forked from mrq/DL-Art-School
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
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import torch
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import trainer.eval.evaluator as evaluator
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from data import create_dataset
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from data.audio.nv_tacotron_dataset import TextMelCollate
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from models.tacotron2.loss import Tacotron2LossRaw
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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# Evaluates the performance of a MEL spectrogram predictor.
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class MelEvaluator(evaluator.Evaluator):
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def __init__(self, model, opt_eval, env):
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super().__init__(model, opt_eval, env, uses_all_ddp=True)
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self.batch_sz = opt_eval['batch_size']
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self.dataset = create_dataset(opt_eval['dataset'])
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assert self.batch_sz is not None
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self.dataloader = DataLoader(self.dataset, self.batch_sz, shuffle=False, num_workers=1, collate_fn=TextMelCollate(n_frames_per_step=1))
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self.criterion = Tacotron2LossRaw()
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def perform_eval(self):
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counter = 0
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total_error = 0
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self.model.eval()
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for batch in tqdm(self.dataloader):
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model_params = {
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'text_inputs': 'padded_text',
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'text_lengths': 'input_lengths',
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'mels': 'padded_mel',
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'output_lengths': 'output_lengths',
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}
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params = {k: batch[v].to(self.env['device']) for k, v in model_params.items()}
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with torch.no_grad():
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pred = self.model(**params)
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targets = ['padded_mel', 'padded_gate']
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targets = [batch[t].to(self.env['device']) for t in targets]
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total_error += self.criterion(pred, targets).item()
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counter += 1
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self.model.train()
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return {"validation-score": total_error / counter}
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