| import unittest
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|
|
| import numpy as np
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| import torch
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| from torch import optim
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|
|
| from TTS.vocoder.configs import WavegradConfig
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| from TTS.vocoder.models.wavegrad import Wavegrad, WavegradArgs
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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):
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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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| args = WavegradArgs(
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| 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], [1, 2, 4, 8], [1, 2, 4, 8], [1, 2, 4, 8]],
|
| )
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| config = WavegradConfig(model_params=args)
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| model = Wavegrad(config)
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|
|
| model_ref = Wavegrad(config)
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| model.train()
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| model.to(device)
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| betas = np.linspace(1e-6, 1e-2, 1000)
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| model.compute_noise_level(betas)
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| model_ref.load_state_dict(model.state_dict())
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| model_ref.to(device)
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| count = 0
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| for param, param_ref in zip(model.parameters(), 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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|
|
| count = 0
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| for param, param_ref in zip(model.parameters(), model_ref.parameters()):
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|
|
|
|
| assert (param != param_ref).any(), "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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|
|
