TensorRT-使用tensorrt在GPU上部署CenterNet-优质算法部署项目实战.zip

# benchmark [](https://travis-ci.org/google/benchmark) [](https://ci.appveyor.com/project/google/benchmark/branch/master) [](https://coveralls.io/r/google/benchmark) [](https://slackin-iqtfqnpzxd.now.sh/) A library to support the benchmarking of functions, similar to unit-tests. Discussion group: https://groups.google.com/d/forum/benchmark-discuss IRC channel: https://freenode.net #googlebenchmark [Known issues and common problems](#known-issues) [Additional Tooling Documentation](docs/tools.md) [Assembly Testing Documentation](docs/AssemblyTests.md) ## Building The basic steps for configuring and building the library look like this: ```bash $ git clone https://github.com/google/benchmark.git # Benchmark requires Google Test as a dependency. Add the source tree as a subdirectory. $ git clone https://github.com/google/googletest.git benchmark/googletest $ mkdir build && cd build $ cmake -G <generator> [options] ../benchmark # Assuming a makefile generator was used $ make ``` Note that Google Benchmark requires Google Test to build and run the tests. This dependency can be provided two ways: * Checkout the Google Test sources into `benchmark/googletest` as above. * Otherwise, if `-DBENCHMARK_DOWNLOAD_DEPENDENCIES=ON` is specified during configuration, the library will automatically download and build any required dependencies. If you do not wish to build and run the tests, add `-DBENCHMARK_ENABLE_GTEST_TESTS=OFF` to `CMAKE_ARGS`. ## Installation Guide For Ubuntu and Debian Based System First make sure you have git and cmake installed (If not please install it) ``` sudo apt-get install git sudo apt-get install cmake ``` Now, let's clone the repository and build it ``` git clone https://github.com/google/benchmark.git cd benchmark git clone https://github.com/google/googletest.git mkdir build cd build cmake .. -DCMAKE_BUILD_TYPE=RELEASE make ``` We need to install the library globally now ``` sudo make install ``` Now you have google/benchmark installed in your machine Note: Don't forget to link to pthread library while building ## Stable and Experimental Library Versions The main branch contains the latest stable version of the benchmarking library; the API of which can be considered largely stable, with source breaking changes being made only upon the release of a new major version. Newer, experimental, features are implemented and tested on the [`v2` branch](https://github.com/google/benchmark/tree/v2). Users who wish to use, test, and provide feedback on the new features are encouraged to try this branch. However, this branch provides no stability guarantees and reserves the right to change and break the API at any time. ##Prerequisite knowledge Before attempting to understand this framework one should ideally have some familiarity with the structure and format of the Google Test framework, upon which it is based. Documentation for Google Test, including a "Getting Started" (primer) guide, is available here: https://github.com/google/googletest/blob/master/googletest/docs/Documentation.md ## Example usage ### Basic usage Define a function that executes the code to be measured. ```c++ #include <benchmark/benchmark.h> static void BM_StringCreation(benchmark::State& state) { for (auto _ : state) std::string empty_string; } // Register the function as a benchmark BENCHMARK(BM_StringCreation); // Define another benchmark static void BM_StringCopy(benchmark::State& state) { std::string x = "hello"; for (auto _ : state) std::string copy(x); } BENCHMARK(BM_StringCopy); BENCHMARK_MAIN(); ``` Don't forget to inform your linker to add benchmark library e.g. through `-lbenchmark` compilation flag. Alternatively, you may leave out the `BENCHMARK_MAIN();` at the end of the source file and link against `-lbenchmark_main` to get the same default behavior. The benchmark library will reporting the timing for the code within the `for(...)` loop. ### Passing arguments Sometimes a family of benchmarks can be implemented with just one routine that takes an extra argument to specify which one of the family of benchmarks to run. For example, the following code defines a family of benchmarks for measuring the speed of `memcpy()` calls of different lengths: ```c++ static void BM_memcpy(benchmark::State& state) { char* src = new char[state.range(0)]; char* dst = new char[state.range(0)]; memset(src, 'x', state.range(0)); for (auto _ : state) memcpy(dst, src, state.range(0)); state.SetBytesProcessed(int64_t(state.iterations()) * int64_t(state.range(0))); delete[] src; delete[] dst; } BENCHMARK(BM_memcpy)->Arg(8)->Arg(64)->Arg(512)->Arg(1<<10)->Arg(8<<10); ``` The preceding code is quite repetitive, and can be replaced with the following short-hand. The following invocation will pick a few appropriate arguments in the specified range and will generate a benchmark for each such argument. ```c++ BENCHMARK(BM_memcpy)->Range(8, 8<<10); ``` By default the arguments in the range are generated in multiples of eight and the command above selects [ 8, 64, 512, 4k, 8k ]. In the following code the range multiplier is changed to multiples of two. ```c++ BENCHMARK(BM_memcpy)->RangeMultiplier(2)->Range(8, 8<<10); ``` Now arguments generated are [ 8, 16, 32, 64, 128, 256, 512, 1024, 2k, 4k, 8k ]. You might have a benchmark that depends on two or more inputs. For example, the following code defines a family of benchmarks for measuring the speed of set insertion. ```c++ static void BM_SetInsert(benchmark::State& state) { std::set<int> data; for (auto _ : state) { state.PauseTiming(); data = ConstructRandomSet(state.range(0)); state.ResumeTiming(); for (int j = 0; j < state.range(1); ++j) data.insert(RandomNumber()); } } BENCHMARK(BM_SetInsert) ->Args({1<<10, 128}) ->Args({2<<10, 128}) ->Args({4<<10, 128}) ->Args({8<<10, 128}) ->Args({1<<10, 512}) ->Args({2<<10, 512}) ->Args({4<<10, 512}) ->Args({8<<10, 512}); ``` The preceding code is quite repetitive, and can be replaced with the following short-hand. The following macro will pick a few appropriate arguments in the product of the two specified ranges and will generate a benchmark for each such pair. ```c++ BENCHMARK(BM_SetInsert)->Ranges({{1<<10, 8<<10}, {128, 512}}); ``` For more complex patterns of inputs, passing a custom function to `Apply` allows programmatic specification of an arbitrary set of arguments on which to run the benchmark. The following example enumerates a dense range on one parameter, and a sparse range on the second. ```c++ static void CustomArguments(benchmark::internal::Benchmark* b) { for (int i = 0; i <= 10; ++i) for (int j = 32; j <= 1024*1024; j *= 8) b->Args({i, j}); } BENCHMARK(BM_SetInsert)->Apply(CustomArguments); ``` ### Calculate asymptotic complexity (Big O) Asymptotic complexity might be calculated for a family of benchmarks. The following code will calculate the coefficient for the high-order term in the running time and the normalized root-mean square error of string comparison. ```c++ static void BM_StringCompare(benchmark::State& state) { std::string s1(state.range(0), '-'); std::string s2(state.range(0), '-'); for (auto _ : state) { benchmark::DoNotOptimize(s1.compare(s2)); } state.SetComplexityN(state.range(0)); } BENCHMARK(BM_StringCompare) ->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity(benchmark::oN); ``` As shown in the following invocation, asymptotic complexity might also be calculated automatically. ```c++ BENCHMARK(BM_StringCompare) ->RangeMultiplier(2)->Range(1<<10, 1<<18)->Complexity()