mirror of https://github.com/coqui-ai/TTS.git
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
import os
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import torch
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import datetime
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import pickle as pickle_tts
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from TTS.utils.io import RenamingUnpickler
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def load_checkpoint(model, checkpoint_path, use_cuda=False, eval=False):
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try:
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state = torch.load(checkpoint_path, map_location=torch.device('cpu'))
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except ModuleNotFoundError:
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pickle_tts.Unpickler = RenamingUnpickler
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state = torch.load(checkpoint_path, map_location=torch.device('cpu'), pickle_module=pickle_tts)
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model.load_state_dict(state['model'])
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if use_cuda:
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model.cuda()
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if eval:
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model.eval()
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return model, state
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def save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
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scheduler_disc, current_step, epoch, output_path, **kwargs):
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if hasattr(model, 'module'):
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model_state = model.module.state_dict()
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else:
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model_state = model.state_dict()
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model_disc_state = model_disc.state_dict()\
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if model_disc is not None else None
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optimizer_state = optimizer.state_dict()\
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if optimizer is not None else None
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optimizer_disc_state = optimizer_disc.state_dict()\
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if optimizer_disc is not None else None
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scheduler_state = scheduler.state_dict()\
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if scheduler is not None else None
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scheduler_disc_state = scheduler_disc.state_dict()\
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if scheduler_disc is not None else None
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state = {
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'model': model_state,
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'optimizer': optimizer_state,
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'scheduler': scheduler_state,
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'model_disc': model_disc_state,
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'optimizer_disc': optimizer_disc_state,
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'scheduler_disc': scheduler_disc_state,
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'step': current_step,
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'epoch': epoch,
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'date': datetime.date.today().strftime("%B %d, %Y"),
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}
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state.update(kwargs)
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torch.save(state, output_path)
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def save_checkpoint(model, optimizer, scheduler, model_disc, optimizer_disc,
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scheduler_disc, current_step, epoch, output_folder,
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**kwargs):
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file_name = 'checkpoint_{}.pth.tar'.format(current_step)
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checkpoint_path = os.path.join(output_folder, file_name)
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print(" > CHECKPOINT : {}".format(checkpoint_path))
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save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
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scheduler_disc, current_step, epoch, checkpoint_path, **kwargs)
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def save_best_model(target_loss, best_loss, model, optimizer, scheduler,
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model_disc, optimizer_disc, scheduler_disc, current_step,
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epoch, output_folder, **kwargs):
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if target_loss < best_loss:
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file_name = 'best_model.pth.tar'
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checkpoint_path = os.path.join(output_folder, file_name)
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print(" > BEST MODEL : {}".format(checkpoint_path))
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save_model(model,
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optimizer,
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scheduler,
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model_disc,
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optimizer_disc,
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scheduler_disc,
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current_step,
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epoch,
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checkpoint_path,
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model_loss=target_loss,
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**kwargs)
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best_loss = target_loss
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return best_loss
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