mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-03 03:09:16 +00:00
Merge pull request #5536 from LDOUBLEV/dygraph
[benchmark] fix pretrain model download
This commit is contained in:
Double_V
GitHub
commit
9c0a4d9d30
@ -26,35 +26,57 @@ def parse_args():
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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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"--log_with_profiler",
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type=str,
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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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"--separator",
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type=str,
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default=None,
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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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'--range',
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type=str,
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default="",
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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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'--skip_steps',
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type=int,
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default=0,
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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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'--model_mode',
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type=int,
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default=-1,
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help='Analysis mode, default value is -1')
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parser.add_argument('--ips_unit', type=str, default=None, help='IPS unit')
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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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'--model_name',
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type=str,
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default=0,
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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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'--run_mode',
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type=str,
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default="sp",
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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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'--index',
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type=int,
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default=1,
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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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@ -72,7 +94,12 @@ def _is_number(num):
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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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def __init__(self,
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filename,
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keyword=None,
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separator=None,
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position=None,
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range="-1"):
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if filename is None:
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raise Exception("Please specify the filename!")
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@ -99,7 +126,8 @@ class TimeAnalyzer(object):
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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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line_words = line.split(
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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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@ -108,27 +136,36 @@ class TimeAnalyzer(object):
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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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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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result = result[int(self.range.split(":")[0]):int(
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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("line is: {}; separator={}; position={}".format(
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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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print("Extract {} records: separator={}; position={}".format(
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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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def _get_fps(self,
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mode,
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batch_size,
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gpu_num,
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avg_of_records,
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run_mode,
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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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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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@ -155,12 +192,20 @@ class TimeAnalyzer(object):
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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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def analysis(self,
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batch_size,
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gpu_num=1,
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skip_steps=0,
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mode=-1,
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run_mode='sp',
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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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if len(
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self.records
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) <= 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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@ -180,16 +225,20 @@ class TimeAnalyzer(object):
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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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avg_of_records_skipped = sum_of_records_skipped / float(count -
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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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fps, fps_unit = self._get_fps(mode, batch_size, gpu_num, avg_of_records,
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run_mode, unit)
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fps_skipped, _ = self._get_fps(mode, batch_size, gpu_num,
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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("average ips of %d steps, skip %d steps:" %
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(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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@ -199,7 +248,8 @@ class TimeAnalyzer(object):
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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("average latency of %d steps, skip %d steps:" %
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(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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@ -209,7 +259,8 @@ class TimeAnalyzer(object):
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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("average latency of %d steps, skip %d steps:" %
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(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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@ -236,7 +287,8 @@ if __name__ == "__main__":
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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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analyzer = TimeAnalyzer(args.filename, args.keyword, args.separator,
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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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@ -245,29 +297,50 @@ if __name__ == "__main__":
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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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if int(os.getenv('job_fail_flag')) == 1 or int(run_info[
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"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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records_fo_total = TimeAnalyzer(args.filename, 'Framework overhead',
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None, 3, '').records
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records_fo_ratio = TimeAnalyzer(args.filename, 'Framework overhead',
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None, 5).records
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records_ct_total = TimeAnalyzer(args.filename, 'Computation time',
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None, 3, '').records
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records_gm_total = TimeAnalyzer(args.filename,
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'GpuMemcpy Calls',
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None, 4, '').records
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records_gm_ratio = TimeAnalyzer(args.filename,
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'GpuMemcpy Calls',
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None, 6).records
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records_gmas_total = TimeAnalyzer(args.filename,
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'GpuMemcpyAsync Calls',
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None, 4, '').records
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records_gms_total = TimeAnalyzer(args.filename,
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'GpuMemcpySync Calls',
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None, 4, '').records
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run_info["FINAL_RESULT"]["Framework_Total"] = records_fo_total[
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0] if records_fo_total else 0
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run_info["FINAL_RESULT"]["Framework_Ratio"] = records_fo_ratio[
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0] if records_fo_ratio else 0
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run_info["FINAL_RESULT"][
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"ComputationTime_Total"] = records_ct_total[
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0] if records_ct_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpy_Total"] = records_gm_total[
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0] if records_gm_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpy_Ratio"] = records_gm_ratio[
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0] if records_gm_ratio else 0
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run_info["FINAL_RESULT"][
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"GpuMemcpyAsync_Total"] = records_gmas_total[
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0] if records_gmas_total else 0
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run_info["FINAL_RESULT"]["GpuMemcpySync_Total"] = records_gms_total[
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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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traceback.print_exc()
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print("{}".format(json.dumps(run_info))
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) # it's required, for the log file path insert to the database
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@ -58,3 +58,4 @@ source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合
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_set_params $@
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#_train # 如果只想产出训练log,不解析,可取消注释
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_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
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@ -36,3 +36,4 @@ for model_mode in ${model_mode_list[@]}; do
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done
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@ -3,8 +3,6 @@ source test_tipc/common_func.sh
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# set env
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python=python
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export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3`
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export model_commit=$(git log|head -n1|awk '{print $2}')
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export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
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export frame_version=${str_tmp%%.post*}
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export frame_commit=$(echo `${python} -c "import paddle;print(paddle.version.commit)"`)
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@ -24,7 +24,17 @@ if [ ${MODE} = "benchmark_train" ];then
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pip install -r requirements.txt
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if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then
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rm -rf ./train_data/icdar2015
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
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cd ./train_data/ && tar xf icdar2015.tar && cd ../
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fi
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if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
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cd ./train_data/ && tar xf icdar2015.tar && cd ../
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fi
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if [[ ${model_name} =~ "det_r18_db_v2_0" ]];then
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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams --no-check-certificate
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
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cd ./train_data/ && tar xf icdar2015.tar && cd ../
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fi
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