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import unittest
import torch
from torch.autograd.gradcheck import gradcheck
from models.tts.delightful_tts.conv_blocks.activation import GLUActivation
# Unit Testing Class
class TestGLUActivation(unittest.TestCase):
def setUp(self):
self.glu = GLUActivation()
# Test that dimensions remain unchanged
def test_dimensions(self):
# random data with shape like (batch_size, channels, height, width)
x = torch.randn(32, 4, 64, 64)
x_after_glu = self.glu(x)
expected_shape = (
x_after_glu.shape[0],
x_after_glu.shape[1] * 2,
x_after_glu.shape[2],
x_after_glu.shape[3],
)
self.assertEqual(x.shape, expected_shape)
# Test the gradients
def test_gradcheck(self):
# use double precision for gradcheck
x = torch.randn(2, 2, dtype=torch.float64, requires_grad=True)
self.assertTrue(gradcheck(self.glu, x), "Gradient check failed")
# Test for specific values
def test_values(self):
x = torch.tensor([[-0.5, -0.5], [0.5, 0.5]], dtype=torch.float32)
x_after_glu = self.glu(x)
expected_values = torch.tensor([[0.075], [0.25]], dtype=torch.float32)
torch.testing.assert_close(
x_after_glu, expected_values, rtol=1e-4, atol=1e-8,
)
if __name__ == "__main__":
unittest.main()
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