Parakeet/parakeet/training/trainer.py

100 lines
3.3 KiB
Python

# 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 pathlib import Path
import tqdm
from dataclasses import dataclass
from parakeet.training.trigger import get_trigger, IntervalTrigger
from parakeet.training.updater import UpdaterBase
from parakeet.training.reporter import scope
class ExtensionEntry(object):
def __init__(self, extension, trigger, priority):
self.extension = extension
self.trigger = trigger
self.priority = priority
class Trainer(object):
def __init__(self,
updater: UpdaterBase,
stop_trigger=None,
out='result',
extensions=None):
self.updater = updater
self.extensions = {}
self.stop_trigger = get_trigger(stop_trigger)
self.out = Path(out)
self.observation = {}
def setup(self):
pass
def extend(self, extension, name=None, trigger=None, priority=None):
trigger = get_trigger(trigger)
ordinal = 0
modified_name = name
while name in self.extensions:
ordinal += 1
modified_name = f"{name}_{ordinal}"
self.extensions[modified_name] = ExtensionEntry(extension, trigger,
priority)
def run(self):
# sort extensions by priorities once
extension_order = sorted(
self.extensions.keys(),
key=lambda name: self.extensions[name].priority,
reverse=True)
extensions = [(name, self.extensions[name])
for name in extension_order]
update = self.updater.update
stop_trigger = self.stop_trigger
# TODO(chenfeiyu): display progress bar correctly
# if the trainer is controlled by epoch: use 2 progressbars
# if the trainer is controlled by iteration: use 1 progressbar
if isinstance(stop_trigger, IntervalTrigger):
if stop_trigger.unit is 'epoch':
max_epoch = self.stop_trigger.period
else:
max_iteration = self.stop_trigger.period
p = tqdm.tqdm()
while True:
self.observation = {}
# set observation as the report target
# you can use report freely in Updater.update()
# updating parameters and state
with scope(self.observation):
update()
p.update()
print(self.observation)
# execute extension when necessary
for name, entry in extensions:
if entry.trigger(self):
entry.extension(self)
if stop_trigger(self):
print("Training Done!")
break