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