chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 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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import os
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import time
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import paddle
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from paddle.hapi.callbacks import (
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Callback,
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CallbackList,
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LRScheduler,
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ModelCheckpoint,
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ProgBarLogger,
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)
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from ..interface import CollectionNames, get_collection
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def config_callbacks(
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callbacks=None,
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engine=None,
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batch_size=None,
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epochs=None,
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steps=None,
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log_freq=2,
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verbose=2,
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save_freq=1,
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save_dir=None,
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metrics=None,
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acc_step=1,
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mode='train',
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):
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cbks = callbacks or []
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cbks = cbks if isinstance(cbks, (list, tuple)) else [cbks]
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if not any(isinstance(k, ProgBarLogger) for k in cbks) and verbose:
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cbks = [ProgBarLoggerAuto(log_freq, verbose=verbose), *cbks]
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if not any(isinstance(k, LRScheduler) for k in cbks):
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cbks = [LRSchedulerAuto(), *cbks]
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if not any(isinstance(k, ModelCheckpoint) for k in cbks):
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cbks = [*cbks, ModelCheckpointAuto(save_freq, save_dir)]
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if not any(isinstance(k, Profiler) for k in cbks) and verbose == 3:
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cbks = [*cbks, Profiler(timer_only=True)]
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if not any(isinstance(k, History) for k in cbks):
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cbks = [*cbks, History()]
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for i, k in enumerate(cbks):
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if isinstance(k, ProgBarLogger):
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cbks[i] = ProgBarLoggerAuto(k.log_freq, k.verbose)
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if isinstance(k, LRScheduler):
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cbks[i] = LRSchedulerAuto(k.by_step, k.by_epoch)
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if isinstance(k, ModelCheckpoint):
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cbks[i] = ModelCheckpointAuto(k.save_freq, k.save_dir)
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cbk_list = CallbackList(cbks)
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cbk_list.set_model(engine)
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metrics = metrics or [] if mode != 'test' else []
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params = {
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'batch_size': batch_size,
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'epochs': epochs,
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'steps': steps,
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'verbose': verbose,
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'metrics': metrics,
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'acc_step': acc_step,
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}
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cbk_list.set_params(params)
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return cbk_list
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class ProgBarLoggerAuto(ProgBarLogger):
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def __init__(self, log_freq=1, verbose=2):
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super().__init__(log_freq, verbose)
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def _is_print(self):
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return True
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def _updates(self, logs, mode):
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values = []
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metrics = getattr(self, f'{mode}_metrics')
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progbar = getattr(self, f'{mode}_progbar')
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steps = getattr(self, f'{mode}_step')
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for k in metrics:
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if k in logs:
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values.append((k, logs[k]))
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if 'lr' in logs:
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values.append(('lr', logs['lr']))
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fetches_logs = logs.get('fetches', {})
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collect_logging = get_collection(CollectionNames.LOGGING)
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for name, var in collect_logging:
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k = name or var.name
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if k in fetches_logs:
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values.append((k, fetches_logs[k]))
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out_logs = logs.get('outputs', {})
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for k in out_logs:
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values.append((k, out_logs[k]))
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if self.verbose == 3 and hasattr(self, f'_{mode}_timer'):
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timer = getattr(self, f'_{mode}_timer')
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cnt = timer['count'] if timer['count'] > 0 else 1.0
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samples = timer['samples'] if timer['samples'] > 0 else 1.0
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values.append(
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('avg_reader_cost', "%.5f sec" % (timer['data_time'] / cnt))
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)
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values.append(
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('avg_batch_cost', "%.5f sec" % (timer['batch_time'] / cnt))
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)
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values.append(
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(
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'ips',
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"%.5f samples/sec"
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% (samples / (timer['data_time'] + timer['batch_time'])),
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)
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)
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timer['count'] = 0
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timer['samples'] = 0
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timer['data_time'] = 0.0
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timer['batch_time'] = 0.0
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progbar.update(steps, values)
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def on_eval_batch_end(self, step, logs=None):
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logs = logs or {}
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self.eval_step += 1
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samples = self.params['batch_size']
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self.evaled_samples += samples
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self._eval_timer['batch_time'] += (
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time.time() - self._eval_timer['batch_data_end_time']
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)
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self._eval_timer['count'] += 1
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samples = self.params['batch_size']
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self._eval_timer['samples'] += samples
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if self._is_print() and self.eval_step % self.log_freq == 0:
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if self.eval_steps is None or self.eval_step < self.eval_steps:
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self._updates(logs, 'eval')
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self._eval_timer['batch_start_time'] = time.time()
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class LRSchedulerAuto(LRScheduler):
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def __init__(self, by_step=True, by_epoch=False):
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super().__init__(by_step, by_epoch)
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def on_epoch_begin(self, epoch=None, logs=None):
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self.acc_step = self.params["acc_step"]
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self.epoch = epoch
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self.train_step = 0
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def on_train_batch_end(self, step, logs=None):
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self.train_step += 1
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if self.by_step and self.train_step % self.acc_step == 0:
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if (
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self.model.optimizer
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and hasattr(self.model.optimizer, '_learning_rate')
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and isinstance(
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self.model.optimizer._learning_rate,
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paddle.optimizer.lr.LRScheduler,
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)
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):
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self.model.optimizer._learning_rate.step()
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class History(Callback):
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def __init__(self):
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self.history = {}
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def on_train_begin(self, logs=None):
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self.epoch = []
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def on_epoch_end(self, epoch, logs=None):
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logs = logs or {}
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self.epoch.append(epoch)
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for k, v in logs.items():
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self.history.setdefault(k, []).append(v)
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self.model.history = self
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class Profiler(Callback):
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def __init__(self, *args, **kwargs):
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self.prof = paddle.profiler.Profiler(*args, **kwargs)
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def on_epoch_begin(self, epoch=None, logs=None):
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self.epoch = epoch
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self.train_step = 0
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self.batch_size = self.params["batch_size"]
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self.steps = self.params['steps']
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def on_train_begin(self, logs=None):
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self.prof.start()
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def on_train_batch_end(self, step, logs=None):
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self.train_step += 1
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self.prof.step(num_samples=self.batch_size)
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print(
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"step {}:{}".format(
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self.train_step, self.prof.step_info(unit='samples')
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)
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)
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def on_train_end(self, logs=None):
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self.prof.stop()
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self.prof.summary()
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class ModelCheckpointAuto(ModelCheckpoint):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def _is_save(self):
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return self.model and self.save_dir
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def on_epoch_end(self, epoch, logs=None):
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if self._is_save() and (self.epoch + 1) % self.save_freq == 0:
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path = f'{self.save_dir}/epoch{epoch}'
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print(f'save checkpoint at {os.path.abspath(path)}')
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self.model.save(path)
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def on_train_end(self, logs=None):
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if self._is_save():
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path = f'{self.save_dir}/final'
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print(f'save checkpoint at {os.path.abspath(path)}')
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self.model.save(path)
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