mirror of
https://github.com/wassname/TTS.git
synced 2026-09-09 11:16:00 +08:00
Fix Pylint issues
This commit is contained in:
Reuben Morais
@@ -7,7 +7,6 @@ import traceback
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import numpy as np
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import torch
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import torch.nn as nn
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from tensorboardX import SummaryWriter
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from torch import optim
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from torch.utils.data import DataLoader
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@@ -18,9 +17,8 @@ from layers.losses import L1LossMasked, MSELossMasked
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from utils.audio import AudioProcessor
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from utils.generic_utils import (NoamLR, check_update, count_parameters,
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create_experiment_folder, get_git_branch,
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load_config, lr_decay,
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remove_experiment_folder, save_best_model,
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save_checkpoint, sequence_mask, weight_decay,
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load_config, remove_experiment_folder,
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save_best_model, save_checkpoint, weight_decay,
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set_init_dict, copy_config_file, setup_model,
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split_dataset)
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from utils.logger import Logger
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@@ -87,7 +85,7 @@ def setup_loader(is_val=False, verbose=False):
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def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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ap, epoch):
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data_loader = setup_loader(is_val=False, verbose=(epoch==0))
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data_loader = setup_loader(is_val=False, verbose=(epoch == 0))
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if c.use_speaker_embedding:
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speaker_mapping = load_speaker_mapping(OUT_PATH)
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model.train()
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@@ -131,7 +129,8 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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if c.lr_decay:
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scheduler.step()
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optimizer.zero_grad()
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if optimizer_st: optimizer_st.zero_grad();
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if optimizer_st:
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optimizer_st.zero_grad()
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# dispatch data to GPU
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if use_cuda:
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@@ -146,7 +145,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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# forward pass model
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decoder_output, postnet_output, alignments, stop_tokens = model(
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text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
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text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
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# loss computation
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stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
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@@ -203,16 +202,16 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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if args.rank == 0:
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avg_postnet_loss += float(postnet_loss.item())
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avg_decoder_loss += float(decoder_loss.item())
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avg_stop_loss += stop_loss if type(stop_loss) is float else float(stop_loss.item())
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avg_stop_loss += stop_loss if isinstance(stop_loss, float) else float(stop_loss.item())
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avg_step_time += step_time
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# Plot Training Iter Stats
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iter_stats = {"loss_posnet": postnet_loss.item(),
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"loss_decoder": decoder_loss.item(),
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"lr": current_lr,
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"grad_norm": grad_norm,
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"grad_norm_st": grad_norm_st,
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"step_time": step_time}
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"loss_decoder": decoder_loss.item(),
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"lr": current_lr,
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"grad_norm": grad_norm,
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"grad_norm_st": grad_norm_st,
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"step_time": step_time}
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tb_logger.tb_train_iter_stats(current_step, iter_stats)
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if current_step % c.save_step == 0:
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@@ -224,7 +223,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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# Diagnostic visualizations
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const_spec = postnet_output[0].data.cpu().numpy()
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gt_spec = linear_input[0].data.cpu().numpy() if c.model in ["Tacotron", "TacotronGST"] else mel_input[0].data.cpu().numpy()
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gt_spec = linear_input[0].data.cpu().numpy() if c.model in ["Tacotron", "TacotronGST"] else mel_input[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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figures = {
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@@ -239,9 +238,9 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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train_audio = ap.inv_spectrogram(const_spec.T)
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else:
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train_audio = ap.inv_mel_spectrogram(const_spec.T)
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tb_logger.tb_train_audios(current_step,
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{'TrainAudio': train_audio},
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c.audio["sample_rate"])
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tb_logger.tb_train_audios(current_step,
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{'TrainAudio': train_audio},
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c.audio["sample_rate"])
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avg_postnet_loss /= (num_iter + 1)
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avg_decoder_loss /= (num_iter + 1)
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@@ -263,9 +262,9 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
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if args.rank == 0:
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# Plot Training Epoch Stats
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epoch_stats = {"loss_postnet": avg_postnet_loss,
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss,
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"epoch_time": epoch_time}
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss,
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"epoch_time": epoch_time}
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tb_logger.tb_train_epoch_stats(current_step, epoch_stats)
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if c.tb_model_param_stats:
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tb_logger.tb_model_weights(model, current_step)
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@@ -402,8 +401,8 @@ def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
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# Plot Validation Stats
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epoch_stats = {"loss_postnet": avg_postnet_loss,
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss}
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"loss_decoder": avg_decoder_loss,
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"stop_loss": avg_stop_loss}
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tb_logger.tb_eval_stats(current_step, epoch_stats)
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if args.rank == 0 and epoch > c.test_delay_epochs:
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@@ -420,7 +419,7 @@ def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
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file_path = os.path.join(AUDIO_PATH, str(current_step))
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os.makedirs(file_path, exist_ok=True)
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file_path = os.path.join(file_path,
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"TestSentence_{}.wav".format(idx))
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"TestSentence_{}.wav".format(idx))
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ap.save_wav(wav, file_path)
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test_audios['{}-audio'.format(idx)] = wav
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test_figures['{}-prediction'.format(idx)] = plot_spectrogram(postnet_output, ap)
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@@ -482,12 +481,11 @@ def main(args):
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# TODO: fix optimizer init, model.cuda() needs to be called before
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# optimizer restore
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# optimizer.load_state_dict(checkpoint['optimizer'])
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if len(c.reinit_layers) > 0:
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if c.reinit_layers:
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raise RuntimeError
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model.load_state_dict(checkpoint['model'])
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except:
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print(" > Partial model initialization.")
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partial_init_flag = True
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model_dict = model.state_dict()
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model_dict = set_init_dict(model_dict, checkpoint, c)
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model.load_state_dict(model_dict)
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@@ -496,7 +494,6 @@ def main(args):
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group['lr'] = c.lr
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print(
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" > Model restored from step %d" % checkpoint['step'], flush=True)
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start_epoch = checkpoint['epoch']
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args.restore_step = checkpoint['step']
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else:
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args.restore_step = 0
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@@ -504,7 +501,8 @@ def main(args):
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if use_cuda:
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model = model.cuda()
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criterion.cuda()
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if criterion_st: criterion_st.cuda();
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if criterion_st:
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criterion_st.cuda()
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# DISTRUBUTED
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if num_gpus > 1:
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@@ -615,7 +613,7 @@ if __name__ == '__main__':
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os.chmod(AUDIO_PATH, 0o775)
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os.chmod(OUT_PATH, 0o775)
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if args.rank==0:
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if args.rank == 0:
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LOG_DIR = OUT_PATH
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tb_logger = Logger(LOG_DIR)
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@@ -629,8 +627,8 @@ if __name__ == '__main__':
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try:
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sys.exit(0)
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except SystemExit:
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os._exit(0)
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except Exception:
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os._exit(0) #pylint: disable=protected-access
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except Exception: #pylint: disable=broad-except
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remove_experiment_folder(OUT_PATH)
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traceback.print_exc()
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sys.exit(1)
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