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benchmark.cpp
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benchmark.cpp
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// 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.
#ifdef _WIN32
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#else // _WIN32
#include <sys/time.h>
#endif // _WIN32
#include "benchmark.h"
#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()
{
#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
}
#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