# 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 from typing import Dict 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 timer import timer from parakeet.training.extensions.evaluator import StandardEvaluator from parakeet.training.reporter import report from parakeet.training.updaters.standard_updater import StandardUpdater from parakeet.training.updaters.standard_updater import UpdaterState 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) class PWGUpdater(StandardUpdater): def __init__(self, models: Dict[str, Layer], optimizers: Dict[str, Optimizer], criterions: Dict[str, Layer], schedulers: Dict[str, LRScheduler], dataloader: DataLoader, discriminator_train_start_steps: int, lambda_adv: float, output_dir=None): self.models = models self.generator: Layer = models['generator'] self.discriminator: Layer = models['discriminator'] self.optimizers = optimizers self.optimizer_g: Optimizer = optimizers['generator'] self.optimizer_d: Optimizer = optimizers['discriminator'] self.criterions = criterions self.criterion_stft = criterions['stft'] self.criterion_mse = criterions['mse'] self.schedulers = schedulers self.scheduler_g = schedulers['generator'] self.scheduler_d = schedulers['discriminator'] self.dataloader = dataloader self.discriminator_train_start_steps = discriminator_train_start_steps self.lambda_adv = lambda_adv self.state = UpdaterState(iteration=0, epoch=0) self.train_iterator = iter(self.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 = "" def update_core(self, batch): self.msg = "Rank: {}, ".format(dist.get_rank()) losses_dict = {} # parse batch wav, mel = batch # Generator noise = paddle.randn(wav.shape) with timer() as t: wav_ = self.generator(noise, mel) # logging.debug(f"Generator takes {t.elapse}s.") # initialize gen_loss = 0.0 ## Multi-resolution stft loss with timer() as t: sc_loss, mag_loss = self.criterion_stft(wav_, wav) # logging.debug(f"Multi-resolution STFT loss takes {t.elapse}s.") report("train/spectral_convergence_loss", float(sc_loss)) report("train/log_stft_magnitude_loss", float(mag_loss)) losses_dict["spectral_convergence_loss"] = float(sc_loss) losses_dict["log_stft_magnitude_loss"] = float(mag_loss) gen_loss += sc_loss + mag_loss ## Adversarial loss if self.state.iteration > self.discriminator_train_start_steps: with timer() as t: p_ = self.discriminator(wav_) adv_loss = self.criterion_mse(p_, paddle.ones_like(p_)) # logging.debug( # f"Discriminator and adversarial loss takes {t.elapse}s") report("train/adversarial_loss", float(adv_loss)) losses_dict["adversarial_loss"] = float(adv_loss) gen_loss += self.lambda_adv * adv_loss report("train/generator_loss", float(gen_loss)) losses_dict["generator_loss"] = float(gen_loss) with timer() as t: self.optimizer_g.clear_grad() gen_loss.backward() # logging.debug(f"Backward takes {t.elapse}s.") with timer() as t: self.optimizer_g.step() self.scheduler_g.step() # logging.debug(f"Update takes {t.elapse}s.") # Disctiminator if self.state.iteration > self.discriminator_train_start_steps: with paddle.no_grad(): wav_ = self.generator(noise, mel) p = self.discriminator(wav) p_ = self.discriminator(wav_.detach()) real_loss = self.criterion_mse(p, paddle.ones_like(p)) fake_loss = self.criterion_mse(p_, paddle.zeros_like(p_)) dis_loss = real_loss + fake_loss report("train/real_loss", float(real_loss)) report("train/fake_loss", float(fake_loss)) report("train/discriminator_loss", float(dis_loss)) losses_dict["real_loss"] = float(real_loss) losses_dict["fake_loss"] = float(fake_loss) losses_dict["discriminator_loss"] = float(dis_loss) self.optimizer_d.clear_grad() dis_loss.backward() self.optimizer_d.step() self.scheduler_d.step() self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) class PWGEvaluator(StandardEvaluator): def __init__(self, models, criterions, dataloader, lambda_adv, output_dir=None): self.models = models self.generator = models['generator'] self.discriminator = models['discriminator'] self.criterions = criterions self.criterion_stft = criterions['stft'] self.criterion_mse = criterions['mse'] self.dataloader = dataloader self.lambda_adv = lambda_adv 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 = "" def evaluate_core(self, batch): # logging.debug("Evaluate: ") self.msg = "Evaluate: " losses_dict = {} wav, mel = batch noise = paddle.randn(wav.shape) with timer() as t: wav_ = self.generator(noise, mel) # logging.debug(f"Generator takes {t.elapse}s") ## Adversarial loss with timer() as t: p_ = self.discriminator(wav_) adv_loss = self.criterion_mse(p_, paddle.ones_like(p_)) # logging.debug( # f"Discriminator and adversarial loss takes {t.elapse}s") report("eval/adversarial_loss", float(adv_loss)) losses_dict["adversarial_loss"] = float(adv_loss) gen_loss = self.lambda_adv * adv_loss # stft loss with timer() as t: sc_loss, mag_loss = self.criterion_stft(wav_, wav) # logging.debug(f"Multi-resolution STFT loss takes {t.elapse}s") report("eval/spectral_convergence_loss", float(sc_loss)) report("eval/log_stft_magnitude_loss", float(mag_loss)) losses_dict["spectral_convergence_loss"] = float(sc_loss) losses_dict["log_stft_magnitude_loss"] = float(mag_loss) gen_loss += sc_loss + mag_loss report("eval/generator_loss", float(gen_loss)) losses_dict["generator_loss"] = float(gen_loss) # Disctiminator p = self.discriminator(wav) real_loss = self.criterion_mse(p, paddle.ones_like(p)) fake_loss = self.criterion_mse(p_, paddle.zeros_like(p_)) dis_loss = real_loss + fake_loss report("eval/real_loss", float(real_loss)) report("eval/fake_loss", float(fake_loss)) report("eval/discriminator_loss", float(dis_loss)) losses_dict["real_loss"] = float(real_loss) losses_dict["fake_loss"] = float(fake_loss) losses_dict["discriminator_loss"] = float(dis_loss) self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) self.logger.info(self.msg)