76 lines
2.9 KiB
Python
76 lines
2.9 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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from __future__ import absolute_import, division, print_function
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import datetime
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from ppcls.utils import logger
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from ppcls.utils.misc import AverageMeter
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def update_metric(trainer, out, batch, batch_size):
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# calc metric
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if trainer.train_metric_func is not None:
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metric_dict = trainer.train_metric_func(out, batch[-1])
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for key in metric_dict:
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if key not in trainer.output_info:
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trainer.output_info[key] = AverageMeter(key, '7.5f')
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trainer.output_info[key].update(
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float(metric_dict[key]), batch_size)
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def update_loss(trainer, loss_dict, batch_size):
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# update_output_info
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for key in loss_dict:
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if key not in trainer.output_info:
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trainer.output_info[key] = AverageMeter(key, '7.5f')
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trainer.output_info[key].update(float(loss_dict[key]), batch_size)
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def log_info(trainer, batch_size, epoch_id, iter_id):
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lr_msg = ", ".join([
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"lr({}): {:.8f}".format(lr.__class__.__name__, lr.get_lr())
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for i, lr in enumerate(trainer.lr_sch)
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])
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, trainer.output_info[key].avg)
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for key in trainer.output_info
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])
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time_msg = "s, ".join([
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"{}: {:.5f}".format(key, trainer.time_info[key].avg)
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for key in trainer.time_info
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])
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ips_msg = "ips: {:.5f} samples/s".format(
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batch_size / trainer.time_info["batch_cost"].avg)
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eta_sec = (
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(trainer.config["Global"]["epochs"] - epoch_id + 1
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) * trainer.max_iter - iter_id) * trainer.time_info["batch_cost"].avg
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eta_msg = "eta: {:s}".format(str(datetime.timedelta(seconds=int(eta_sec))))
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logger.info("[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".format(
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epoch_id, trainer.config["Global"]["epochs"], iter_id,
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trainer.max_iter, lr_msg, metric_msg, time_msg, ips_msg, eta_msg))
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for i, lr in enumerate(trainer.lr_sch):
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logger.scaler(
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name="lr({})".format(lr.__class__.__name__),
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value=lr.get_lr(),
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step=trainer.global_step,
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writer=trainer.vdl_writer)
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for key in trainer.output_info:
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logger.scaler(
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name="train_{}".format(key),
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value=trainer.output_info[key].avg,
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step=trainer.global_step,
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writer=trainer.vdl_writer)
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