+        CHECK(cudaMemcpyAsync(output + 3 * (IN_H/4 * IN_W/4 + IN_H/8 * IN_W/8) * 6, buffers[outputIndex3], batchSize * 3 * IN_H/16 * IN_W /16 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+
+        cudaStreamSynchronize(stream);
+
+
+
+        // Release stream and buffers
+
+        cudaStreamDestroy(stream);
+
+        CHECK(cudaFree(buffers[inputIndex]));
+
+        CHECK(cudaFree(buffers[outputIndex1]));
+        CHECK(cudaFree(buffers[outputIndex2]));
+        CHECK(cudaFree(buffers[outputIndex3]));
+
+}
+
+
+
+int main(int argc, char** argv)
+
+{
+
+        // create a model using the API directly and serialize it to a stream
+
+        char *trtModelStream{ nullptr };
+
+        size_t size{ 0 };
+
+
+
+        std::ifstream file("yolov5-crowd-n.engine", std::ios::binary);
+
+        if (file.good()) {
+
+                file.seekg(0, file.end);
+
+                size = file.tellg();
+
+                file.seekg(0, file.beg);
+
+                trtModelStream = new char[size];
+
+                assert(trtModelStream);
+
+                file.read(trtModelStream, size);
+
+                file.close();
+
+        }
+
+
+
+        Logger m_logger;
+
+        IRuntime* runtime = createInferRuntime(m_logger);
+
+        assert(runtime != nullptr);
+
+        ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
+
+        assert(engine != nullptr);
+
+        IExecutionContext* context = engine->createExecutionContext();
+
+        assert(context != nullptr);
+
+
+
+        // generate input data
+
+        float data[BATCH_SIZE * 3 * IN_H * IN_W];
+
+        for (int i = 0; i < BATCH_SIZE * 3 * IN_H * IN_W; i++)
+
+                data[i] = 1;
+
+
+
+        // Run inference
+        int num_total = 3 * (IN_H/4 * IN_W/4 + IN_H/8 * IN_W/8 + IN_H/16 * IN_W/16) * 6;
+        float prob[num_total];
+        printf("num_total: %d\n", num_total);
+
+        doInference(*context, data, prob, BATCH_SIZE);
+        float *prob_ptr = prob;
+        int count = 0;
+        int invalid_count = 0;
+        for (int i = 0; i < num_total / 6; i++)
+        {
+            {
+                for (int j = 0; j < 6; j++)
+                {
+                    //printf("%f ", prob_ptr[j]);
+                    //count++;
+                }
+                if( prob_ptr[4] > 1){
+                    invalid_count++;
+                }else{
+                    count++;
+                }
+                //printf("\n");
+                
+            }
+            prob_ptr += 6;
+
+        }
+        printf("invalid_count: %d\n", invalid_count);
+        printf("count: %d\n", count);
+        printf("inference done");
+
+
+
+        // Destroy the engine
+
+        context->destroy();
+
+        engine->destroy();
+
+        runtime->destroy();
+
+        return 0;
+
+}

yolov5.cpp

@@ -0,0 +1,319 @@
+#include <iostream>
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn/dnn.hpp>
+#include <string>
+#include <vector>
+
+using cv::Mat;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
+
+static const vector<string> class_name = {"person"};
+
+
+// const vector<vector<vector<float>>> anchors = 
+//     {{{3.90, 5.848}, {6.684, 6.81}, {30.48, 25.40}}, 
+//     {{38.40, 32}, {48.38, 40.32}, {60.96, 50.80}}, 
+//     {{76.8, 64}, {96.76, 80.63}, {121.91, 101.59}}};
+const vector<vector<vector<float>>> anchors = 
+    {{{3.90, 5.848}, {6.684, 6.81}, {5.79, 9.88}}, 
+    {{7.754, 11.71}, {10.25, 16.5}, {13.66, 12.74}}, 
+    {{14.73, 22.51}, {19.187, 33.625}, {33.906, 54.719}}};
+
+const vector<int> strides = {8, 16, 32};
+const vector<int> grid_sizes = {160, 80, 40};
+const vector<float> stride_list = {4, 8, 16};
+
+float sigmoid(float x) {
+    return 1.0 / (1.0 + std::exp(-x));
+};
+
+std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> make_grid(int nx = 20, int ny = 20) {
+    std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> grid(1, 
+        std::vector<std::vector<std::vector<std::vector<float>>>>(1, 
+            std::vector<std::vector<std::vector<float>>>(ny, 
+                std::vector<std::vector<float>>(nx, 
+                    std::vector<float>(2)))));
+
+    for (int y = 0; y < ny; ++y) {
+        for (int x = 0; x < nx; ++x) {
+            grid[0][0][y][x][0] = x; // xv
+            grid[0][0][y][x][1] = y; // yv
+        }
+    }
+
+    return grid;
+};
+vector<Mat> postprocess(const vector<Mat> &out, float conf = 0.7, int len_data = 6){
+    int nc = 1;
+    int no = nc + 5;
+    int nl = anchors.size();
+    //int nl = 1;
+    int na = anchors[0].size() / 2;
+    vector<Mat> result;
+
+    for(int i = 0; i < nl; i++){
+        std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> grid = make_grid(grid_sizes[i], grid_sizes[i]);
+        cv::Mat x = out[i];
+        cv::Mat x_sigmoid = x.clone();
+        for (int n = 0; n < x.size[0]; n++) {
+            for (int j = 0; j < x_sigmoid.size[1]; j++) {
+                for (int k = 0; k < x_sigmoid.size[2]; k++) {
+                    for (int l = 0; l < x_sigmoid.size[3]; l++) {
+                        for (int m = 0; m < x_sigmoid.size[4]; m++) {
+                            // 假设数据是多通道的二维数据
+                            // int channel = l * x_sigmoid.size[3] + m;
+                            int offset = j * x_sigmoid.size[2]*x_sigmoid.size[3]*x_sigmoid.size[4] + k * x_sigmoid.size[3] * x_sigmoid.size[4] + l *x_sigmoid.size[4] + m;
+                            x_sigmoid.at<float>(n, offset) = sigmoid(x.at<float>(n, offset));
+                            float x_sigmoid_before = x_sigmoid.at<float>(n, offset);
+                            if(m < 2){ 
+                                x_sigmoid.at<float>(n, offset) = ((x_sigmoid.at<float>(n, offset) * 2.0) - 0.5 + grid[0][0][k][l][m]) * stride_list[i];
+                            }else if(m < 4){
+                                x_sigmoid.at<float>(n, offset) = (x_sigmoid.at<float>(n, offset) * 2.0)*(x_sigmoid.at<float>(n, offset) * 2) * anchors[i][j][m-2];
+                            }
+                            //printf("j: %d, k: %d, l: %d, m: %d, before: %f, data: %f \n", j, k, l, m, x_sigmoid_before, x_sigmoid.at<float>(j, offset));
+                        }
+                    }
+                }
+            }
+        }
+        
+        //printf("x_sigmoid.size: %d %d %d %d\n", x_sigmoid.size[1], x_sigmoid.size[2], x_sigmoid.size[3], x_sigmoid.size[4]);
+        //printf("x_sigmoid value: %f\n", x_sigmoid.at<cv::Vec<float, 1>>(0, 0)[0]);
+        x_sigmoid = x_sigmoid.reshape(1, {x_sigmoid.size[1]*x_sigmoid.size[2]*x_sigmoid.size[3], 6});
+        result.push_back(x_sigmoid);
+    }
+
+    
+    return result;
+
+};
+
+
+
+
+
+void print_result(const Mat &result, float conf = 0.7, int len_data = 6)
+{
+    float *pdata = (float *)result.data;
+    for (int i = 0; i < result.total() / len_data; i++)
+    {
+        if (pdata[4] > conf)
+        {
+            for (int j = 0; j < len_data; j++)
+            {
+                cout << pdata[j] << " ";
+            }
+            cout << endl;
+        }
+        pdata += len_data;
+    }
+    return;
+}
+
+vector<vector<float>> get_info(const Mat &result, float conf = 0.7, int len_data = 6)
+{
+    float *pdata = (float *)result.data;
+    vector<vector<float>> info;
+    for (int i = 0; i < result.total() / len_data; i++)
+    {
+        if (pdata[4] > conf)
+        {
+            vector<float> info_line;
+            for (int j = 0; j < len_data; j++)
+            {
+                // cout << pdata[j] << " ";
+                info_line.push_back(pdata[j]);
+            }
+            // cout << endl;
+            info.push_back(info_line);
+        }
+        pdata += len_data;
+    }
+    return info;
+}
+
+void info_simplify(vector<vector<float>> &info, float witdh_scale = 1.0, float height_scale = 1.0)
+{
+    for (auto i = 0; i < info.size(); i++)
+    {
+        info[i][5] = std::max_element(info[i].cbegin() + 5, info[i].cend()) - (info[i].cbegin() + 5);
+        info[i].resize(6);
+        float x = info[i][0];
+        float y = info[i][1];
+        float w = info[i][2];
+        float h = info[i][3];
+        info[i][0] = (x - w / 2.0) * witdh_scale;
+        info[i][1] = (y - h / 2.0) * height_scale;
+        info[i][2] = (x + w / 2.0) * witdh_scale;
+        info[i][3] = (y + h / 2.0) * height_scale;
+    }
+}
+
+vector<vector<vector<float>>> split_info(vector<vector<float>> &info)
+{
+    vector<vector<vector<float>>> info_split;
+    vector<int> class_id;
+    for (auto i = 0; i < info.size(); i++)
+    {
+        if (std::find(class_id.begin(), class_id.end(), (int)info[i][5]) == class_id.end())
+        {
+            class_id.push_back((int)info[i][5]);
+            vector<vector<float>> info_;
+            info_split.push_back(info_);
+        }
+        info_split[std::find(class_id.begin(), class_id.end(), (int)info[i][5]) - class_id.begin()].push_back(info[i]);
+    }
+    return info_split;
+}
+
+void nms(vector<vector<float>> &info, float iou = 0.45)
+{
+    int counter = 0;
+    vector<vector<float>> return_info;
+    while (counter < info.size())
+    {
+        return_info.clear();
+        float x1 = 0;
+        float x2 = 0;
+        float y1 = 0;
+        float y2 = 0;
+        // 按置信度降序排列
+        std::sort(info.begin(), info.end(), [](vector<float> p1, vector<float> p2)
+                  { return p1[4] > p2[4]; });
+        for (auto i = 0; i < info.size(); i++)
+        {
+            if (i < counter)
+            {
+                return_info.push_back(info[i]);
+                continue;
+            }
+            if (i == counter)
+            {
+                x1 = info[i][0];
+                y1 = info[i][1];
+                x2 = info[i][2];
+                y2 = info[i][3];
+                return_info.push_back(info[i]);
+                continue;
+            }
+            if (info[i][0] > x2 or info[i][2] < x1 or info[i][1] > y2 or info[i][3] < y1)
+            {
+                return_info.push_back(info[i]);
+            }
+            else
+            {
+                float over_x1 = std::max(x1, info[i][0]);
+                float over_y1 = std::max(y1, info[i][1]);
+                float over_x2 = std::min(x2, info[i][2]);
+                float over_y2 = std::min(y2, info[i][3]);
+                float s_over = (over_x2 - over_x1) * (over_y2 - over_y1);
+                float s_total = (x2 - x1) * (y2 - y1) + (info[i][0] - info[i][2]) * (info[i][1] - info[i][3]) - s_over;
+                if (s_over / s_total < iou)
+                {
+                    return_info.push_back(info[i]);
+                }
+            }
+        }
+        info = return_info;
+        counter += 1;
+    }
+}
+
+void print_info(const vector<vector<float>> &info)
+{
+    for (auto i = 0; i < info.size(); i++)
+    {
+        for (auto j = 0; j < info[i].size(); j++)
+        {
+            cout << info[i][j] << " ";
+        }
+        cout << endl;
+    }
+}
+
+void draw_box(Mat &img, const vector<vector<float>> &info)
+{
+    for (int i = 0; i < info.size(); i++)
+    {
+        cv::rectangle(img, cv::Point(info[i][0], info[i][1]), cv::Point(info[i][2], info[i][3]), cv::Scalar(0, 255, 0));
+        // string label;
+        // label += class_name[info[i][5]];
+        // label += "  ";
+        // std::stringstream oss;
+        // oss << info[i][4];
+        // label += oss.str();
+        // cv::putText(img, label, cv::Point(info[i][0], info[i][1]), 1, 2, cv::Scalar(0, 255, 0), 2);
+
+    }
+    // 在左上角显示总数
+    string head_info = "Total: " + std::to_string(info.size());
+    cv::putText(img, head_info, cv::Point(10, 20), 1, 2, cv::Scalar(0, 255, 0), 2);
+}
+
+int main()
+{
+    cv::dnn::Net net = cv::dnn::readNet("yolo-crowd-n.onnx");
+    printf("net loaded\n");
+    Mat img = cv::imread("image.jpg", -1);
+
+    if (img.empty()) {
+        std::cout << "Failed to load image" << std::endl;
+        return -1;
+    }
+
+    // 获取图像的高度、宽度和通道数
+    int height = img.rows;   // 图像高度
+    int width = img.cols;    // 图像宽度
+    int channels = img.channels(); // 图像通道数
+
+    // 输出图像信息
+    std::cout << "Image size: " << height << " x " << width << ", Channels: " << channels << std::endl;
+
+    cv::resize(img, img, cv::Size(640, 640));
+    
+    Mat blob = cv::dnn::blobFromImage(img, 1.0 / 255.0, cv::Size(640, 640), cv::Scalar(), true); // bgr rgb
+
+    float height_scale = height / 640.0;
+    float width_scale = width / 640.0;
+
+    net.setInput(blob);
+    vector<Mat> netoutput;
+    vector<string> out_name = {"output", "947", "961"};
+    net.forward(netoutput, out_name);
+    printf("netouput_size:%ld \n", netoutput.size());
+
+    // 将输出根据grid和anchor进行计算，得到预测框的坐标和置信度
+    vector<Mat> result_list = postprocess(netoutput);
+
+    // 拼接
+    Mat concatMAT;
+    vconcat(result_list[0], result_list[1], concatMAT);
+    vconcat(concatMAT, result_list[2], concatMAT);
+    Mat result = concatMAT;
+
+    // 根据置信度 获得有效框
+    vector<vector<float>> info = get_info(result, 0.25, 6);
+    info_simplify(info, width_scale, height_scale);
+    vector<vector<vector<float>>> info_split = split_info(info);
+
+    printf("info size: %ld\n", info_split.size());
+    cv::resize(img, img, cv::Size(width, height));
+    for(auto i=0; i < info_split.size(); i++)
+    {
+        printf("class %d size: %ld\n", i, info_split[i].size());
+
+        // 根据阈值进行nms
+        nms(info_split[i], 0.45);
+
+        draw_box(img, info_split[i]);
+
+        printf("class %d size: %ld\n", i, info_split[i].size());
+
+    }
+
+    cv::imwrite("result.jpg", img);
+    return 0;
+}

yolov5_trt.cpp

@@ -0,0 +1,533 @@
+#include <iostream>
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn/dnn.hpp>
+#include <string>
+#include <vector>
+
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <vector>
+
+#include <NvInfer.h>
+#include </home/cl/package/TensorRT-8.6.1.6/samples/common/logger.h>
+
+
+using cv::Mat;
+using std::cout;
+using std::endl;
+using std::string;
+using std::vector;
+
+using namespace nvinfer1;
+
+
+
+
+
+static const vector<string> class_name = {"person"};
+
+
+// const vector<vector<vector<float>>> anchors = 
+//     {{{3.90, 5.848}, {6.684, 6.81}, {30.48, 25.40}}, 
+//     {{38.40, 32}, {48.38, 40.32}, {60.96, 50.80}}, 
+//     {{76.8, 64}, {96.76, 80.63}, {121.91, 101.59}}};
+const vector<vector<vector<float>>> anchors = 
+    {{{3.90, 5.848}, {6.684, 6.81}, {5.79, 9.88}}, 
+    {{7.754, 11.71}, {10.25, 16.5}, {13.66, 12.74}}, 
+    {{14.73, 22.51}, {19.187, 33.625}, {33.906, 54.719}}};
+
+const vector<int> strides = {8, 16, 32};
+const vector<int> grid_sizes = {160, 80, 40};
+const vector<float> stride_list = {4, 8, 16};
+
+float sigmoid(float x) {
+    return 1.0 / (1.0 + std::exp(-x));
+};
+
+std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> make_grid(int nx = 20, int ny = 20) {
+    std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> grid(1, 
+        std::vector<std::vector<std::vector<std::vector<float>>>>(1, 
+            std::vector<std::vector<std::vector<float>>>(ny, 
+                std::vector<std::vector<float>>(nx, 
+                    std::vector<float>(2)))));
+
+    for (int y = 0; y < ny; ++y) {
+        for (int x = 0; x < nx; ++x) {
+            grid[0][0][y][x][0] = x; // xv
+            grid[0][0][y][x][1] = y; // yv
+        }
+    }
+
+    return grid;
+};
+float* postprocess(float *results, float conf = 0.7, int len_data = 6){
+    int nc = 1;
+    int no = nc + 5;
+    int nl = anchors.size();
+    //int nl = 1;
+    int na = anchors[0].size() / 2;
+
+    int count = 0;
+    for(int i = 0; i < nl; i++){
+        std::vector<std::vector<std::vector<std::vector<std::vector<float>>>>> grid = make_grid(grid_sizes[i], grid_sizes[i]);
+        for (int n = 0; n < 1; n++) {
+            for (int j = 0; j < 3; j++) {
+                for (int k = 0; k < grid_sizes[i]; k++) {
+                    for (int l = 0; l < grid_sizes[i]; l++) {
+                        for (int m = 0; m < 6; m++) {
+                            // 假设数据是多通道的二维数据
+                            // int channel = l * x_sigmoid.size[3] + m;
+                            int offset;
+                            if(i == 0){
+                                offset = j * grid_sizes[i] * grid_sizes[i] * 6 + k * grid_sizes[i] * 6 + l * 6 + m;
+                            }else if(i == 1){
+                                int zero_offset = 3 * 160 * 160 * 6;
+                                offset = zero_offset + j * grid_sizes[i] * grid_sizes[i] * 6 + k * grid_sizes[i] * 6 + l * 6 + m;
+                            }else if(i == 2){
+                                int zero_offset = 3 * 160 * 160 * 6 + 3 * 80 * 80 * 6;
+                                offset = zero_offset + j * grid_sizes[i] * grid_sizes[i] * 6 + k * grid_sizes[i] * 6 + l * 6 + m;
+                            }
+                            float x = results[offset];
+                            x = sigmoid(x);
+                            float x_sigmoid_before = results[offset];
+                            if(m < 2){ 
+                                x = ((x * 2.0) - 0.5 + grid[0][0][k][l][m]) * stride_list[i];
+                            }else if(m < 4){
+                                x = (x * 2.0)*(x * 2.0) * anchors[i][j][m-2];
+                            }
+                            results[offset] = x;
+                            count++;
+                            //printf("j: %d, k: %d, l: %d, m: %d, before: %f, data: %f \n", j, k, l, m, x_sigmoid_before, results[offset]);
+                            //printf("j: %d, k: %d, l: %d, m: %d, before: %f, data: %f \n", j, k, l, m, x_sigmoid_before, results[offset]);
+                        }
+                    }
+                }
+            }
+        }
+
+    }
+
+    printf("postprocess count: %d\n", count);
+    return results;
+
+};
+
+
+
+
+
+void print_result(const Mat &result, float conf = 0.7, int len_data = 6)
+{
+    float *pdata = (float *)result.data;
+    for (int i = 0; i < result.total() / len_data; i++)
+    {
+        if (pdata[4] > conf)
+        {
+            for (int j = 0; j < len_data; j++)
+            {
+                cout << pdata[j] << " ";
+            }
+            cout << endl;
+        }
+        pdata += len_data;
+    }
+    return;
+}
+
+vector<vector<float>> get_info(float *result, float conf = 0.7, int len_data = 6)
+{
+    float *pdata = result;
+    vector<vector<float>> info;
+    for (int i = 0; i < 604800 / len_data; i++)
+    {
+        if (pdata[4] > conf)
+        {
+            vector<float> info_line;
+            for (int j = 0; j < len_data; j++)
+            {
+                // cout << pdata[j] << " ";
+                info_line.push_back(pdata[j]);
+            }
+            // cout << endl;
+            info.push_back(info_line);
+        }
+        pdata += len_data;
+    }
+    return info;
+}
+
+void info_simplify(vector<vector<float>> &info, float witdh_scale = 1.0, float height_scale = 1.0)
+{
+    for (auto i = 0; i < info.size(); i++)
+    {
+        info[i][5] = std::max_element(info[i].cbegin() + 5, info[i].cend()) - (info[i].cbegin() + 5);
+        info[i].resize(6);
+        float x = info[i][0];
+        float y = info[i][1];
+        float w = info[i][2];
+        float h = info[i][3];
+        info[i][0] = (x - w / 2.0) * witdh_scale;
+        info[i][1] = (y - h / 2.0) * height_scale;
+        info[i][2] = (x + w / 2.0) * witdh_scale;
+        info[i][3] = (y + h / 2.0) * height_scale;
+    }
+}
+
+vector<vector<vector<float>>> split_info(vector<vector<float>> &info)
+{
+    vector<vector<vector<float>>> info_split;
+    vector<int> class_id;
+    for (auto i = 0; i < info.size(); i++)
+    {
+        if (std::find(class_id.begin(), class_id.end(), (int)info[i][5]) == class_id.end())
+        {
+            class_id.push_back((int)info[i][5]);
+            vector<vector<float>> info_;
+            info_split.push_back(info_);
+        }
+        info_split[std::find(class_id.begin(), class_id.end(), (int)info[i][5]) - class_id.begin()].push_back(info[i]);
+    }
+    return info_split;
+}
+
+void nms(vector<vector<float>> &info, float iou = 0.45)
+{
+    int counter = 0;
+    vector<vector<float>> return_info;
+    while (counter < info.size())
+    {
+        return_info.clear();
+        float x1 = 0;
+        float x2 = 0;
+        float y1 = 0;
+        float y2 = 0;
+        // 按置信度降序排列
+        std::sort(info.begin(), info.end(), [](vector<float> p1, vector<float> p2)
+                  { return p1[4] > p2[4]; });
+        for (auto i = 0; i < info.size(); i++)
+        {
+            if (i < counter)
+            {
+                return_info.push_back(info[i]);
+                continue;
+            }
+            if (i == counter)
+            {
+                x1 = info[i][0];
+                y1 = info[i][1];
+                x2 = info[i][2];
+                y2 = info[i][3];
+                return_info.push_back(info[i]);
+                continue;
+            }
+            if (info[i][0] > x2 or info[i][2] < x1 or info[i][1] > y2 or info[i][3] < y1)
+            {
+                return_info.push_back(info[i]);
+            }
+            else
+            {
+                float over_x1 = std::max(x1, info[i][0]);
+                float over_y1 = std::max(y1, info[i][1]);
+                float over_x2 = std::min(x2, info[i][2]);
+                float over_y2 = std::min(y2, info[i][3]);
+                float s_over = (over_x2 - over_x1) * (over_y2 - over_y1);
+                float s_total = (x2 - x1) * (y2 - y1) + (info[i][0] - info[i][2]) * (info[i][1] - info[i][3]) - s_over;
+                if (s_over / s_total < iou)
+                {
+                    return_info.push_back(info[i]);
+                }
+            }
+        }
+        info = return_info;
+        counter += 1;
+    }
+}
+
+void print_info(const vector<vector<float>> &info)
+{
+    for (auto i = 0; i < info.size(); i++)
+    {
+        for (auto j = 0; j < info[i].size(); j++)
+        {
+            cout << info[i][j] << " ";
+        }
+        cout << endl;
+    }
+}
+
+void draw_box(Mat &img, const vector<vector<float>> &info)
+{
+    for (int i = 0; i < info.size(); i++)
+    {
+        cv::rectangle(img, cv::Point(info[i][0], info[i][1]), cv::Point(info[i][2], info[i][3]), cv::Scalar(0, 255, 0));
+        // string label;
+        // label += class_name[info[i][5]];
+        // label += "  ";
+        // std::stringstream oss;
+        // oss << info[i][4];
+        // label += oss.str();
+        // cv::putText(img, label, cv::Point(info[i][0], info[i][1]), 1, 2, cv::Scalar(0, 255, 0), 2);
+
+    }
+    // 在左上角显示总数
+    string head_info = "Total: " + std::to_string(info.size());
+    cv::putText(img, head_info, cv::Point(10, 20), 1, 2, cv::Scalar(0, 255, 0), 2);
+}
+
+
+void CHECK(int status) {
+    if (status != 0) {
+        std::cerr << "Cuda failure: " << status << std::endl;
+        std::abort();
+    }
+}
+
+
+
+using namespace nvinfer1;
+
+using namespace sample;
+
+
+
+const char* IN_NAME = "images";
+
+const char* OUT_NAME1 = "output";
+const char* OUT_NAME2 = "947";
+const char* OUT_NAME3 = "961";
+
+static const int IN_H = 640;
+
+static const int IN_W = 640;
+
+static const int BATCH_SIZE = 1;
+
+static const int EXPLICIT_BATCH = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+
+
+
+
+
+void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
+
+{
+
+        const ICudaEngine& engine = context.getEngine();
+
+
+
+        // Pointers to input and output device buffers to pass to engine.
+
+        // Engine requires exactly IEngine::getNbBindings() number of buffers.
+
+        assert(engine.getNbBindings() == 4);
+
+        void* buffers[4];
+
+
+
+        // In order to bind the buffers, we need to know the names of the input and output tensors.
+
+        // Note that indices are guaranteed to be less than IEngine::getNbBindings()
+
+        const int inputIndex = engine.getBindingIndex(IN_NAME);
+
+        const int outputIndex1 = engine.getBindingIndex(OUT_NAME1);
+        const int outputIndex2 = engine.getBindingIndex(OUT_NAME2);
+        const int outputIndex3 = engine.getBindingIndex(OUT_NAME3);
+        printf("outputIndex1: %d, outputIndex2: %d, outputIndex3: %d\n", outputIndex1, outputIndex2, outputIndex3);
+
+
+
+        // Create GPU buffers on device
+
+        CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * IN_H * IN_W * sizeof(float)));
+
+        CHECK(cudaMalloc(&buffers[outputIndex1], batchSize * 3 * IN_H/4 * IN_W /4 * 6 * sizeof(float)));
+        CHECK(cudaMalloc(&buffers[outputIndex2], batchSize * 3 * IN_H/8 * IN_W /8 * 6 * sizeof(float)));
+        CHECK(cudaMalloc(&buffers[outputIndex3], batchSize * 3 * IN_H/16 * IN_W /16 * 6 * sizeof(float)));
+
+
+
+        // Create stream
+
+        cudaStream_t stream;
+
+        CHECK(cudaStreamCreate(&stream));
+
+
+
+        // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
+
+        CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * IN_H * IN_W * sizeof(float), cudaMemcpyHostToDevice, stream));
+
+        context.enqueue(batchSize, buffers, stream, nullptr);
+
+        CHECK(cudaMemcpyAsync(output, buffers[outputIndex1], batchSize * 3 * IN_H/4 * IN_W /4 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+        CHECK(cudaMemcpyAsync(output + 3 * (IN_H/4 * IN_W/4) * 6, buffers[outputIndex2], batchSize * 3 * IN_H/8 * IN_W /8 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+        CHECK(cudaMemcpyAsync(output + 3 * (IN_H/4 * IN_W/4 + IN_H/8 * IN_W/8) * 6, buffers[outputIndex3], batchSize * 3 * IN_H/16 * IN_W /16 * 6 * sizeof(float), cudaMemcpyDeviceToHost, stream));
+
+        cudaStreamSynchronize(stream);
+
+
+
+        // Release stream and buffers
+
+        cudaStreamDestroy(stream);
+
+        CHECK(cudaFree(buffers[inputIndex]));
+
+        CHECK(cudaFree(buffers[outputIndex1]));
+        CHECK(cudaFree(buffers[outputIndex2]));
+        CHECK(cudaFree(buffers[outputIndex3]));
+
+}
+
+int main()
+{
+    // create a model using the API directly and serialize it to a stream
+
+        char *trtModelStream{ nullptr };
+
+        size_t size{ 0 };
+
+        // 读取engine文件
+        std::ifstream file("yolov5-crowd-n.engine", std::ios::binary);
+
+        if (file.good()) {
+
+                file.seekg(0, file.end);
+
+                size = file.tellg();
+
+                file.seekg(0, file.beg);
+
+                trtModelStream = new char[size];
+
+                assert(trtModelStream);
+
+                file.read(trtModelStream, size);
+
+                file.close();
+
+        }
+
+
+
+        Logger m_logger;
+
+        IRuntime* runtime = createInferRuntime(m_logger);
+
+        assert(runtime != nullptr);
+
+        ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
+
+        assert(engine != nullptr);
+
+        IExecutionContext* context = engine->createExecutionContext();
+
+        assert(context != nullptr);
+
+
+
+        // generate input data
+        float data[BATCH_SIZE * 3 * IN_H * IN_W];
+
+        // Run inference
+        int num_total = 3 * (IN_H/4 * IN_W/4 + IN_H/8 * IN_W/8 + IN_H/16 * IN_W/16) * 6;
+        // 存储推理结果
+        float prob[num_total];
+        printf("num_total: %d\n", num_total);
+
+            Mat img = cv::imread("image.jpg", -1);
+
+    if (img.empty()) {
+        std::cout << "Failed to load image" << std::endl;
+        return -1;
+    }
+
+    // 获取图像的高度、宽度和通道数
+    int height = img.rows;   // 图像高度
+    int width = img.cols;    // 图像宽度
+    int channels = img.channels(); // 图像通道数
+
+    // 输出图像信息
+    std::cout << "Image size: " << height << " x " << width << ", Channels: " << channels << std::endl;
+
+    cv::resize(img, img, cv::Size(640, 640));
+    
+    Mat blob = cv::dnn::blobFromImage(img, 1.0 / 255.0, cv::Size(640, 640), cv::Scalar(), true); // bgr rgb
+
+    std::memcpy(data, blob.data, 3 * 640 * 640 * sizeof(float));
+
+    float height_scale = height / 640.0;
+    float width_scale = width / 640.0;
+
+    doInference(*context, data, prob, BATCH_SIZE);
+
+    printf("inference done");
+
+
+
+    // Destroy the engine
+
+    context->destroy();
+
+    engine->destroy();
+
+    runtime->destroy();
+
+    // 将输出根据grid和anchor进行计算，得到预测框的坐标和置信度
+    float* result = postprocess(prob);  // prob -> 604800
+    float* prob_ptr = result;
+    int count = 0;
+    int invalid_count = 0;
+    for (int i = 0; i < num_total / 6; i++)
+        {
+            {
+                for (int j = 0; j < 6; j++)
+                {
+                    //printf("%f ", prob_ptr[j]);
+                    //count++;
+                }
+                if( prob_ptr[4] < 0){
+                    invalid_count++;
+                }else if(prob_ptr[4] > 0.25){
+                    count++;
+                }
+                //printf("\n");
+                
+            }
+            prob_ptr += 6;
+
+        }
+    printf("invalid_count: %d\n", invalid_count);
+    printf("count: %d\n", count);
+
+
+    // 根据置信度 获得有效框
+    vector<vector<float>> info = get_info(result, 0.25, 6);
+    // 将推理的xywh 转换为原图中的 xyxy
+    info_simplify(info, width_scale, height_scale);
+    // 将推理结果按类别分割
+    vector<vector<vector<float>>> info_split = split_info(info);
+
+    printf("info size: %ld\n", info_split.size());
+    cv::resize(img, img, cv::Size(width, height));
+    for(auto i=0; i < info_split.size(); i++)
+    {
+        printf("class %d size: %ld\n", i, info_split[i].size());
+
+        // 根据阈值进行nms
+        nms(info_split[i], 0.45);
+
+        draw_box(img, info_split[i]);
+
+        printf("class %d size: %ld\n", i, info_split[i].size());
+
+    }
+
+    cv::imwrite("result.jpg", img);
+    return 0;
+}