mirror of https://github.com/coqui-ai/TTS.git
58 lines
2.1 KiB
Python
58 lines
2.1 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, amp=None, use_cuda=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 amp and 'amp' in state:
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amp.load_state_dict(state['amp'])
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if use_cuda:
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model.cuda()
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# set model stepsize
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if hasattr(model.decoder, 'r'):
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model.decoder.set_r(state['r'])
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print(" > Model r: ", state['r'])
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return model, state
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def save_model(model, optimizer, current_step, epoch, r, output_path, amp_state_dict=None, **kwargs):
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new_state_dict = model.state_dict()
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state = {
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'model': new_state_dict,
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'optimizer': optimizer.state_dict() if optimizer is not None else None,
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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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'r': r
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}
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if amp_state_dict:
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state['amp'] = amp_state_dict
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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, current_step, epoch, r, output_folder, **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, current_step, epoch, r, checkpoint_path, **kwargs)
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def save_best_model(target_loss, best_loss, model, optimizer, current_step, epoch, r, 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, optimizer, current_step, epoch, r, checkpoint_path, model_loss=target_loss, **kwargs)
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best_loss = target_loss
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return best_loss
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