add benchmark
This commit is contained in:
LDOUBLEV
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d2ab194db6
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# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import print_function
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import argparse
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import json
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import os
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import re
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import traceback
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def parse_args():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--filename", type=str, help="The name of log which need to analysis.")
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parser.add_argument(
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"--log_with_profiler", type=str, help="The path of train log with profiler")
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parser.add_argument(
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"--profiler_path", type=str, help="The path of profiler timeline log.")
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parser.add_argument(
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"--keyword", type=str, help="Keyword to specify analysis data")
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parser.add_argument(
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"--separator", type=str, default=None, help="Separator of different field in log")
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parser.add_argument(
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'--position', type=int, default=None, help='The position of data field')
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parser.add_argument(
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'--range', type=str, default="", help='The range of data field to intercept')
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parser.add_argument(
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'--base_batch_size', type=int, help='base_batch size on gpu')
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parser.add_argument(
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'--skip_steps', type=int, default=0, help='The number of steps to be skipped')
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parser.add_argument(
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'--model_mode', type=int, default=-1, help='Analysis mode, default value is -1')
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parser.add_argument(
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'--ips_unit', type=str, default=None, help='IPS unit')
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parser.add_argument(
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'--model_name', type=str, default=0, help='training model_name, transformer_base')
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parser.add_argument(
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'--mission_name', type=str, default=0, help='training mission name')
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parser.add_argument(
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'--direction_id', type=int, default=0, help='training direction_id')
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parser.add_argument(
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'--run_mode', type=str, default="sp", help='multi process or single process')
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parser.add_argument(
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'--index', type=int, default=1, help='{1: speed, 2:mem, 3:profiler, 6:max_batch_size}')
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parser.add_argument(
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'--gpu_num', type=int, default=1, help='nums of training gpus')
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args = parser.parse_args()
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args.separator = None if args.separator == "None" else args.separator
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return args
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def _is_number(num):
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pattern = re.compile(r'^[-+]?[-0-9]\d*\.\d*|[-+]?\.?[0-9]\d*$')
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result = pattern.match(num)
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if result:
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return True
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else:
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return False
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class TimeAnalyzer(object):
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def __init__(self, filename, keyword=None, separator=None, position=None, range="-1"):
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if filename is None:
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raise Exception("Please specify the filename!")
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if keyword is None:
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raise Exception("Please specify the keyword!")
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self.filename = filename
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self.keyword = keyword
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self.separator = separator
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self.position = position
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self.range = range
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self.records = None
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self._distil()
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def _distil(self):
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self.records = []
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with open(self.filename, "r") as f_object:
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lines = f_object.readlines()
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for line in lines:
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if self.keyword not in line:
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continue
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try:
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result = None
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# Distil the string from a line.
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line = line.strip()
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line_words = line.split(self.separator) if self.separator else line.split()
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if args.position:
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result = line_words[self.position]
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else:
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# Distil the string following the keyword.
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for i in range(len(line_words) - 1):
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if line_words[i] == self.keyword:
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result = line_words[i + 1]
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break
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# Distil the result from the picked string.
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if not self.range:
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result = result[0:]
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elif _is_number(self.range):
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result = result[0: int(self.range)]
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else:
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result = result[int(self.range.split(":")[0]): int(self.range.split(":")[1])]
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self.records.append(float(result))
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except Exception as exc:
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print("line is: {}; separator={}; position={}".format(line, self.separator, self.position))
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print("Extract {} records: separator={}; position={}".format(len(self.records), self.separator, self.position))
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def _get_fps(self, mode, batch_size, gpu_num, avg_of_records, run_mode, unit=None):
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if mode == -1 and run_mode == 'sp':
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assert unit, "Please set the unit when mode is -1."
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fps = gpu_num * avg_of_records
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elif mode == -1 and run_mode == 'mp':
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assert unit, "Please set the unit when mode is -1."
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fps = gpu_num * avg_of_records #temporarily, not used now
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print("------------this is mp")
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elif mode == 0:
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# s/step -> samples/s
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fps = (batch_size * gpu_num) / avg_of_records
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unit = "samples/s"
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elif mode == 1:
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# steps/s -> steps/s
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fps = avg_of_records
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unit = "steps/s"
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elif mode == 2:
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# s/step -> steps/s
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fps = 1 / avg_of_records
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unit = "steps/s"
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elif mode == 3:
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# steps/s -> samples/s
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fps = batch_size * gpu_num * avg_of_records
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unit = "samples/s"
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elif mode == 4:
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# s/epoch -> s/epoch
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fps = avg_of_records
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unit = "s/epoch"
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else:
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ValueError("Unsupported analysis mode.")
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return fps, unit
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def analysis(self, batch_size, gpu_num=1, skip_steps=0, mode=-1, run_mode='sp', unit=None):
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if batch_size <= 0:
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print("base_batch_size should larger than 0.")
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return 0, ''
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if len(self.records) <= skip_steps: # to address the condition which item of log equals to skip_steps
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print("no records")
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return 0, ''
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sum_of_records = 0
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sum_of_records_skipped = 0
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skip_min = self.records[skip_steps]
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skip_max = self.records[skip_steps]
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count = len(self.records)
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for i in range(count):
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sum_of_records += self.records[i]
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if i >= skip_steps:
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sum_of_records_skipped += self.records[i]
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if self.records[i] < skip_min:
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skip_min = self.records[i]
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if self.records[i] > skip_max:
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skip_max = self.records[i]
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avg_of_records = sum_of_records / float(count)
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avg_of_records_skipped = sum_of_records_skipped / float(count - skip_steps)
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fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records, run_mode, unit)
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fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num, avg_of_records_skipped, run_mode, unit)
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if mode == -1:
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print("average ips of %d steps, skip 0 step:" % count)
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print("\tAvg: %.3f %s" % (avg_of_records, fps_unit))
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print("\tFPS: %.3f %s" % (fps, fps_unit))
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if skip_steps > 0:
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print("average ips of %d steps, skip %d steps:" % (count, skip_steps))
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print("\tAvg: %.3f %s" % (avg_of_records_skipped, fps_unit))
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print("\tMin: %.3f %s" % (skip_min, fps_unit))
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print("\tMax: %.3f %s" % (skip_max, fps_unit))
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print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
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elif mode == 1 or mode == 3:
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print("average latency of %d steps, skip 0 step:" % count)
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print("\tAvg: %.3f steps/s" % avg_of_records)
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print("\tFPS: %.3f %s" % (fps, fps_unit))
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if skip_steps > 0:
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print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
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print("\tAvg: %.3f steps/s" % avg_of_records_skipped)
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print("\tMin: %.3f steps/s" % skip_min)
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print("\tMax: %.3f steps/s" % skip_max)
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print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
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elif mode == 0 or mode == 2:
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print("average latency of %d steps, skip 0 step:" % count)
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print("\tAvg: %.3f s/step" % avg_of_records)
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print("\tFPS: %.3f %s" % (fps, fps_unit))
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if skip_steps > 0:
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print("average latency of %d steps, skip %d steps:" % (count, skip_steps))
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print("\tAvg: %.3f s/step" % avg_of_records_skipped)
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print("\tMin: %.3f s/step" % skip_min)
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print("\tMax: %.3f s/step" % skip_max)
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print("\tFPS: %.3f %s" % (fps_skipped, fps_unit))
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return round(fps_skipped, 3), fps_unit
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if __name__ == "__main__":
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args = parse_args()
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run_info = dict()
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run_info["log_file"] = args.filename
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run_info["model_name"] = args.model_name
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run_info["mission_name"] = args.mission_name
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run_info["direction_id"] = args.direction_id
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run_info["run_mode"] = args.run_mode
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run_info["index"] = args.index
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run_info["gpu_num"] = args.gpu_num
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run_info["FINAL_RESULT"] = 0
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run_info["JOB_FAIL_FLAG"] = 0
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try:
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if args.index == 1:
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if args.gpu_num == 1:
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run_info["log_with_profiler"] = args.log_with_profiler
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run_info["profiler_path"] = args.profiler_path
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analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator, args.position, args.range)
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run_info["FINAL_RESULT"], run_info["UNIT"] = analyzer.analysis(
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batch_size=args.base_batch_size,
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gpu_num=args.gpu_num,
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skip_steps=args.skip_steps,
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mode=args.model_mode,
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run_mode=args.run_mode,
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unit=args.ips_unit)
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try:
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if int(os.getenv('job_fail_flag')) == 1 or int(run_info["FINAL_RESULT"]) == 0:
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run_info["JOB_FAIL_FLAG"] = 1
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except:
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pass
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elif args.index == 3:
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run_info["FINAL_RESULT"] = {}
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records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead', None, 3, '').records
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records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead', None, 5).records
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records_ct_total = TimeAnalyzer(args.filename, 'Computation time', None, 3, '').records
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records_gm_total = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 4, '').records
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records_gm_ratio = TimeAnalyzer(args.filename, 'GpuMemcpy Calls', None, 6).records
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records_gmas_total = TimeAnalyzer(args.filename, 'GpuMemcpyAsync Calls', None, 4, '').records
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records_gms_total = TimeAnalyzer(args.filename, 'GpuMemcpySync Calls', None, 4, '').records
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run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[0] if records_fo_total else 0
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run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[0] if records_fo_ratio else 0
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run_info["FINAL_RESULT"]["ComputationTime_Total"] = records_ct_total[0] if records_ct_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[0] if records_gm_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[0] if records_gm_ratio else 0
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run_info["FINAL_RESULT"]["GpuMemcpyAsync_Total"] = records_gmas_total[0] if records_gmas_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[0] if records_gms_total else 0
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else:
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print("Not support!")
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except Exception:
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traceback.print_exc()
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print("{}".format(json.dumps(run_info))) # it's required, for the log file path insert to the database
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@ -20,9 +20,7 @@ function _train(){
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echo "Train on ${num_gpu_devices} GPUs"
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echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
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train_cmd="-c configs/det/${model_name}.yml
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-o Train.loader.batch_size_per_card=${batch_size}
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-o Global.epoch_num=${max_iter} "
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train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} "
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case ${run_mode} in
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sp)
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train_cmd="python3.7 tools/train.py "${train_cmd}""
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@ -8,20 +8,20 @@ python3.7 -m pip install -r requirements.txt
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#wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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# 3 批量运行（如不方便批量，1，2需放到单个模型中）
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model_mode_list=(det_mv3_db det_r50_vd_east)
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model_mode_list=(ch_ppocr_v2.0/ch_det_res18_db_v2.0 det_r50_vd_east)
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fp_item_list=(fp32)
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bs_list=(4 8)
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bs_list=(8 16)
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for model_mode in ${model_mode_list[@]}; do
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for fp_item in ${fp_item_list[@]}; do
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for bs_item in ${bs_list[@]}; do
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echo "index is speed, 1gpus, begin, ${model_name}"
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run_mode=sp
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CUDA_VISIBLE_DEVICES=3 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
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CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
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sleep 60
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echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
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#run_mode=mp
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#CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
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#sleep 60
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run_mode=mp
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
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sleep 60
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done
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done
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done
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Global:
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use_gpu: true
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: ./output/ch_db_res18/
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save_epoch_step: 1200
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [3000, 2000]
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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img: doc/imgs_en/img_10.jpg
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save_res_path: ./output/det_db/predicts_db.txt
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Architecture:
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model_type: det
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algorithm: DB
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Transform:
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Backbone:
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name: ResNet
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layers: 18
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disable_se: True
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Neck:
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name: DBFPN
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out_channels: 256
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Head:
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name: DBHead
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k: 50
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Loss:
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name: DBLoss
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balance_loss: true
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main_loss_type: DiceLoss
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alpha: 5
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beta: 10
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ohem_ratio: 3
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Cosine
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learning_rate: 0.001
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warmup_epoch: 2
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regularizer:
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name: 'L2'
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factor: 0
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PostProcess:
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name: DBPostProcess
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thresh: 0.3
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box_thresh: 0.6
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max_candidates: 1000
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unclip_ratio: 1.5
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Metric:
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name: DetMetric
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main_indicator: hmean
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/icdar2015/text_localization/
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label_file_list:
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- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
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ratio_list: [1.0]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- DetLabelEncode: # Class handling label
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- IaaAugment:
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augmenter_args:
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- { 'type': Fliplr, 'args': { 'p': 0.5 } }
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- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
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- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
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- EastRandomCropData:
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size: [960, 960]
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max_tries: 50
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keep_ratio: true
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- MakeBorderMap:
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shrink_ratio: 0.4
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thresh_min: 0.3
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thresh_max: 0.7
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- MakeShrinkMap:
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shrink_ratio: 0.4
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min_text_size: 8
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||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: True
|
||||
drop_last: False
|
||||
batch_size_per_card: 8
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
# image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 2
|
||||
Loading…
Reference in New Issue