mirror of https://github.com/coqui-ai/TTS.git
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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import copy
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import os
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import unittest
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import torch
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from tests import get_tests_input_path
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from torch import nn, optim
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from TTS.vocoder.models.wavegrad import Wavegrad
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from TTS.utils.io import load_config
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from TTS.utils.audio import AudioProcessor
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#pylint: disable=unused-variable
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torch.manual_seed(1)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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class WavegradTrainTest(unittest.TestCase):
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def test_train_step(self): # pylint: disable=no-self-use
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"""Test if all layers are updated in a basic training cycle"""
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input_dummy = torch.rand(8, 1, 20 * 300).to(device)
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mel_spec = torch.rand(8, 80, 20).to(device)
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criterion = torch.nn.L1Loss().to(device)
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model = Wavegrad(in_channels=80,
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out_channels=1,
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upsample_factors=[5, 5, 3, 2, 2],
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upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
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[1, 2, 4, 8], [1, 2, 4, 8],
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[1, 2, 4, 8]])
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model.train()
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model.to(device)
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model_ref = copy.deepcopy(model)
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count = 0
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for param, param_ref in zip(model.parameters(),
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model_ref.parameters()):
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assert (param - param_ref).sum() == 0, param
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count += 1
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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for i in range(5):
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y_hat = model.forward(input_dummy, mel_spec, torch.rand(8).to(device))
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optimizer.zero_grad()
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loss = criterion(y_hat, input_dummy)
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loss.backward()
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optimizer.step()
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# check parameter changes
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count = 0
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for param, param_ref in zip(model.parameters(),
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model_ref.parameters()):
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# ignore pre-higway layer since it works conditional
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# if count not in [145, 59]:
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assert (param != param_ref).any(
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), "param {} with shape {} not updated!! \n{}\n{}".format(
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count, param.shape, param, param_ref)
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count += 1
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