add kbest
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# 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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import operator
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from queue import PriorityQueue
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from typing import Callable, Mapping
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from pathlib import Path
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class KBest(object):
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"""
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A utility class to help save the hard drive by only keeping K best
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checkpoints.
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To be as modularized as possible, this class does not assume anything like
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a Trainer class or anything like a checkpoint directory, it does not know
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about the model or the optimizer, etc.
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It is basically a dynamically mantained K-bset Mapping. When a new item is
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added to the map, save_fn is called. And when an item is remove from the
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map, del_fn is called. `save_fn` and `del_fn` takes a Path object as input
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and returns nothing.
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Though it is designed to control checkpointing behaviors, it can be used
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to do something else if you pass some save_fn and del_fn.
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Example
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--------
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>>> from pathlib import Path
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>>> import shutil
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>>> import paddle
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>>> from paddle import nn
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>>> model = nn.Linear(2, 3)
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>>> def save_model(path):
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... paddle.save(model.state_dict())
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>>> kbest_manager = KBest(max_size=5, save_fn=save_model)
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>>> checkpoint_dir = Path("checkpoints")
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>>> shutil.rmtree(checkpoint_dir)
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>>> checkpoint_dir.mkdir(parents=True)
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>>> a = np.random.rand(20)
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>>> for i, score in enumerate(a):
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... path = checkpoint_dir / f"step_{i}"
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... kbest_manager.add_checkpoint(score, path)
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>>> assert len(list(checkpoint_dir.glob("step_*"))) == 5
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"""
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def __init__(self,
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max_size: int=5,
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save_fn: Callable=None,
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del_fn: Callable=lambda f: f.unlink()):
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self.best_records: Mapping[Path, float] = {}
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self.save_fn = save_fn
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self.del_fn = del_fn
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self.max_size = max_size
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self._save_all = (max_size == -1)
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def should_save(self, metric: float) -> bool:
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if not self.full():
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return True
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# already full
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worst_record_path = max(self.best_records, key=self.best_records.get)
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worst_metric = self.best_records[worst_record_path]
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return metric < worst_metric
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def full(self):
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return (not self._save_all) and len(self.best_records) == self.max_size
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def add_checkpoint(self, metric, path):
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if self.should_save(metric):
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self.save_checkpoint_and_update(metric, path)
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def save_checkpoint_and_update(self, metric, path):
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# remove the worst
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if self.full():
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worst_record_path = max(self.best_records,
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key=self.best_records.get)
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self.best_records.pop(worst_record_path)
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self.del_fn(path)
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# add the new one
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self.save_fn(path)
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self.best_records[path] = metric
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@ -0,0 +1,34 @@
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# 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 parakeet.training.chekpoint import KBest
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import numpy as np
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from pathlib import Path
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import shutil
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def test_kbest():
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def save_fn(path):
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with open(path, 'wt') as f:
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f.write(f"My path is {str(path)}\n")
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kbest_manager = KBest(max_size=5, save_fn=save_fn)
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checkpoint_dir = Path("checkpoints")
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shutil.rmtree(checkpoint_dir)
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checkpoint_dir.mkdir(parents=True)
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a = np.random.rand(20)
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for i, score in enumerate(a):
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path = checkpoint_dir / f"step_{i}"
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kbest_manager.add_checkpoint(score, path)
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assert len(list(checkpoint_dir.glob("step_*"))) == 5
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