86 lines
3.9 KiB
Python
86 lines
3.9 KiB
Python
# Copyright The PyTorch Lightning team.
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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 pytorch_lightning.utilities.exceptions import MisconfigurationException
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class LRSchedulerConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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def update_learning_rates(self, interval: str, monitor_metrics=None):
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"""Update learning rates.
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Args:
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interval: either 'epoch' or 'step'.
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monitor_metrics: dict of possible values to monitor
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"""
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if not self.trainer.lr_schedulers:
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return
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for scheduler_idx, lr_scheduler in enumerate(self.trainer.lr_schedulers):
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current_idx = self.trainer.batch_idx if interval == 'step' else self.trainer.current_epoch
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current_idx += 1 # account for both batch and epoch starts from 0
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# Take step if call to update_learning_rates matches the interval key and
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# the current step modulo the schedulers frequency is zero
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if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:
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# If instance of ReduceLROnPlateau, we need to pass validation loss
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if lr_scheduler['reduce_on_plateau']:
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monitor_key = lr_scheduler['monitor']
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if monitor_metrics is not None:
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monitor_val = monitor_metrics.get(monitor_key)
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else:
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monitor_val = self.trainer.logger_connector.callback_metrics.get(monitor_key)
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if monitor_val is None:
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avail_metrics = ','.join(list(self.trainer.logger_connector.callback_metrics.keys()))
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raise MisconfigurationException(
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f'ReduceLROnPlateau conditioned on metric {monitor_key}'
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f' which is not available. Available metrics are: {avail_metrics}.'
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' Condition can be set using `monitor` key in lr scheduler dict'
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)
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if self.trainer.dev_debugger.enabled:
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old_lr = lr_scheduler['scheduler'].optimizer.param_groups[0]['lr']
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# update LR
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lr_scheduler['scheduler'].step(monitor_val)
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if self.trainer.dev_debugger.enabled:
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new_lr = lr_scheduler['scheduler'].optimizer.param_groups[0]['lr']
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self.trainer.dev_debugger.track_lr_schedulers_update(
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self.trainer.batch_idx,
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interval,
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scheduler_idx,
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old_lr,
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new_lr,
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monitor_key,
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)
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else:
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if self.trainer.dev_debugger.enabled:
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old_lr = lr_scheduler['scheduler'].optimizer.param_groups[0]['lr']
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# update LR
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lr_scheduler['scheduler'].step()
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if self.trainer.dev_debugger.enabled:
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new_lr = lr_scheduler['scheduler'].optimizer.param_groups[0]['lr']
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self.trainer.dev_debugger.track_lr_schedulers_update(
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self.trainer.batch_idx,
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interval,
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scheduler_idx,
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old_lr, new_lr
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)
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