diff --git a/benchmark/analysis.py b/benchmark/analysis.py deleted file mode 100644 index c4189b99d8ee082082a254718617a7e58bebe961..0000000000000000000000000000000000000000 --- a/benchmark/analysis.py +++ /dev/null @@ -1,273 +0,0 @@ -# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. -# -# 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. - -from __future__ import print_function - -import argparse -import json -import os -import re -import traceback - - -def parse_args(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--filename", type=str, help="The name of log which need to analysis.") - parser.add_argument( - "--log_with_profiler", type=str, help="The path of train log with profiler") - parser.add_argument( - "--profiler_path", type=str, help="The path of profiler timeline log.") - parser.add_argument( - "--keyword", type=str, help="Keyword to specify analysis data") - parser.add_argument( - "--separator", type=str, default=None, help="Separator of different field in log") - parser.add_argument( - '--position', type=int, default=None, help='The position of data field') - parser.add_argument( - '--range', type=str, default="", help='The range of data field to intercept') - parser.add_argument( - '--base_batch_size', type=int, help='base_batch size on gpu') - parser.add_argument( - '--skip_steps', type=int, default=0, help='The number of steps to be skipped') - parser.add_argument( - '--model_mode', type=int, default=-1, help='Analysis mode, default value is -1') - parser.add_argument( - '--ips_unit', type=str, default=None, help='IPS unit') - parser.add_argument( - '--model_name', type=str, default=0, help='training model_name, transformer_base') - parser.add_argument( - '--mission_name', type=str, default=0, help='training mission name') - parser.add_argument( - '--direction_id', type=int, default=0, help='training direction_id') - parser.add_argument( - '--run_mode', type=str, default="sp", help='multi process or single process') - parser.add_argument( - '--index', type=int, default=1, help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}') - parser.add_argument( - '--gpu_num', type=int, default=1, help='nums of training gpus') - args = parser.parse_args() - args.separator = None if args.separator == "None" else args.separator - return args - - -def _is_number(num): - pattern = re.compile(r'^[-+]?[-0-9]\d*\.\d*|[-+]?\.?[0-9]\d*$') - result = pattern.match(num) - if result: - return True - else: - return False - - -class TimeAnalyzer(object): - def __init__(self, filename, keyword=None, separator=None, position=None, range="-1"): - if filename is None: - raise Exception("Please specify the filename!") - - if keyword is None: - raise Exception("Please specify the keyword!") - - self.filename = filename - self.keyword = keyword - self.separator = separator - self.position = position - self.range = range - self.records = None - self._distil() - - def _distil(self): - self.records = [] - with open(self.filename, "r") as f_object: - lines = f_object.readlines() - for line in lines: - if self.keyword not in line: - continue - try: - result = None - - # Distil the string from a line. - line = line.strip() - line_words = line.split(self.separator) if self.separator else line.split() - if args.position: - result = line_words[self.position] - else: - # Distil the string following the keyword. - for i in range(len(line_words) - 1): - if line_words[i] == self.keyword: - result = line_words[i + 1] - break - - # Distil the result from the picked string. - if not self.range: - result = result[0:] - elif _is_number(self.range): - result = result[0: int(self.range)] - else: - result = result[int(self.range.split(":")[0]): int(self.range.split(":")[1])] - self.records.append(float(result)) - except Exception as exc: - print("line is: {}; separator={}; position={}".format(line, self.separator, self.position)) - - print("Extract {} records: separator={}; position={}".format(len(self.records), self.separator, self.position)) - - def _get_fps(self, mode, batch_size, gpu_num, avg_of_records, run_mode, unit=None): - if mode == -1 and run_mode == 'sp': - assert unit, "Please set the unit when mode is -1." - fps = gpu_num * avg_of_records - elif mode == -1 and run_mode == 'mp': - assert unit, "Please set the unit when mode is -1." - fps = gpu_num * avg_of_records #temporarily, not used now - print("------------this is mp") - elif mode == 0: - # s/step -> samples/s - fps = (batch_size * gpu_num) / avg_of_records - unit = "samples/s" - elif mode == 1: - # steps/s -> steps/s - fps = avg_of_records - unit = "steps/s" - elif mode == 2: - # s/step -> steps/s - fps = 1 / avg_of_records - unit = "steps/s" - elif mode == 3: - # steps/s -> samples/s - fps = batch_size * gpu_num * avg_of_records - unit = "samples/s" - elif mode == 4: - # s/epoch -> s/epoch - fps = avg_of_records - unit = "s/epoch" - else: - ValueError("Unsupported analysis mode.") - - return fps, unit - - def analysis(self, batch_size, gpu_num=1, skip_steps=0, mode=-1, run_mode='sp', unit=None): - if batch_size <= 0: - print("base_batch_size should larger than 0.") - return 0, '' - - if len(self.records) <= skip_steps: # to address the condition which item of log equals to skip_steps - print("no records") - return 0, '' - - sum_of_records = 0 - sum_of_records_skipped = 0 - skip_min = self.records[skip_steps] - skip_max = self.records[skip_steps] - - count = len(self.records) - for i in range(count): - sum_of_records += self.records[i] - if i >= skip_steps: - sum_of_records_skipped += self.records[i] - if self.records[i] < skip_min: - skip_min = self.records[i] - if self.records[i] > skip_max: - skip_max = self.records[i] - - avg_of_records = sum_of_records / float(count) - avg_of_records_skipped = sum_of_records_skipped / float(count - skip_steps) - - fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records, run_mode, unit) - fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num, avg_of_records_skipped, run_mode, unit) - if mode == -1: - print("average ips of %d steps, skip 0 step:" % count) - print("\tAvg: %.3f %s" % (avg_of_records, fps_unit)) - print("\tFPS: %.3f %s" % (fps, fps_unit)) - if skip_steps > 0: - print("average ips of %d steps, skip %d steps:" % (count, skip_steps)) - print("\tAvg: %.3f %s" % (avg_of_records_skipped, fps_unit)) - print("\tMin: %.3f %s" % (skip_min, fps_unit)) - print("\tMax: %.3f %s" % (skip_max, fps_unit)) - print("\tFPS: %.3f %s" % (fps_skipped, fps_unit)) - elif mode == 1 or mode == 3: - print("average latency of %d steps, skip 0 step:" % count) - print("\tAvg: %.3f steps/s" % avg_of_records) - print("\tFPS: %.3f %s" % (fps, fps_unit)) - if skip_steps > 0: - print("average latency of %d steps, skip %d steps:" % (count, skip_steps)) - print("\tAvg: %.3f steps/s" % avg_of_records_skipped) - print("\tMin: %.3f steps/s" % skip_min) - print("\tMax: %.3f steps/s" % skip_max) - print("\tFPS: %.3f %s" % (fps_skipped, fps_unit)) - elif mode == 0 or mode == 2: - print("average latency of %d steps, skip 0 step:" % count) - print("\tAvg: %.3f s/step" % avg_of_records) - print("\tFPS: %.3f %s" % (fps, fps_unit)) - if skip_steps > 0: - print("average latency of %d steps, skip %d steps:" % (count, skip_steps)) - print("\tAvg: %.3f s/step" % avg_of_records_skipped) - print("\tMin: %.3f s/step" % skip_min) - print("\tMax: %.3f s/step" % skip_max) - print("\tFPS: %.3f %s" % (fps_skipped, fps_unit)) - - return round(fps_skipped, 3), fps_unit - - -if __name__ == "__main__": - args = parse_args() - run_info = dict() - run_info["log_file"] = args.filename - run_info["model_name"] = args.model_name - run_info["mission_name"] = args.mission_name - run_info["direction_id"] = args.direction_id - run_info["run_mode"] = args.run_mode - run_info["index"] = args.index - run_info["gpu_num"] = args.gpu_num - run_info["FINAL_RESULT"] = 0 - run_info["JOB_FAIL_FLAG"] = 0 - - try: - if args.index == 1: - if args.gpu_num == 1: - run_info["log_with_profiler"] = args.log_with_profiler - run_info["profiler_path"] = args.profiler_path - analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator, args.position, args.range) - run_info["FINAL_RESULT"], run_info["UNIT"] = analyzer.analysis( - batch_size=args.base_batch_size, - gpu_num=args.gpu_num, - skip_steps=args.skip_steps, - mode=args.model_mode, - run_mode=args.run_mode, - unit=args.ips_unit) - try: - if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0: - run_info["JOB_FAIL_FLAG"] = 1 - except: - pass - elif args.index == 3: - run_info["FINAL_RESULT"] = {} - records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead', None, 3, '').records - records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead', None, 5).records - records_ct_total = TimeAnalyzer(args.filename, 'Computation time', None, 3, '').records - records_gm_total = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 4, '').records - records_gm_ratio = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 6).records - records_gmas_total = TimeAnalyzer(args.filename, 'GpuMemcpyAsync Calls', None, 4, '').records - records_gms_total = TimeAnalyzer(args.filename, 'GpuMemcpySync Calls', None, 4, '').records - run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[0] if records_fo_total else 0 - run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[0] if records_fo_ratio else 0 - run_info["FINAL_RESULT"]["ComputationTime_Total"] = records_ct_total[0] if records_ct_total else 0 - run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[0] if records_gm_total else 0 - run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[0] if records_gm_ratio else 0 - run_info["FINAL_RESULT"]["GpuMemcpyAsync_Total"] = records_gmas_total[0] if records_gmas_total else 0 - run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[0] if records_gms_total else 0 - else: - print("Not support!") - except Exception: - traceback.print_exc() - print("{}".format(json.dumps(run_info))) # it's required, for the log file path insert to the database - diff --git a/benchmark/readme.md b/benchmark/readme.md deleted file mode 100644 index d90d21468e7c9d0c9068a273ae704c0c8a086eab..0000000000000000000000000000000000000000 --- a/benchmark/readme.md +++ /dev/null @@ -1,30 +0,0 @@ - -# PaddleOCR DB/EAST/PSE 算法训练benchmark测试 - -PaddleOCR/benchmark目录下的文件用于获取并分析训练日志。 -训练采用icdar2015数据集，包括1000张训练图像和500张测试图像。模型配置采用resnet18_vd作为backbone，分别训练batch_size=8和batch_size=16的情况。 - -## 运行训练benchmark - -benchmark/run_det.sh 中包含了三个过程： -- 安装依赖 -- 下载数据 -- 执行训练 -- 日志分析获取IPS - -在执行训练部分，会执行单机单卡（默认0号卡）单机多卡训练，并分别执行batch_size=8和batch_size=16的情况。所以执行完后，每种模型会得到4个日志文件。 - -run_det.sh 执行方式如下: - -``` -# cd PaddleOCR/ -bash benchmark/run_det.sh -``` - -以DB为例，将得到四个日志文件，如下： -``` -det_res18_db_v2.0_sp_bs16_fp32_1 -det_res18_db_v2.0_sp_bs8_fp32_1 -det_res18_db_v2.0_mp_bs16_fp32_1 -det_res18_db_v2.0_mp_bs8_fp32_1 -``` diff --git a/benchmark/run_benchmark_det.sh b/benchmark/run_benchmark_det.sh deleted file mode 100644 index 54263e953f3f758b318df147d34ee942a247ed18..0000000000000000000000000000000000000000 --- a/benchmark/run_benchmark_det.sh +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env bash -set -xe -# 运行示例：CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode} -# 参数说明 -function _set_params(){ - run_mode=${1:-"sp"} # 单卡sp|多卡mp - batch_size=${2:-"64"} - fp_item=${3:-"fp32"} # fp32|fp16 - max_epoch=${4:-"10"} # 可选，如果需要修改代码提前中断 - model_item=${5:-"model_item"} - run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数 -# 日志解析所需参数 - base_batch_size=${batch_size} - mission_name="OCR" - direction_id="0" - ips_unit="images/sec" - skip_steps=2 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填) - keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填) - index="1" - model_name=${model_item}_bs${batch_size}_${fp_item} # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示 -# 以下不用修改 - device=${CUDA_VISIBLE_DEVICES//,/ } - arr=(${device}) - num_gpu_devices=${#arr[*]} - log_file=${run_log_path}/${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices} -} -function _train(){ - echo "Train on ${num_gpu_devices} GPUs" - echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size" - - train_cmd="-c configs/det/${model_item}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_epoch} Global.eval_batch_step=[0,20000] Global.print_batch_step=2" - case ${run_mode} in - sp) - train_cmd="python tools/train.py "${train_cmd}"" - ;; - mp) - train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}" - ;; - *) echo "choose run_mode(sp or mp)"; exit 1; - esac -# 以下不用修改 - timeout 15m ${train_cmd} > ${log_file} 2>&1 - if [ $? -ne 0 ];then - echo -e "${model_name}, FAIL" - export job_fail_flag=1 - else - echo -e "${model_name}, SUCCESS" - export job_fail_flag=0 - fi - - if [ $run_mode = "mp" -a -d mylog ]; then - rm ${log_file} - cp mylog/workerlog.0 ${log_file} - fi -} - -source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开 -_set_params $@ -#_train # 如果只想产出训练log,不解析,可取消注释 -_run # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开 diff --git a/benchmark/run_det.sh b/benchmark/run_det.sh deleted file mode 100644 index be0c141f7ee168d10eebb6efb57158d18ed02f72..0000000000000000000000000000000000000000 --- a/benchmark/run_det.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash -# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37 -# 执行目录: ./PaddleOCR -# 1 安装该模型需要的依赖 (如需开启优化策略请注明) -log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} -python -m pip install -r requirements.txt -# 2 拷贝该模型需要数据、预训练模型 -wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../ -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams -wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams -# 3 批量运行（如不方便批量，1，2需放到单个模型中） - -model_mode_list=(det_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse) -fp_item_list=(fp32) -for model_mode in ${model_mode_list[@]}; do - for fp_item in ${fp_item_list[@]}; do - if [ ${model_mode} == "det_r50_vd_east" ]; then - bs_list=(16) - else - bs_list=(8 16) - fi - for bs_item in ${bs_list[@]}; do - echo "index is speed, 1gpus, begin, ${model_name}" - run_mode=sp - log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode} - CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (5min) - sleep 60 - echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}" - run_mode=mp - log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode} - CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1 - sleep 60 - done - done -done - -