You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.8 KiB
71 lines
2.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. |
|
# |
|
# 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 absolute_import, division, print_function |
|
|
|
import datetime |
|
from ppcls.utils import logger |
|
from ppcls.utils.misc import AverageMeter |
|
|
|
|
|
def update_metric(trainer, out, batch, batch_size): |
|
# calc metric |
|
if trainer.train_metric_func is not None: |
|
metric_dict = trainer.train_metric_func(out, batch[-1]) |
|
for key in metric_dict: |
|
if key not in trainer.output_info: |
|
trainer.output_info[key] = AverageMeter(key, '7.5f') |
|
trainer.output_info[key].update(float(metric_dict[key]), batch_size) |
|
|
|
|
|
def update_loss(trainer, loss_dict, batch_size): |
|
# update_output_info |
|
for key in loss_dict: |
|
if key not in trainer.output_info: |
|
trainer.output_info[key] = AverageMeter(key, '7.5f') |
|
trainer.output_info[key].update(float(loss_dict[key]), batch_size) |
|
|
|
|
|
def log_info(trainer, batch_size, epoch_id, iter_id): |
|
lr_msg = "lr: {:.5f}".format(trainer.lr_sch.get_lr()) |
|
metric_msg = ", ".join([ |
|
"{}: {:.5f}".format(key, trainer.output_info[key].avg) |
|
for key in trainer.output_info |
|
]) |
|
time_msg = "s, ".join([ |
|
"{}: {:.5f}".format(key, trainer.time_info[key].avg) |
|
for key in trainer.time_info |
|
]) |
|
|
|
ips_msg = "ips: {:.5f} images/sec".format( |
|
batch_size / trainer.time_info["batch_cost"].avg) |
|
eta_sec = ((trainer.config["Global"]["epochs"] - epoch_id + 1 |
|
) * len(trainer.train_dataloader) - iter_id |
|
) * trainer.time_info["batch_cost"].avg |
|
eta_msg = "eta: {:s}".format(str(datetime.timedelta(seconds=int(eta_sec)))) |
|
logger.info("[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".format( |
|
epoch_id, trainer.config["Global"]["epochs"], iter_id, |
|
len(trainer.train_dataloader), lr_msg, metric_msg, time_msg, ips_msg, |
|
eta_msg)) |
|
|
|
logger.scaler( |
|
name="lr", |
|
value=trainer.lr_sch.get_lr(), |
|
step=trainer.global_step, |
|
writer=trainer.vdl_writer) |
|
for key in trainer.output_info: |
|
logger.scaler( |
|
name="train_{}".format(key), |
|
value=trainer.output_info[key].avg, |
|
step=trainer.global_step, |
|
writer=trainer.vdl_writer)
|
|
|