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import unittest
import torch
from torch import nn
from models.vocoder.univnet.lvc_block import LVCBlock
class TestLVCBlock(unittest.TestCase):
def setUp(self):
self.batch_size = 2
self.in_channels = 3
self.cond_channels = 4
self.stride = 2
self.in_length = 65536
self.kernel_length = 10
self.cond_length = 256
self.dilations = [1, 3, 9, 27]
self.lReLU_slope = 0.2
self.conv_kernel_size = 3
self.cond_hop_length = 256
self.kpnet_hidden_channels = 64
self.kpnet_conv_size = 3
self.kpnet_dropout = 0.0
self.x = torch.randn(self.batch_size, self.in_channels, self.in_length)
self.kernel = torch.randn(
self.batch_size,
self.cond_channels,
self.cond_length,
)
self.lvc_block = LVCBlock(
in_channels=self.in_channels,
cond_channels=self.cond_channels,
stride=self.stride,
dilations=self.dilations,
lReLU_slope=self.lReLU_slope,
conv_kernel_size=self.conv_kernel_size,
cond_hop_length=self.cond_hop_length,
kpnet_hidden_channels=self.kpnet_hidden_channels,
kpnet_conv_size=self.kpnet_conv_size,
kpnet_dropout=self.kpnet_dropout,
)
def test_remove_weight_norm(self):
self.lvc_block.remove_weight_norm()
for _, module in self.lvc_block.named_modules():
if isinstance(module, (nn.Conv1d, nn.ConvTranspose1d)):
self.assertFalse(hasattr(module, "weight_g"))
self.assertFalse(hasattr(module, "weight_v"))
def test_location_variable_convolution(self):
kernel = torch.randn(
self.batch_size,
self.in_channels,
2 * self.in_channels,
self.conv_kernel_size,
self.cond_length,
)
bias = torch.randn(self.batch_size, 2 * self.in_channels, self.cond_length)
output = self.lvc_block.location_variable_convolution(
x=self.x,
kernel=kernel,
bias=bias,
dilation=1,
hop_size=self.cond_hop_length,
)
self.assertEqual(
output.shape, (self.batch_size, 2 * self.in_channels, self.in_length),
)
def test_forward(self):
x = torch.randn(
self.batch_size,
self.in_channels,
self.in_length // self.stride,
)
output = self.lvc_block(x, self.kernel)
self.assertEqual(
output.shape, (self.batch_size, self.in_channels, self.in_length),
)
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