# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # 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. import fastdeploy as fd import cv2 import os import numpy as np import time from fastdeploy import ModelFormat def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() parser.add_argument( "--model", required=True, help="Path of Yolo onnx model.") parser.add_argument( "--image", type=str, required=False, help="Path of test image file.") parser.add_argument( "--cpu_num_thread", type=int, default=8, help="default number of cpu thread.") parser.add_argument( "--device_id", type=int, default=0, help="device(gpu) id") parser.add_argument( "--iter_num", required=True, type=int, default=300, help="number of iterations for computing performance.") parser.add_argument( "--device", default="cpu", help="Type of inference device, support 'cpu' or 'gpu'.") parser.add_argument( "--backend", type=str, default="default", help="inference backend, default, ort, ov, trt, paddle, paddle_trt.") parser.add_argument( "--enable_trt_fp16", type=ast.literal_eval, default=False, help="whether enable fp16 in trt backend") parser.add_argument( "--enable_collect_memory_info", type=ast.literal_eval, default=False, help="whether enable collect memory info") args = parser.parse_args() return args def build_option(args): option = fd.RuntimeOption() device = args.device backend = args.backend enable_trt_fp16 = args.enable_trt_fp16 option.set_cpu_thread_num(args.cpu_num_thread) if device == "gpu": option.use_gpu() if backend == "ort": option.use_ort_backend() elif backend == "paddle": option.use_paddle_backend() elif backend == "ov": option.use_openvino_backend() option.set_openvino_device(name="GPU") # change name and shape for models option.set_openvino_shape_info({"images": [1, 3, 640, 640]}) elif backend in ["trt", "paddle_trt"]: option.use_trt_backend() if backend == "paddle_trt": option.use_paddle_infer_backend() option.paddle_infer_option.enable_trt = True if enable_trt_fp16: option.enable_trt_fp16() elif backend == "default": return option else: raise Exception( "While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.". format(backend)) elif device == "cpu": if backend == "ort": option.use_ort_backend() elif backend == "ov": option.use_openvino_backend() elif backend == "paddle": option.use_paddle_backend() elif backend == "default": return option else: raise Exception( "While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.". format(backend)) else: raise Exception( "Only support device CPU/GPU now, {} is not supported.".format( device)) return option class StatBase(object): """StatBase""" nvidia_smi_path = "nvidia-smi" gpu_keys = ('index', 'uuid', 'name', 'timestamp', 'memory.total', 'memory.free', 'memory.used', 'utilization.gpu', 'utilization.memory') nu_opt = ',nounits' cpu_keys = ('cpu.util', 'memory.util', 'memory.used') class Monitor(StatBase): """Monitor""" def __init__(self, use_gpu=False, gpu_id=0, interval=0.1): self.result = {} self.gpu_id = gpu_id self.use_gpu = use_gpu self.interval = interval self.cpu_stat_q = multiprocessing.Queue() def start(self): cmd = '%s --id=%s --query-gpu=%s --format=csv,noheader%s -lms 50' % ( StatBase.nvidia_smi_path, self.gpu_id, ','.join(StatBase.gpu_keys), StatBase.nu_opt) if self.use_gpu: self.gpu_stat_worker = subprocess.Popen( cmd, stderr=subprocess.STDOUT, stdout=subprocess.PIPE, shell=True, close_fds=True, preexec_fn=os.setsid) # cpu stat pid = os.getpid() self.cpu_stat_worker = multiprocessing.Process( target=self.cpu_stat_func, args=(self.cpu_stat_q, pid, self.interval)) self.cpu_stat_worker.start() def stop(self): try: if self.use_gpu: os.killpg(self.gpu_stat_worker.pid, signal.SIGUSR1) # os.killpg(p.pid, signal.SIGTERM) self.cpu_stat_worker.terminate() self.cpu_stat_worker.join(timeout=0.01) except Exception as e: print(e) return # gpu if self.use_gpu: lines = self.gpu_stat_worker.stdout.readlines() lines = [ line.strip().decode("utf-8") for line in lines if line.strip() != '' ] gpu_info_list = [{ k: v for k, v in zip(StatBase.gpu_keys, line.split(', ')) } for line in lines] if len(gpu_info_list) == 0: return result = gpu_info_list[0] for item in gpu_info_list: for k in item.keys(): if k not in ["name", "uuid", "timestamp"]: result[k] = max(int(result[k]), int(item[k])) else: result[k] = max(result[k], item[k]) self.result['gpu'] = result # cpu cpu_result = {} if self.cpu_stat_q.qsize() > 0: cpu_result = { k: v for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get()) } while not self.cpu_stat_q.empty(): item = { k: v for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get()) } for k in StatBase.cpu_keys: cpu_result[k] = max(cpu_result[k], item[k]) cpu_result['name'] = cpuinfo.get_cpu_info()['brand_raw'] self.result['cpu'] = cpu_result def output(self): return self.result def cpu_stat_func(self, q, pid, interval=0.0): """cpu stat function""" stat_info = psutil.Process(pid) while True: # pid = os.getpid() cpu_util, mem_util, mem_use = stat_info.cpu_percent( ), stat_info.memory_percent(), round(stat_info.memory_info().rss / 1024.0 / 1024.0, 4) q.put([cpu_util, mem_util, mem_use]) time.sleep(interval) return if __name__ == '__main__': args = parse_arguments() option = build_option(args) model_file = args.model gpu_id = args.device_id enable_collect_memory_info = args.enable_collect_memory_info dump_result = dict() end2end_statis = list() cpu_mem = list() gpu_mem = list() gpu_util = list() if args.device == "cpu": file_path = args.model + "_model_" + args.backend + "_" + \ args.device + "_" + str(args.cpu_num_thread) + ".txt" else: if args.enable_trt_fp16: file_path = args.model + "_model_" + args.backend + "_fp16_" + args.device + ".txt" else: file_path = args.model + "_model_" + args.backend + "_" + args.device + ".txt" f = open(file_path, "w") f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4])) try: if "yolox" in model_file: if ".onnx" in model_file: model = fd.vision.detection.YOLOX( model_file, runtime_option=option) else: model_file = os.path.join(args.model, "model.pdmodel") params_file = os.path.join(args.model, "model.pdiparams") model = fd.vision.detection.YOLOX( model_file, params_file, runtime_option=option, model_format=ModelFormat.PADDLE) elif "yolov5" in model_file: if ".onnx" in model_file: model = fd.vision.detection.YOLOv5( model_file, runtime_option=option) else: model_file = os.path.join(args.model, "model.pdmodel") params_file = os.path.join(args.model, "model.pdiparams") model = fd.vision.detection.YOLOv5( model_file, params_file, runtime_option=option, model_format=ModelFormat.PADDLE) elif "yolov6" in model_file: if ".onnx" in model_file: model = fd.vision.detection.YOLOv6( model_file, runtime_option=option) else: model_file = os.path.join(args.model, "model.pdmodel") params_file = os.path.join(args.model, "model.pdiparams") model = fd.vision.detection.YOLOv6( model_file, params_file, runtime_option=option, model_format=ModelFormat.PADDLE) elif "yolov7" in model_file: if ".onnx" in model_file: model = fd.vision.detection.YOLOv7( model_file, runtime_option=option) else: model_file = os.path.join(args.model, "model.pdmodel") params_file = os.path.join(args.model, "model.pdiparams") model = fd.vision.detection.YOLOv7( model_file, params_file, runtime_option=option, model_format=ModelFormat.PADDLE) else: raise Exception("model {} not support now in yolo series".format( args.model)) if enable_collect_memory_info: import multiprocessing import subprocess import psutil import signal import cpuinfo enable_gpu = args.device == "gpu" monitor = Monitor(enable_gpu, gpu_id) monitor.start() model.enable_record_time_of_runtime() im_ori = cv2.imread(args.image) for i in range(args.iter_num): im = im_ori start = time.time() result = model.predict(im) end2end_statis.append(time.time() - start) runtime_statis = model.print_statis_info_of_runtime() warmup_iter = args.iter_num // 5 end2end_statis_repeat = end2end_statis[warmup_iter:] if enable_collect_memory_info: monitor.stop() mem_info = monitor.output() dump_result["cpu_rss_mb"] = mem_info['cpu'][ 'memory.used'] if 'cpu' in mem_info else 0 dump_result["gpu_rss_mb"] = mem_info['gpu'][ 'memory.used'] if 'gpu' in mem_info else 0 dump_result["gpu_util"] = mem_info['gpu'][ 'utilization.gpu'] if 'gpu' in mem_info else 0 dump_result["runtime"] = runtime_statis["avg_time"] * 1000 dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000 f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"]))) f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"]))) print("Runtime(ms): {} \n".format(str(dump_result["runtime"]))) print("End2End(ms): {} \n".format(str(dump_result["end2end"]))) if enable_collect_memory_info: f.writelines("cpu_rss_mb: {} \n".format( str(dump_result["cpu_rss_mb"]))) f.writelines("gpu_rss_mb: {} \n".format( str(dump_result["gpu_rss_mb"]))) f.writelines("gpu_util: {} \n".format( str(dump_result["gpu_util"]))) print("cpu_rss_mb: {} \n".format(str(dump_result["cpu_rss_mb"]))) print("gpu_rss_mb: {} \n".format(str(dump_result["gpu_rss_mb"]))) print("gpu_util: {} \n".format(str(dump_result["gpu_util"]))) except: f.writelines("!!!!!Infer Failed\n") f.close()