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PaddleSpeech/deepspeech/training/extensions/evaluator.py

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# 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 typing import Dict
import extension
import paddle
from paddle.io import DataLoader
from paddle.nn import Layer
from ..reporter import DictSummary
from ..reporter import report
from ..reporter import scope
class StandardEvaluator(extension.Extension):
trigger = (1, 'epoch')
default_name = 'validation'
priority = extension.PRIORITY_WRITER
name = None
def __init__(self, model: Layer, dataloader: DataLoader):
# it is designed to hold multiple models
models = {"main": model}
self.models: Dict[str, Layer] = models
self.model = model
# dataloaders
self.dataloader = dataloader
def evaluate_core(self, batch):
# compute
self.model(batch) # you may report here
def evaluate(self):
# switch to eval mode
for model in self.models.values():
model.eval()
# to average evaluation metrics
summary = DictSummary()
for batch in self.dataloader:
observation = {}
with scope(observation):
# main evaluation computation here.
with paddle.no_grad():
self.evaluate_core(batch)
summary.add(observation)
summary = summary.compute_mean()
return summary
def __call__(self, trainer=None):
# evaluate and report the averaged metric to current observation
# if it is used to extend a trainer, the metrics is reported to
# to observation of the trainer
# or otherwise, you can use your own observation
summary = self.evaluate()
for k, v in summary.items():
report(k, v)