PaddleSpeech/paddlespeech/t2s/training/extensions/evaluator.py

# 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.
# Modified from chainer(https://github.com/chainer/chainer)
from typing import Dict

import paddle
from paddle.io import DataLoader
from paddle.nn import Layer

from paddlespeech.t2s.training import extension
from paddlespeech.t2s.training.reporter import DictSummary
from paddlespeech.t2s.training.reporter import report
from paddlespeech.t2s.training.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 layer in self.models.values():
            layer.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)