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