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| import unittest | |
| import torch | |
| from models.vocoder.univnet.kernel_predictor import KernelPredictor | |
| class TestKernelPredictor(unittest.TestCase): | |
| def setUp(self): | |
| self.batch_size = 2 | |
| self.cond_channels = 4 | |
| self.conv_in_channels = 3 | |
| self.conv_out_channels = 5 | |
| self.conv_layers = 2 | |
| self.conv_kernel_size = 3 | |
| self.kpnet_hidden_channels = 64 | |
| self.kpnet_conv_size = 3 | |
| self.kpnet_dropout = 0.0 | |
| self.lReLU_slope = 0.1 | |
| self.model = KernelPredictor( | |
| self.cond_channels, | |
| self.conv_in_channels, | |
| self.conv_out_channels, | |
| self.conv_layers, | |
| self.conv_kernel_size, | |
| self.kpnet_hidden_channels, | |
| self.kpnet_conv_size, | |
| self.kpnet_dropout, | |
| self.lReLU_slope, | |
| ) | |
| def test_forward(self): | |
| c = torch.randn(self.batch_size, self.cond_channels, 10) | |
| kernels, bias = self.model(c) | |
| self.assertIsInstance(kernels, torch.Tensor) | |
| self.assertEqual( | |
| kernels.shape, | |
| ( | |
| self.batch_size, | |
| self.conv_layers, | |
| self.conv_in_channels, | |
| self.conv_out_channels, | |
| self.conv_kernel_size, | |
| 10, | |
| ), | |
| ) | |
| self.assertIsInstance(bias, torch.Tensor) | |
| self.assertEqual( | |
| bias.shape, (self.batch_size, self.conv_layers, self.conv_out_channels, 10), | |
| ) | |
| def test_remove_weight_norm(self): | |
| self.model.remove_weight_norm() | |
| for module in self.model.modules(): | |
| if hasattr(module, "weight_g"): | |
| self.assertIsNone(module.weight_g) | |
| self.assertIsNone(module.weight_v) | |