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https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +08:00
[Backend] support bechmark mode for runtime and backend (#1201)
* [backend] support bechmark mode for runtime and backend * [backend] support bechmark mode for runtime and backend * [pybind11] add benchmark methods pybind * [pybind11] add benchmark methods pybind * [Other] Update build scripts * [Other] Update cmake/summary.cmake * [Other] update build scripts * [Other] add ENABLE_BENCHMARK option -> setup.py * optimize backend time recording * optimize backend time recording * optimize trt backend time record * [backend] optimze backend_time recording for trt * [benchmark] remove redundant logs * fixed ov_backend confilct * [benchmark] fixed paddle_backend conflicts * [benchmark] fixed paddle_backend conflicts * [benchmark] fixed paddle_backend conflicts * [benchmark] remove use_gpu option from ort backend option * [benchmark] update benchmark_ppdet.py * [benchmark] update benchmark_ppcls.py * fixed lite backend conflicts * [Lite] fixed lite xpu * add benchmark macro * add RUNTIME_PROFILE_LOOP macros * add comments for RUNTIME_PROFILE macros * add comments for new apis * add comments for new apis * update benchmark_ppdet.py * afixed bugs * remove unused codes * optimize RUNTIME_PROFILE_LOOP macros * optimize RUNTIME_PROFILE_LOOP macros * add comments for benchmark option and result * add docs for benchmark namespace
This commit is contained in:
DefTruth
@@ -17,7 +17,7 @@ import cv2
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import os
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import numpy as np
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import time
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from tqdm import tqdm
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def parse_arguments():
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import argparse
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@@ -35,11 +35,22 @@ def parse_arguments():
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parser.add_argument(
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"--device_id", type=int, default=0, help="device(gpu) id")
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parser.add_argument(
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"--iter_num",
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"--profile_mode",
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type=str,
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default="runtime",
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help="runtime or end2end.")
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parser.add_argument(
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"--repeat",
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required=True,
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type=int,
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default=300,
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help="number of iterations for computing performace.")
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default=1000,
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help="number of repeats for profiling.")
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parser.add_argument(
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"--warmup",
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required=True,
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type=int,
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default=50,
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help="number of warmup for profiling.")
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parser.add_argument(
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"--device",
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default="cpu",
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@@ -59,6 +70,11 @@ def parse_arguments():
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type=ast.literal_eval,
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default=False,
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help="whether enable collect memory info")
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parser.add_argument(
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"--include_h2d_d2h",
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type=ast.literal_eval,
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default=False,
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help="whether run profiling with h2d and d2h")
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args = parser.parse_args()
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return args
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@@ -68,6 +84,8 @@ def build_option(args):
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device = args.device
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backend = args.backend
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enable_trt_fp16 = args.enable_trt_fp16
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if args.profile_mode == "runtime":
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option.enable_profiling(args.include_h2d_d2h, args.repeat, args.warmup)
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option.set_cpu_thread_num(args.cpu_num_thread)
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if device == "gpu":
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option.use_gpu()
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@@ -229,7 +247,6 @@ if __name__ == '__main__':
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gpu_id = args.device_id
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enable_collect_memory_info = args.enable_collect_memory_info
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dump_result = dict()
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end2end_statis = list()
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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@@ -257,19 +274,27 @@ if __name__ == '__main__':
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enable_gpu = args.device == "gpu"
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monitor = Monitor(enable_gpu, gpu_id)
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monitor.start()
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model.enable_record_time_of_runtime()
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im_ori = cv2.imread(args.image)
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for i in range(args.iter_num):
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im = im_ori
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if args.profile_mode == "runtime":
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result = model.predict(im_ori)
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profile_time = model.get_profile_time()
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dump_result["runtime"] = profile_time * 1000
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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print("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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else:
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# end2end
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for i in range(args.warmup):
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result = model.predict(im_ori)
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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for i in tqdm(range(args.repeat)):
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result = model.predict(im_ori)
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end = time.time()
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dump_result["end2end"] = ((end - start) / args.repeat) * 1000.0
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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print("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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if enable_collect_memory_info:
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monitor.stop()
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mem_info = monitor.output()
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@@ -279,14 +304,7 @@ if __name__ == '__main__':
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'memory.used'] if 'gpu' in mem_info else 0
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dump_result["gpu_util"] = mem_info['gpu'][
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'utilization.gpu'] if 'gpu' in mem_info else 0
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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print("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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print("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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if enable_collect_memory_info:
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f.writelines("cpu_rss_mb: {} \n".format(
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str(dump_result["cpu_rss_mb"])))
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@@ -297,7 +315,8 @@ if __name__ == '__main__':
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print("cpu_rss_mb: {} \n".format(str(dump_result["cpu_rss_mb"])))
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print("gpu_rss_mb: {} \n".format(str(dump_result["gpu_rss_mb"])))
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print("gpu_util: {} \n".format(str(dump_result["gpu_util"])))
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except:
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except Exception as e:
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f.writelines("!!!!!Infer Failed\n")
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raise e
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f.close()
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