# Copyright 2019 The Texar 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.
"""
Actions for the Executor module.
"""
from abc import ABC, abstractmethod
from texar.torch.run.executor import Executor
__all__ = [
"reset_params",
"scale_lr",
"early_stop",
]
class Action(ABC):
@abstractmethod
def __call__(self, executor: Executor) -> None:
raise NotImplementedError
[docs]class reset_params(Action):
def __init__(self, training_state: bool = True):
self.load_training_state = training_state
def __call__(self, executor: Executor):
# TODO: Only optimizer?
executor.load(load_training_state=self.load_training_state)
[docs]class scale_lr(Action):
def __init__(self, scale: float):
# TODO: Change to accept LRScheduler.
self.scale = scale
def __call__(self, executor: Executor):
new_lr = []
assert executor.optimizer is not None
for group in executor.optimizer.param_groups: # type: ignore
lr = group['lr'] * self.scale
group['lr'] = lr
new_lr.append(lr)
lr_str = ", ".join(f"{lr:.4e}" for lr in new_lr)
if len(new_lr) > 1:
lr_str = f"[{lr_str}]"
executor.write_log(f"Learning rate scaled by {self.scale}. "
f"New LR: {lr_str}")
[docs]class early_stop(Action):
def __init__(self, patience: int):
if not isinstance(patience, int) or patience <= 0:
raise ValueError("`patience` must be a positive integer")
self.patience = patience
self.count = 0
def __call__(self, executor: Executor):
self.count += 1
if self.count >= self.patience:
executor.terminate()
executor.write_log(f"Early stopping patience exhausted, "
f"terminating training...")
else:
executor.write_log(f"Early stopping patience decrease to "
f"{self.patience - self.count}")