mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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205 lines
6.9 KiB
Python
Executable File
205 lines
6.9 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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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 PaddleSeg 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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"--iter_num",
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required=True,
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type=int,
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default=300,
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help="number of iterations for computing performace.")
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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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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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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 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.enable_paddle_to_trt()
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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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def get_current_memory_mb(gpu_id=None):
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import pynvml
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import psutil
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pid = os.getpid()
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p = psutil.Process(pid)
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info = p.memory_full_info()
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cpu_mem = info.uss / 1024. / 1024.
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gpu_mem = 0
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if gpu_id is not None:
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pynvml.nvmlInit()
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
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gpu_mem = meminfo.used / 1024. / 1024.
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return cpu_mem, gpu_mem
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def get_current_gputil(gpu_id):
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import GPUtil
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GPUs = GPUtil.getGPUs()
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gpu_load = GPUs[gpu_id].load
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return gpu_load
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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, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.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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end2end_statis = list()
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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_" + 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.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=option)
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model.enable_record_time_of_runtime()
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im_ori = cv2.imread(args.image)
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for i in range(args.iter_num):
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im = im_ori
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start = time.time()
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result = model.predict(im)
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end2end_statis.append(time.time() - start)
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if enable_collect_memory_info:
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gpu_util.append(get_current_gputil(gpu_id))
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cm, gm = get_current_memory_mb(gpu_id)
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cpu_mem.append(cm)
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gpu_mem.append(gm)
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runtime_statis = model.print_statis_info_of_runtime()
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warmup_iter = args.iter_num // 5
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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if enable_collect_memory_info:
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cpu_mem_repeat = cpu_mem[warmup_iter:]
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gpu_mem_repeat = gpu_mem[warmup_iter:]
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gpu_util_repeat = gpu_util[warmup_iter:]
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dump_result = dict()
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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if enable_collect_memory_info:
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dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat)
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dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat)
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dump_result["gpu_util"] = np.mean(gpu_util_repeat)
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f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"])))
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f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"])))
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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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except:
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f.writelines("!!!!!Infer Failed\n")
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f.close()
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