114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
# 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 time
|
|
import paddle
|
|
from ppcls.engine.train.utils import update_loss, update_metric, log_info, type_name
|
|
from ppcls.utils import profiler
|
|
|
|
|
|
def train_epoch(engine, epoch_id, print_batch_step):
|
|
tic = time.time()
|
|
|
|
if not hasattr(engine, "train_dataloader_iter"):
|
|
engine.train_dataloader_iter = iter(engine.train_dataloader)
|
|
|
|
for iter_id in range(engine.iter_per_epoch):
|
|
# fetch data batch from dataloader
|
|
try:
|
|
batch = next(engine.train_dataloader_iter)
|
|
except Exception:
|
|
# NOTE: reset DALI dataloader manually
|
|
if engine.use_dali:
|
|
engine.train_dataloader.reset()
|
|
engine.train_dataloader_iter = iter(engine.train_dataloader)
|
|
batch = next(engine.train_dataloader_iter)
|
|
|
|
profiler.add_profiler_step(engine.config["profiler_options"])
|
|
if iter_id == 5:
|
|
for key in engine.time_info:
|
|
engine.time_info[key].reset()
|
|
engine.time_info["reader_cost"].update(time.time() - tic)
|
|
|
|
batch_size = batch[0].shape[0]
|
|
if not engine.config["Global"].get("use_multilabel", False):
|
|
batch[1] = batch[1].reshape([batch_size, -1])
|
|
engine.global_step += 1
|
|
|
|
# image input
|
|
if engine.amp:
|
|
amp_level = engine.config["AMP"].get("level", "O1").upper()
|
|
with paddle.amp.auto_cast(
|
|
custom_black_list={
|
|
"flatten_contiguous_range", "greater_than"
|
|
},
|
|
level=amp_level):
|
|
out = forward(engine, batch)
|
|
loss_dict = engine.train_loss_func(out, batch[1])
|
|
else:
|
|
out = forward(engine, batch)
|
|
loss_dict = engine.train_loss_func(out, batch[1])
|
|
|
|
# loss
|
|
loss = loss_dict["loss"] / engine.update_freq
|
|
|
|
# backward & step opt
|
|
if engine.amp:
|
|
scaled = engine.scaler.scale(loss)
|
|
scaled.backward()
|
|
if (iter_id + 1) % engine.update_freq == 0:
|
|
for i in range(len(engine.optimizer)):
|
|
engine.scaler.minimize(engine.optimizer[i], scaled)
|
|
else:
|
|
loss.backward()
|
|
if (iter_id + 1) % engine.update_freq == 0:
|
|
for i in range(len(engine.optimizer)):
|
|
engine.optimizer[i].step()
|
|
|
|
if (iter_id + 1) % engine.update_freq == 0:
|
|
# clear grad
|
|
for i in range(len(engine.optimizer)):
|
|
engine.optimizer[i].clear_grad()
|
|
# step lr(by step)
|
|
for i in range(len(engine.lr_sch)):
|
|
if not getattr(engine.lr_sch[i], "by_epoch", False):
|
|
engine.lr_sch[i].step()
|
|
# update ema
|
|
if engine.ema:
|
|
engine.model_ema.update(engine.model)
|
|
|
|
# below code just for logging
|
|
# update metric_for_logger
|
|
update_metric(engine, out, batch, batch_size)
|
|
# update_loss_for_logger
|
|
update_loss(engine, loss_dict, batch_size)
|
|
engine.time_info["batch_cost"].update(time.time() - tic)
|
|
if iter_id % print_batch_step == 0:
|
|
log_info(engine, batch_size, epoch_id, iter_id)
|
|
tic = time.time()
|
|
|
|
# step lr(by epoch)
|
|
for i in range(len(engine.lr_sch)):
|
|
if getattr(engine.lr_sch[i], "by_epoch", False) and \
|
|
type_name(engine.lr_sch[i]) != "ReduceOnPlateau":
|
|
engine.lr_sch[i].step()
|
|
|
|
|
|
def forward(engine, batch):
|
|
if not engine.is_rec:
|
|
return engine.model(batch[0])
|
|
else:
|
|
return engine.model(batch[0], batch[1])
|