# 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)