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| import unittest | |
| import torch | |
| from models.tts.delightful_tts.conv_blocks.conv1d import ( | |
| DepthWiseConv1d, | |
| PointwiseConv1d, | |
| ) | |
| class TestDepthwiseConv1d(unittest.TestCase): | |
| def setUp(self): | |
| # initialize parameters once and reuse them in multiple test cases | |
| self.in_channels, self.out_channels, self.kernel_size, self.padding = 2, 4, 3, 1 | |
| self.depthwise_conv = DepthWiseConv1d( | |
| self.in_channels, | |
| self.out_channels, | |
| self.kernel_size, | |
| self.padding, | |
| ) | |
| # Generate an input data | |
| self.x_rand = torch.randn( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| self.x_zero = torch.zeros( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| self.x_ones = torch.ones( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| def test_forward(self): | |
| out = self.depthwise_conv(self.x_rand) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 64)) | |
| def test_non_random_input(self): | |
| out = self.depthwise_conv(self.x_ones) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 64)) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 64)) | |
| def test_zero_input(self): | |
| out = self.depthwise_conv(self.x_zero) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 64)) | |
| def test_weight_change(self): | |
| self.depthwise_conv.conv.weight.data.fill_(0.5) | |
| out_first = self.depthwise_conv(self.x_rand) | |
| self.depthwise_conv.conv.weight.data.fill_(1.0) | |
| out_second = self.depthwise_conv(self.x_rand) | |
| # Ensuring weight changes have an effect | |
| self.assertTrue(torch.any(out_first != out_second)) | |
| class TestPointwiseConv1d(unittest.TestCase): | |
| def setUp(self): | |
| # initialize parameters once and reuse them in multiple test cases | |
| self.in_channels, self.out_channels, self.stride, self.padding, self.bias = ( | |
| 2, | |
| 4, | |
| 1, | |
| 1, | |
| True, | |
| ) | |
| self.pointwise_conv = PointwiseConv1d( | |
| self.in_channels, self.out_channels, self.stride, self.padding, self.bias, | |
| ) | |
| # Generate an input data | |
| self.x_rand = torch.randn( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| self.x_zero = torch.zeros( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| self.x_ones = torch.ones( | |
| 32, | |
| self.in_channels, | |
| 64, | |
| ) | |
| def test_forward(self): | |
| out = self.pointwise_conv(self.x_rand) | |
| self.assertIsInstance(out, torch.Tensor) | |
| # Padding of 1 means one column of zeroes got added both at the beginning and at the end, | |
| # that's why you have 64 (original) + 2 (padding) = 66 | |
| self.assertEqual(out.shape, (32, self.out_channels, 66)) | |
| def test_non_random_input(self): | |
| out = self.pointwise_conv(self.x_ones) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 66)) | |
| def test_zero_input(self): | |
| out = self.pointwise_conv(self.x_zero) | |
| self.assertIsInstance(out, torch.Tensor) | |
| self.assertEqual(out.shape, (32, self.out_channels, 66)) | |
| def test_weight_change(self): | |
| self.pointwise_conv.conv.weight.data.fill_(0.5) | |
| out_first = self.pointwise_conv(self.x_rand) | |
| self.pointwise_conv.conv.weight.data.fill_(1.0) | |
| out_second = self.pointwise_conv(self.x_rand) | |
| self.assertTrue(torch.any(out_first != out_second)) | |
| def test_kernel_size(self): | |
| # Checking if the module can handle non-default kernel sizes. | |
| kernel_size = 2 | |
| pointwise_conv = PointwiseConv1d( | |
| self.in_channels, | |
| self.out_channels, | |
| self.stride, | |
| self.padding, | |
| self.bias, | |
| kernel_size, | |
| ) | |
| out = pointwise_conv(self.x_rand) | |
| # ((input_size - kernel_size + 2*padding)/stride ) + 1 | |
| # ((64 - 2 + 2*1) / 1 ) + 1 = 65 | |
| self.assertEqual(out.shape, (32, self.out_channels, 65)) | |
| if __name__ == "__main__": | |
| unittest.main() | |