mirror of
https://github.com/deepinsight/insightface.git
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94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
# Copyright (c) 2021 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 logging
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import os
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import sys
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import time
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class AverageMeter(object):
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"""Computes and stores the average and current value
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"""
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def __init__(self):
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self.val = None
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self.avg = None
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self.sum = None
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self.count = None
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def init_logging(rank, models_root):
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if rank is 0:
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log_root = logging.getLogger()
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log_root.setLevel(logging.INFO)
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formatter = logging.Formatter("Training: %(asctime)s - %(message)s")
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handler_file = logging.FileHandler(
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os.path.join(models_root, "training.log"))
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handler_stream = logging.StreamHandler(sys.stdout)
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handler_file.setFormatter(formatter)
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handler_stream.setFormatter(formatter)
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log_root.addHandler(handler_file)
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log_root.addHandler(handler_stream)
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log_root.info('rank: %d' % rank)
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class CallBackLogging(object):
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def __init__(self,
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frequent,
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rank,
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world_size,
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total_step,
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batch_size,
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writer=None):
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self.frequent: int = frequent
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self.rank: int = rank
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self.world_size: int = world_size
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self.time_start = time.time()
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self.total_step: int = total_step
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self.batch_size: int = batch_size
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self.writer = writer
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self.tic = time.time()
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def __call__(self, global_step, loss: AverageMeter, epoch: int, lr_value, avg_reader_cost, avg_batch_cost, avg_samples, ips):
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if self.rank is 0 and global_step > 0 and global_step % self.frequent == 0:
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time_now = (time.time() - self.time_start) / 3600
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time_total = time_now / ((global_step + 1) / self.total_step)
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time_for_end = time_total - time_now
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if self.writer is not None:
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self.writer.add_scalar('time_for_end', time_for_end,
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global_step)
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self.writer.add_scalar('loss', loss.avg, global_step)
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# ips is throughput
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msg = "loss %.4f, lr: %f, epoch: %d, step: %d, eta: %1.2f hours, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f images/sec" % (
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loss.avg, lr_value, epoch, global_step, time_for_end,avg_reader_cost, avg_batch_cost, avg_samples, ips * self.world_size)
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logging.info(msg)
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loss.reset()
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self.tic = time.time()
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