You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
72 lines
2.3 KiB
72 lines
2.3 KiB
# 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 paddle
|
|
from paddle.io import DataLoader
|
|
from paddle.nn import Layer
|
|
|
|
from parakeet.training import extension
|
|
from parakeet.training.reporter import DictSummary
|
|
from parakeet.training.reporter import report
|
|
from parakeet.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)
|