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https://github.com/PaddlePaddle/FastDeploy.git
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[Benchmark]Add Failed Log (#1693)
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug * fixed thread nums * Add Failed log * optimizer x86 pipeline
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@@ -36,6 +36,14 @@
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if (__config_info__["profile_mode"] == "runtime") { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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__ss__ << "Runtime(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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return 0; \
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} \
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double __profile_time__ = MODEL_NAME.GetProfileTime() * 1000; \
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@@ -49,6 +57,14 @@
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for (int __i__ = 0; __i__ < __warmup__; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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__ss__ << "End2End(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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return 0; \
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} \
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} \
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@@ -62,6 +78,14 @@
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for (int __i__ = 0; __i__ < __repeat__; __i__++) { \
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if (!BENCHMARK_FUNC) { \
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std::cerr << "Failed to predict." << std::endl; \
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__ss__ << "End2End(ms): Failed" << std::endl; \
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if (__config_info__["collect_memory_info"] == "true") { \
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__ss__ << "cpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_rss_mb: Failed" << std::endl; \
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__ss__ << "gpu_util: Failed" << std::endl; \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult( \
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__ss__.str(), __config_info__["result_path"]); \
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return 0; \
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} \
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} \
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@@ -79,7 +103,7 @@
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std::cout << "gpu_rss_mb: " << __gpu_mem__ << "MB." << std::endl; \
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__ss__ << "gpu_rss_mb: " << __gpu_mem__ << "MB." << std::endl; \
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std::cout << "gpu_util: " << __gpu_util__ << std::endl; \
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__ss__ << "gpu_util: " << __gpu_util__ << "MB." << std::endl; \
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__ss__ << "gpu_util: " << __gpu_util__ << "MB." << std::endl; \
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__resource_moniter__.Stop(); \
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} \
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fastdeploy::benchmark::ResultManager::SaveBenchmarkResult(__ss__.str(), \
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