88 lines
3.1 KiB
Python
88 lines
3.1 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 time
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import paddle
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from ppcls.engine.train.utils import update_loss, update_metric, log_info
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from ppcls.utils import profiler
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def train_epoch(trainer, epoch_id, print_batch_step):
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tic = time.time()
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train_dataloader = trainer.train_dataloader if trainer.use_dali else trainer.train_dataloader(
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)
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for iter_id, batch in enumerate(train_dataloader):
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if iter_id >= trainer.max_iter:
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break
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profiler.add_profiler_step(trainer.config["profiler_options"])
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if iter_id == 5:
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for key in trainer.time_info:
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trainer.time_info[key].reset()
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trainer.time_info["reader_cost"].update(time.time() - tic)
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if trainer.use_dali:
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batch = [
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paddle.to_tensor(batch[0]['data']),
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paddle.to_tensor(batch[0]['label'])
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]
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batch_size = batch[0].shape[0]
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batch[1] = batch[1].reshape([-1, 1]).astype("int64")
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trainer.global_step += 1
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# image input
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if trainer.amp:
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with paddle.amp.auto_cast(custom_black_list={
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"flatten_contiguous_range", "greater_than"
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}):
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out = forward(trainer, batch)
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loss_dict = trainer.train_loss_func(out, batch[1])
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else:
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out = forward(trainer, batch)
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# calc loss
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if trainer.config["DataLoader"]["Train"]["dataset"].get(
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"batch_transform_ops", None):
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loss_dict = trainer.train_loss_func(out, batch[1:])
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else:
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loss_dict = trainer.train_loss_func(out, batch[1])
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# step opt and lr
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if trainer.amp:
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scaled = trainer.scaler.scale(loss_dict["loss"])
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scaled.backward()
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trainer.scaler.minimize(trainer.optimizer, scaled)
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else:
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loss_dict["loss"].backward()
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trainer.optimizer.step()
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trainer.optimizer.clear_grad()
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trainer.lr_sch.step()
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# below code just for logging
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# update metric_for_logger
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update_metric(trainer, out, batch, batch_size)
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# update_loss_for_logger
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update_loss(trainer, loss_dict, batch_size)
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trainer.time_info["batch_cost"].update(time.time() - tic)
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if iter_id % print_batch_step == 0:
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log_info(trainer, batch_size, epoch_id, iter_id)
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tic = time.time()
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def forward(trainer, batch):
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if not trainer.is_rec:
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return trainer.model(batch[0])
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else:
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return trainer.model(batch[0], batch[1])
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