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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), | |
| ) | |