# Copyright (c) 2022 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 fastdeploy as fd import cv2 import os import numpy as np import time def parse_arguments(): import argparse import ast parser = argparse.ArgumentParser() parser.add_argument( "--model", required=True, help="Path of PaddleSeg model.") parser.add_argument( "--image", type=str, required=False, help="Path of test image file.") parser.add_argument( "--cpu_num_thread", type=int, default=8, help="default number of cpu thread.") parser.add_argument( "--device_id", type=int, default=0, help="device(gpu) id") parser.add_argument( "--iter_num", required=True, type=int, default=300, help="number of iterations for computing performace.") parser.add_argument( "--device", default="cpu", help="Type of inference device, support 'cpu' or 'gpu'.") parser.add_argument( "--backend", type=str, default="default", help="inference backend, default, ort, ov, trt, paddle, paddle_trt.") parser.add_argument( "--enable_trt_fp16", type=ast.literal_eval, default=False, help="whether enable fp16 in trt backend") parser.add_argument( "--enable_collect_memory_info", type=ast.literal_eval, default=False, help="whether enable collect memory info") args = parser.parse_args() return args def build_option(args): option = fd.RuntimeOption() device = args.device backend = args.backend enable_trt_fp16 = args.enable_trt_fp16 option.set_cpu_thread_num(args.cpu_num_thread) if device == "gpu": option.use_gpu() if backend == "ort": option.use_ort_backend() elif backend == "paddle": option.use_paddle_backend() elif backend in ["trt", "paddle_trt"]: option.use_trt_backend() if backend == "paddle_trt": option.enable_paddle_to_trt() if enable_trt_fp16: option.enable_trt_fp16() elif backend == "default": return option else: raise Exception( "While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.". format(backend)) elif device == "cpu": if backend == "ort": option.use_ort_backend() elif backend == "ov": option.use_openvino_backend() elif backend == "paddle": option.use_paddle_backend() elif backend == "default": return option else: raise Exception( "While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.". format(backend)) else: raise Exception( "Only support device CPU/GPU now, {} is not supported.".format( device)) return option def get_current_memory_mb(gpu_id=None): import pynvml import psutil pid = os.getpid() p = psutil.Process(pid) info = p.memory_full_info() cpu_mem = info.uss / 1024. / 1024. gpu_mem = 0 if gpu_id is not None: pynvml.nvmlInit() handle = pynvml.nvmlDeviceGetHandleByIndex(0) meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle) gpu_mem = meminfo.used / 1024. / 1024. return cpu_mem, gpu_mem def get_current_gputil(gpu_id): import GPUtil GPUs = GPUtil.getGPUs() gpu_load = GPUs[gpu_id].load return gpu_load if __name__ == '__main__': args = parse_arguments() option = build_option(args) model_file = os.path.join(args.model, "model.pdmodel") params_file = os.path.join(args.model, "model.pdiparams") config_file = os.path.join(args.model, "deploy.yaml") gpu_id = args.device_id enable_collect_memory_info = args.enable_collect_memory_info end2end_statis = list() cpu_mem = list() gpu_mem = list() gpu_util = list() if args.device == "cpu": file_path = args.model + "_model_" + args.backend + "_" + \ args.device + "_" + str(args.cpu_num_thread) + ".txt" else: if args.enable_trt_fp16: file_path = args.model + "_model_" + args.backend + "_fp16_" + args.device + ".txt" else: file_path = args.model + "_model_" + args.backend + "_" + args.device + ".txt" f = open(file_path, "w") f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4])) try: model = fd.vision.segmentation.PaddleSegModel( model_file, params_file, config_file, runtime_option=option) model.enable_record_time_of_runtime() im_ori = cv2.imread(args.image) for i in range(args.iter_num): im = im_ori start = time.time() result = model.predict(im) end2end_statis.append(time.time() - start) if enable_collect_memory_info: gpu_util.append(get_current_gputil(gpu_id)) cm, gm = get_current_memory_mb(gpu_id) cpu_mem.append(cm) gpu_mem.append(gm) runtime_statis = model.print_statis_info_of_runtime() warmup_iter = args.iter_num // 5 end2end_statis_repeat = end2end_statis[warmup_iter:] if enable_collect_memory_info: cpu_mem_repeat = cpu_mem[warmup_iter:] gpu_mem_repeat = gpu_mem[warmup_iter:] gpu_util_repeat = gpu_util[warmup_iter:] dump_result = dict() dump_result["runtime"] = runtime_statis["avg_time"] * 1000 dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000 if enable_collect_memory_info: dump_result["cpu_rss_mb"] = np.mean(cpu_mem_repeat) dump_result["gpu_rss_mb"] = np.mean(gpu_mem_repeat) dump_result["gpu_util"] = np.mean(gpu_util_repeat) f.writelines("Runtime(ms): {} \n".format(str(dump_result["runtime"]))) f.writelines("End2End(ms): {} \n".format(str(dump_result["end2end"]))) if enable_collect_memory_info: f.writelines("cpu_rss_mb: {} \n".format( str(dump_result["cpu_rss_mb"]))) f.writelines("gpu_rss_mb: {} \n".format( str(dump_result["gpu_rss_mb"]))) f.writelines("gpu_util: {} \n".format( str(dump_result["gpu_util"]))) except: f.writelines("!!!!!Infer Failed\n") f.close()