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- # 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(
- "--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='fluid',
- help="mode of running(fluid/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(
- "--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(
- "--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=bool,
- default=True,
- help='whether to use darkpose to get better keypoint position predict ')
- 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):
- super(Timer, self).__init__()
- self.preprocess_time_s = Times()
- self.inference_time_s = Times()
- self.postprocess_time_s = Times()
- self.img_num = 0
-
- def info(self, average=False):
- total_time = self.preprocess_time_s.value(
- ) + self.inference_time_s.value() + self.postprocess_time_s.value()
- 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(
- self.preprocess_time_s.value() / max(1, self.img_num),
- 4) if average else self.preprocess_time_s.value()
- postprocess_time = round(
- self.postprocess_time_s.value() / max(1, self.img_num),
- 4) if average else self.postprocess_time_s.value()
- inference_time = round(self.inference_time_s.value() /
- max(1, self.img_num),
- 4) if average else self.inference_time_s.value()
-
- 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))
- 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 = {}
- dic['preprocess_time_s'] = round(
- self.preprocess_time_s.value() / max(1, self.img_num),
- 4) if average else self.preprocess_time_s.value()
- dic['postprocess_time_s'] = round(
- self.postprocess_time_s.value() / max(1, self.img_num),
- 4) if average else self.postprocess_time_s.value()
- dic['inference_time_s'] = round(
- self.inference_time_s.value() / max(1, self.img_num),
- 4) if average else self.inference_time_s.value()
- dic['img_num'] = self.img_num
- total_time = self.preprocess_time_s.value(
- ) + self.inference_time_s.value() + self.postprocess_time_s.value()
- 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)
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