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be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#include "benchmark.h"
#if (__cplusplus >= 201103L || (defined(_MSVC_LANG) && _MSVC_LANG >= 201103L)) && !defined(__riscv) && !NCNN_SIMPLESTL
#include <chrono>
#include <thread>
#include <numeric>
#include <algorithm>
#else
#ifdef _WIN32
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#else // _WIN32
#include <sys/time.h> //gettimeofday()
#include <unistd.h> // sleep()
#endif // _WIN32
#endif
#if NCNN_BENCHMARK
#include "layer/convolution.h"
#include "layer/convolutiondepthwise.h"
#include "layer/deconvolution.h"
#include "layer/deconvolutiondepthwise.h"
#include <stdio.h>
#endif // NCNN_BENCHMARK
namespace ncnn {
double get_current_time()
{
#if (__cplusplus >= 201103L || (defined(_MSVC_LANG) && _MSVC_LANG >= 201103L)) && !defined(__riscv) && !NCNN_SIMPLESTL
auto now = std::chrono::high_resolution_clock::now();
auto usec = std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch());
return usec.count() / 1000.0;
#else
#ifdef _WIN32
LARGE_INTEGER freq;
LARGE_INTEGER pc;
QueryPerformanceFrequency(&freq);
QueryPerformanceCounter(&pc);
return pc.QuadPart * 1000.0 / freq.QuadPart;
#else // _WIN32
struct timeval tv;
gettimeofday(&tv, NULL);
return tv.tv_sec * 1000.0 + tv.tv_usec / 1000.0;
#endif // _WIN32
#endif
}
void sleep(unsigned long long int milliseconds)
{
#if (__cplusplus >= 201103L || (defined(_MSVC_LANG) && _MSVC_LANG >= 201103L)) && !defined(__riscv) && !NCNN_SIMPLESTL
std::this_thread::sleep_for(std::chrono::milliseconds(milliseconds));
#else
#ifdef _WIN32
Sleep(milliseconds);
#elif defined(__unix__) || defined(__APPLE__)
usleep(milliseconds * 1000);
#elif _POSIX_TIMERS
struct timespec ts;
ts.tv_sec = milliseconds * 0.001;
ts.tv_nsec = 0;
nanosleep(&ts, &ts);
#else
// TODO How to handle it ?
#endif
#endif
}
#if NCNN_BENCHMARK
void benchmark(const Layer* layer, double start, double end)
{
fprintf(stderr, "%-24s %-30s %8.2lfms", layer->type.c_str(), layer->name.c_str(), end - start);
fprintf(stderr, " |");
fprintf(stderr, "\n");
}
void benchmark(const Layer* layer, const Mat& bottom_blob, Mat& top_blob, double start, double end)
{
fprintf(stderr, "%-24s %-30s %8.2lfms", layer->type.c_str(), layer->name.c_str(), end - start);
char in_shape_str[64] = {'\0'};
char out_shape_str[64] = {'\0'};
if (bottom_blob.dims == 1)
{
sprintf(in_shape_str, "[%3d *%d]", bottom_blob.w, bottom_blob.elempack);
}
if (bottom_blob.dims == 2)
{
sprintf(in_shape_str, "[%3d, %3d *%d]", bottom_blob.w, bottom_blob.h, bottom_blob.elempack);
}
if (bottom_blob.dims == 3)
{
sprintf(in_shape_str, "[%3d, %3d, %3d *%d]", bottom_blob.w, bottom_blob.h, bottom_blob.c, bottom_blob.elempack);
}
if (top_blob.dims == 1)
{
sprintf(out_shape_str, "[%3d *%d]", top_blob.w, top_blob.elempack);
}
if (top_blob.dims == 2)
{
sprintf(out_shape_str, "[%3d, %3d *%d]", top_blob.w, top_blob.h, top_blob.elempack);
}
if (top_blob.dims == 3)
{
sprintf(out_shape_str, "[%3d, %3d, %3d *%d]", top_blob.w, top_blob.h, top_blob.c, top_blob.elempack);
}
fprintf(stderr, " | %22s -> %-22s", in_shape_str, out_shape_str);
if (layer->type == "Convolution")
{
fprintf(stderr, " kernel: %1d x %1d stride: %1d x %1d",
((Convolution*)layer)->kernel_w,
((Convolution*)layer)->kernel_h,
((Convolution*)layer)->stride_w,
((Convolution*)layer)->stride_h);
}
else if (layer->type == "ConvolutionDepthWise")
{
fprintf(stderr, " kernel: %1d x %1d stride: %1d x %1d",
((ConvolutionDepthWise*)layer)->kernel_w,
((ConvolutionDepthWise*)layer)->kernel_h,
((ConvolutionDepthWise*)layer)->stride_w,
((ConvolutionDepthWise*)layer)->stride_h);
}
else if (layer->type == "Deconvolution")
{
fprintf(stderr, " kernel: %1d x %1d stride: %1d x %1d",
((Deconvolution*)layer)->kernel_w,
((Deconvolution*)layer)->kernel_h,
((Deconvolution*)layer)->stride_w,
((Deconvolution*)layer)->stride_h);
}
else if (layer->type == "DeconvolutionDepthWise")
{
fprintf(stderr, " kernel: %1d x %1d stride: %1d x %1d",
((DeconvolutionDepthWise*)layer)->kernel_w,
((DeconvolutionDepthWise*)layer)->kernel_h,
((DeconvolutionDepthWise*)layer)->stride_w,
((DeconvolutionDepthWise*)layer)->stride_h);
}
fprintf(stderr, "\n");
}
#endif // NCNN_BENCHMARK
} // namespace ncnn
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