mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
da94fc46cf
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme * add cpp benchmark * add cpu and gpu mem * public part split * add runtime mode * fixed bugs * add cpu_thread_nums * deal with comments * deal with comments * deal with comments * rm useless code * add FASTDEPLOY_DECL * add FASTDEPLOY_DECL * fixed for windows * mv rss to pss * mv rss to pss * Update utils.cc * use thread to collect mem * Add ResourceUsageMonitor * rm useless code * fixed bug * fixed typo * update ResourceUsageMonitor * fixed bug * fixed bug * add note for ResourceUsageMonitor * deal with comments * add macros * deal with comments * deal with comments * deal with comments * re-lint * rm pmap and use mem api * rm pmap and use mem api * add mem api * Add PrintBenchmarkInfo func * Add PrintBenchmarkInfo func * Add PrintBenchmarkInfo func * deal with comments * fixed enable_paddle_to_trt * add log for paddle_trt * support ppcls benchmark * use new trt option api * update benchmark info * simplify benchmark.cc * simplify benchmark.cc * deal with comments --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
329 lines
12 KiB
Python
Executable File
329 lines
12 KiB
Python
Executable File
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import fastdeploy as fd
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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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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of PaddleClas model.")
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parser.add_argument(
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"--image", type=str, required=False, help="Path of test image file.")
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parser.add_argument(
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"--cpu_num_thread",
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type=int,
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default=8,
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help="default number of cpu thread.")
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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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"--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=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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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--backend",
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type=str,
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default="default",
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help="inference backend, default, ort, ov, trt, paddle, paddle_trt.")
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parser.add_argument(
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"--enable_trt_fp16",
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type=ast.literal_eval,
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default=False,
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help="whether enable fp16 in trt backend")
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parser.add_argument(
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"--enable_collect_memory_info",
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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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def build_option(args):
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option = fd.RuntimeOption()
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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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if backend == "ort":
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option.use_ort_backend()
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elif backend == "paddle":
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option.use_paddle_backend()
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elif backend == "ov":
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option.use_openvino_backend()
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option.set_openvino_device(name="GPU")
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# change name and shape for models
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option.set_openvino_shape_info({"x": [1, 3, 224, 224]})
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elif backend in ["trt", "paddle_trt"]:
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option.use_trt_backend()
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if backend == "paddle_trt":
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option.use_paddle_infer_backend()
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option.paddle_infer_option.enable_trt = True
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# Set max_batch_size 1 for best performance
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option.trt_option.max_batch_size = 1
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if enable_trt_fp16:
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option.enable_trt_fp16()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.".
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format(backend))
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elif device == "cpu":
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if backend == "ort":
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option.use_ort_backend()
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elif backend == "ov":
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option.use_openvino_backend()
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elif backend == "paddle":
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option.use_paddle_backend()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.".
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format(backend))
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else:
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raise Exception(
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"Only support device CPU/GPU now, {} is not supported.".format(
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device))
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return option
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class StatBase(object):
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"""StatBase"""
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nvidia_smi_path = "nvidia-smi"
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gpu_keys = ('index', 'uuid', 'name', 'timestamp', 'memory.total',
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'memory.free', 'memory.used', 'utilization.gpu',
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'utilization.memory')
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nu_opt = ',nounits'
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cpu_keys = ('cpu.util', 'memory.util', 'memory.used')
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class Monitor(StatBase):
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"""Monitor"""
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def __init__(self, use_gpu=False, gpu_id=0, interval=0.1):
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self.result = {}
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self.gpu_id = gpu_id
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self.use_gpu = use_gpu
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self.interval = interval
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self.cpu_stat_q = multiprocessing.Queue()
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def start(self):
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cmd = '%s --id=%s --query-gpu=%s --format=csv,noheader%s -lms 50' % (
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StatBase.nvidia_smi_path, self.gpu_id, ','.join(StatBase.gpu_keys),
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StatBase.nu_opt)
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if self.use_gpu:
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self.gpu_stat_worker = subprocess.Popen(
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cmd,
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stderr=subprocess.STDOUT,
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stdout=subprocess.PIPE,
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shell=True,
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close_fds=True,
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preexec_fn=os.setsid)
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# cpu stat
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pid = os.getpid()
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self.cpu_stat_worker = multiprocessing.Process(
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target=self.cpu_stat_func,
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args=(self.cpu_stat_q, pid, self.interval))
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self.cpu_stat_worker.start()
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def stop(self):
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try:
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if self.use_gpu:
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os.killpg(self.gpu_stat_worker.pid, signal.SIGUSR1)
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# os.killpg(p.pid, signal.SIGTERM)
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self.cpu_stat_worker.terminate()
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self.cpu_stat_worker.join(timeout=0.01)
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except Exception as e:
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print(e)
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return
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# gpu
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if self.use_gpu:
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lines = self.gpu_stat_worker.stdout.readlines()
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lines = [
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line.strip().decode("utf-8") for line in lines
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if line.strip() != ''
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]
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gpu_info_list = [{
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k: v
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for k, v in zip(StatBase.gpu_keys, line.split(', '))
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} for line in lines]
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if len(gpu_info_list) == 0:
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return
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result = gpu_info_list[0]
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for item in gpu_info_list:
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for k in item.keys():
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if k not in ["name", "uuid", "timestamp"]:
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result[k] = max(int(result[k]), int(item[k]))
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else:
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result[k] = max(result[k], item[k])
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self.result['gpu'] = result
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# cpu
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cpu_result = {}
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if self.cpu_stat_q.qsize() > 0:
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cpu_result = {
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k: v
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for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
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}
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while not self.cpu_stat_q.empty():
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item = {
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k: v
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for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
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}
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for k in StatBase.cpu_keys:
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cpu_result[k] = max(cpu_result[k], item[k])
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cpu_result['name'] = cpuinfo.get_cpu_info()['brand_raw']
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self.result['cpu'] = cpu_result
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def output(self):
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return self.result
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def cpu_stat_func(self, q, pid, interval=0.0):
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"""cpu stat function"""
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stat_info = psutil.Process(pid)
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while True:
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# pid = os.getpid()
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cpu_util, mem_util, mem_use = stat_info.cpu_percent(
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), stat_info.memory_percent(), round(stat_info.memory_info().rss /
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1024.0 / 1024.0, 4)
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q.put([cpu_util, mem_util, mem_use])
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time.sleep(interval)
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return
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if __name__ == '__main__':
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args = parse_arguments()
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option = build_option(args)
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model_file = os.path.join(args.model, "inference.pdmodel")
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params_file = os.path.join(args.model, "inference.pdiparams")
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config_file = os.path.join(args.model, "inference_cls.yaml")
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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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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_thread) + ".txt"
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else:
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if args.enable_trt_fp16:
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file_path = args.model + "_model_" + \
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args.backend + "_fp16_" + args.device + ".txt"
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else:
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file_path = args.model + "_model_" + args.backend + "_" + args.device + ".txt"
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f = open(file_path, "w")
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f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4]))
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try:
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model = fd.vision.classification.PaddleClasModel(
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model_file, params_file, config_file, runtime_option=option)
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if enable_collect_memory_info:
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import multiprocessing
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import subprocess
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import psutil
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import signal
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import cpuinfo
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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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im_ori = cv2.imread(args.image)
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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(
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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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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(
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str(dump_result["end2end"])))
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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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monitor.stop()
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mem_info = monitor.output()
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dump_result["cpu_rss_mb"] = mem_info['cpu'][
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'memory.used'] if 'cpu' in mem_info else 0
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dump_result["gpu_rss_mb"] = mem_info['gpu'][
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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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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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f.writelines("gpu_rss_mb: {} \n".format(
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str(dump_result["gpu_rss_mb"])))
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f.writelines("gpu_util: {} \n".format(
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str(dump_result["gpu_util"])))
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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 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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