# Copyright (c) 2021 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 time import os import ast import argparse def argsparser(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--model_dir", type=str, default=None, help=("Directory include:'model.pdiparams', 'model.pdmodel', " "'infer_cfg.yml', created by tools/export_model.py."), required=True) parser.add_argument("--det_algorithm", type=str, default='DB') parser.add_argument("--det_model_dir", type=str) parser.add_argument("--det_limit_side_len", type=float, default=960) parser.add_argument("--det_limit_type", type=str, default='max') parser.add_argument("--rec_algorithm", type=str, default='SVTR_LCNet') parser.add_argument("--rec_model_dir", type=str) parser.add_argument("--rec_image_shape", type=str, default="3, 48, 320") parser.add_argument("--rec_batch_num", type=int, default=6) parser.add_argument( "--image_file", type=str, default=None, help="Path of image file.") parser.add_argument( "--image_dir", type=str, default=None, help="Dir of image file, `image_file` has a higher priority.") parser.add_argument( "--batch_size", type=int, default=1, help="batch_size for inference.") parser.add_argument( "--video_file", type=str, default=None, help="Path of video file, `video_file` or `camera_id` has a highest priority." ) parser.add_argument( "--camera_id", type=int, default=-1, help="device id of camera to predict.") parser.add_argument( "--threshold", type=float, default=0.5, help="Threshold of score.") parser.add_argument( "--output_dir", type=str, default="output", help="Directory of output visualization files.") parser.add_argument( "--run_mode", type=str, default='paddle', help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)") parser.add_argument( "--device", type=str, default='cpu', help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU." ) parser.add_argument( "--use_gpu", type=ast.literal_eval, default=False, help="Deprecated, please use `--device`.") parser.add_argument( "--run_benchmark", type=ast.literal_eval, default=False, help="Whether to predict a image_file repeatedly for benchmark") parser.add_argument( "--enable_mkldnn", type=ast.literal_eval, default=False, help="Whether use mkldnn with CPU.") parser.add_argument( "--enable_mkldnn_bfloat16", type=ast.literal_eval, default=False, help="Whether use mkldnn bfloat16 inference with CPU.") parser.add_argument( "--cpu_threads", type=int, default=1, help="Num of threads with CPU.") parser.add_argument( "--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.") parser.add_argument( "--trt_max_shape", type=int, default=1280, help="max_shape for TensorRT.") parser.add_argument( "--trt_opt_shape", type=int, default=640, help="opt_shape for TensorRT.") parser.add_argument( "--trt_calib_mode", type=bool, default=False, help="If the model is produced by TRT offline quantitative " "calibration, trt_calib_mode need to set True.") parser.add_argument( '--save_images', action='store_true', help='Save visualization image results.') parser.add_argument( '--save_mot_txts', action='store_true', help='Save tracking results (txt).') parser.add_argument( '--save_mot_txt_per_img', action='store_true', help='Save tracking results (txt) for each image.') parser.add_argument( '--scaled', type=bool, default=False, help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 " "True in general detector.") parser.add_argument( "--tracker_config", type=str, default=None, help=("tracker donfig")) parser.add_argument( "--reid_model_dir", type=str, default=None, help=("Directory include:'model.pdiparams', 'model.pdmodel', " "'infer_cfg.yml', created by tools/export_model.py.")) parser.add_argument( "--reid_batch_size", type=int, default=50, help="max batch_size for reid model inference.") parser.add_argument( '--use_dark', type=ast.literal_eval, default=True, help='whether to use darkpose to get better keypoint position predict ') parser.add_argument( "--action_file", type=str, default=None, help="Path of input file for action recognition.") parser.add_argument( "--window_size", type=int, default=50, help="Temporal size of skeleton feature for action recognition.") parser.add_argument( "--random_pad", type=ast.literal_eval, default=False, help="Whether do random padding for action recognition.") parser.add_argument( "--save_results", type=bool, default=False, help="Whether save detection result to file using coco format") return parser class Times(object): def __init__(self): self.time = 0. # start time self.st = 0. # end time self.et = 0. def start(self): self.st = time.time() def end(self, repeats=1, accumulative=True): self.et = time.time() if accumulative: self.time += (self.et - self.st) / repeats else: self.time = (self.et - self.st) / repeats def reset(self): self.time = 0. self.st = 0. self.et = 0. def value(self): return round(self.time, 4) class Timer(Times): def __init__(self, with_tracker=False): super(Timer, self).__init__() self.with_tracker = with_tracker self.preprocess_time_s = Times() self.inference_time_s = Times() self.postprocess_time_s = Times() self.tracking_time_s = Times() self.img_num = 0 def info(self, average=False): pre_time = self.preprocess_time_s.value() infer_time = self.inference_time_s.value() post_time = self.postprocess_time_s.value() track_time = self.tracking_time_s.value() total_time = pre_time + infer_time + post_time if self.with_tracker: total_time = total_time + track_time total_time = round(total_time, 4) print("------------------ Inference Time Info ----------------------") print("total_time(ms): {}, img_num: {}".format(total_time * 1000, self.img_num)) preprocess_time = round(pre_time / max(1, self.img_num), 4) if average else pre_time postprocess_time = round(post_time / max(1, self.img_num), 4) if average else post_time inference_time = round(infer_time / max(1, self.img_num), 4) if average else infer_time tracking_time = round(track_time / max(1, self.img_num), 4) if average else track_time average_latency = total_time / max(1, self.img_num) qps = 0 if total_time > 0: qps = 1 / average_latency print("average latency time(ms): {:.2f}, QPS: {:2f}".format( average_latency * 1000, qps)) if self.with_tracker: print( "preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}". format(preprocess_time * 1000, inference_time * 1000, postprocess_time * 1000, tracking_time * 1000)) else: print( "preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}". format(preprocess_time * 1000, inference_time * 1000, postprocess_time * 1000)) def report(self, average=False): dic = {} pre_time = self.preprocess_time_s.value() infer_time = self.inference_time_s.value() post_time = self.postprocess_time_s.value() track_time = self.tracking_time_s.value() dic['preprocess_time_s'] = round(pre_time / max(1, self.img_num), 4) if average else pre_time dic['inference_time_s'] = round(infer_time / max(1, self.img_num), 4) if average else infer_time dic['postprocess_time_s'] = round(post_time / max(1, self.img_num), 4) if average else post_time dic['img_num'] = self.img_num total_time = pre_time + infer_time + post_time if self.with_tracker: dic['tracking_time_s'] = round(track_time / max(1, self.img_num), 4) if average else track_time total_time = total_time + track_time dic['total_time_s'] = round(total_time, 4) return dic def get_current_memory_mb(): """ It is used to Obtain the memory usage of the CPU and GPU during the running of the program. And this function Current program is time-consuming. """ import pynvml import psutil import GPUtil gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0)) pid = os.getpid() p = psutil.Process(pid) info = p.memory_full_info() cpu_mem = info.uss / 1024. / 1024. gpu_mem = 0 gpu_percent = 0 gpus = GPUtil.getGPUs() if gpu_id is not None and len(gpus) > 0: gpu_percent = gpus[gpu_id].load pynvml.nvmlInit() handle = pynvml.nvmlDeviceGetHandleByIndex(0) meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle) gpu_mem = meminfo.used / 1024. / 1024. return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)