# 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.
import logging
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.nn import Layer
from paddle.optimizer import Optimizer
from paddle.optimizer.lr import LRScheduler
from sklearn.metrics import f1_score
from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
from paddlespeech.t2s.training.reporter import report
from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater
logging.basicConfig(
format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
datefmt='[%Y-%m-%d %H:%M:%S]')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
[docs]class ErnieLinearUpdater(StandardUpdater):
def __init__(self,
model: Layer,
criterion: Layer,
scheduler: LRScheduler,
optimizer: Optimizer,
dataloader: DataLoader,
output_dir=None):
super().__init__(model, optimizer, dataloader, init_state=None)
self.model = model
self.dataloader = dataloader
self.criterion = criterion
self.scheduler = scheduler
self.optimizer = optimizer
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
[docs] def update_core(self, batch):
self.msg = "Rank: {}, ".format(dist.get_rank())
losses_dict = {}
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
loss = self.criterion(y, label)
self.optimizer.clear_grad()
loss.backward()
self.optimizer.step()
self.scheduler.step()
F1_score = f1_score(
label.numpy().tolist(), pred.numpy().tolist(), average="macro")
report("train/loss", float(loss))
losses_dict["loss"] = float(loss)
report("train/F1_score", float(F1_score))
losses_dict["F1_score"] = float(F1_score)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
[docs]class ErnieLinearEvaluator(StandardEvaluator):
def __init__(self,
model: Layer,
criterion: Layer,
dataloader: DataLoader,
output_dir=None):
super().__init__(model, dataloader)
self.model = model
self.criterion = criterion
self.dataloader = dataloader
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
[docs] def evaluate_core(self, batch):
self.msg = "Evaluate: "
losses_dict = {}
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
loss = self.criterion(y, label)
F1_score = f1_score(
label.numpy().tolist(), pred.numpy().tolist(), average="macro")
report("eval/loss", float(loss))
losses_dict["loss"] = float(loss)
report("eval/F1_score", float(F1_score))
losses_dict["F1_score"] = float(F1_score)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
self.logger.info(self.msg)