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102 lines
3.3 KiB
102 lines
3.3 KiB
# 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 typing import Dict
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import paddle
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from paddle import distributed as dist
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from paddle.io import DataLoader
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from paddle.nn import Layer
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from . import extension
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from ..reporter import DictSummary
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from ..reporter import report
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from ..reporter import scope
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from ..timer import Timer
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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class StandardEvaluator(extension.Extension):
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trigger = (1, 'epoch')
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default_name = 'validation'
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priority = extension.PRIORITY_WRITER
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name = None
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def __init__(self, model: Layer, dataloader: DataLoader):
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# it is designed to hold multiple models
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models = {"main": model}
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self.models: Dict[str, Layer] = models
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self.model = model
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# dataloaders
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self.dataloader = dataloader
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def evaluate_core(self, batch):
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# compute
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self.model(batch) # you may report here
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return
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def evaluate_sync(self, data):
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# dist sync `evaluate_core` outputs
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if data is None:
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return
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numerator, denominator = data
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if dist.get_world_size() > 1:
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numerator = paddle.to_tensor(numerator)
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denominator = paddle.to_tensor(denominator)
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# the default operator in all_reduce function is sum.
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dist.all_reduce(numerator)
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dist.all_reduce(denominator)
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value = numerator / denominator
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value = float(value)
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else:
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value = numerator / denominator
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# used for `snapshort` to do kbest save.
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report("VALID/LOSS", value)
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logger.info(f"Valid: all-reduce loss {value}")
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def evaluate(self):
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# switch to eval mode
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for model in self.models.values():
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model.eval()
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# to average evaluation metrics
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summary = DictSummary()
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for batch in self.dataloader:
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observation = {}
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with scope(observation):
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# main evaluation computation here.
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with paddle.no_grad():
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self.evaluate_sync(self.evaluate_core(batch))
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summary.add(observation)
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summary = summary.compute_mean()
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# switch to train mode
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for model in self.models.values():
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model.train()
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return summary
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def __call__(self, trainer=None):
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# evaluate and report the averaged metric to current observation
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# if it is used to extend a trainer, the metrics is reported to
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# to observation of the trainer
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# or otherwise, you can use your own observation
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with Timer("Eval Time Cost: {}"):
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summary = self.evaluate()
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for k, v in summary.items():
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report(k, v)
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