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reformatting and styling

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This commit is contained in:
Eren Gölge
2021-04-12 11:47:39 +02:00
parent 9011dddf77
commit f519012dea
159 changed files with 6589 additions and 6429 deletions
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tests/test_wavegrad_train.py
+19 -20
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@@ -3,9 +3,10 @@ import unittest
import numpy as np
import torch
from torch import optim
from TTS.vocoder.models.wavegrad import Wavegrad
#pylint: disable=unused-variable
# pylint: disable=unused-variable
torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
@@ -19,19 +20,19 @@ class WavegradTrainTest(unittest.TestCase):
mel_spec = torch.rand(8, 80, 20).to(device)
criterion = torch.nn.L1Loss().to(device)
model = Wavegrad(in_channels=80,
out_channels=1,
upsample_factors=[5, 5, 3, 2, 2],
upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
[1, 2, 4, 8], [1, 2, 4, 8],
[1, 2, 4, 8]])
model = Wavegrad(
in_channels=80,
out_channels=1,
upsample_factors=[5, 5, 3, 2, 2],
upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 4, 8], [1, 2, 4, 8], [1, 2, 4, 8]],
)
model_ref = Wavegrad(in_channels=80,
out_channels=1,
upsample_factors=[5, 5, 3, 2, 2],
upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
[1, 2, 4, 8], [1, 2, 4, 8],
[1, 2, 4, 8]])
model_ref = Wavegrad(
in_channels=80,
out_channels=1,
upsample_factors=[5, 5, 3, 2, 2],
upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 4, 8], [1, 2, 4, 8], [1, 2, 4, 8]],
)
model.train()
model.to(device)
betas = np.linspace(1e-6, 1e-2, 1000)
@@ -39,8 +40,7 @@ class WavegradTrainTest(unittest.TestCase):
model_ref.load_state_dict(model.state_dict())
model_ref.to(device)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
for param, param_ref in zip(model.parameters(), model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=0.001)
@@ -52,11 +52,10 @@ class WavegradTrainTest(unittest.TestCase):
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
for param, param_ref in zip(model.parameters(), model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref
)
count += 1