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- # 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 paddlex.ppcls.engine.train.utils import update_loss, update_metric, log_info
- from paddlex.ppcls.utils import profiler
- def train_epoch(engine, epoch_id, print_batch_step):
- tic = time.time()
- for iter_id, batch in enumerate(engine.train_dataloader):
- if iter_id >= engine.max_iter:
- break
- 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)
- if engine.use_dali:
- batch = [
- paddle.to_tensor(batch[0]['data']),
- paddle.to_tensor(batch[0]['label'])
- ]
- 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:
- with paddle.amp.auto_cast(custom_black_list={
- "flatten_contiguous_range", "greater_than"
- }):
- out = forward(engine, batch)
- else:
- out = forward(engine, batch)
- loss_dict = engine.train_loss_func(out, batch[1])
- # step opt and lr
- if engine.amp:
- scaled = engine.scaler.scale(loss_dict["loss"])
- scaled.backward()
- engine.scaler.minimize(engine.optimizer, scaled)
- else:
- loss_dict["loss"].backward()
- engine.optimizer.step()
- engine.optimizer.clear_grad()
- engine.lr_sch.step()
- # 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()
- def forward(engine, batch):
- if not engine.is_rec:
- return engine.model(batch[0])
- else:
- return engine.model(batch[0], batch[1])
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