Spaces:
Running
Running
File size: 1,779 Bytes
9d61c9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
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)
|