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import unittest
import torch
from models.helpers import (
stride_lens_downsampling,
)
class TestStrideLens(unittest.TestCase):
def test_stride_lens(self):
# Define test case inputs
input_lengths = torch.tensor([5, 7, 10, 12])
stride = 2
# Correct output for this would be ceil([5, 7, 10, 12] / 2) => [3, 4, 5, 6]
expected_output = torch.tensor([3, 4, 5, 6])
# Call the function with the test cases
output = stride_lens_downsampling(input_lengths, stride)
# Check if the output is a tensor
self.assertIsInstance(output, torch.Tensor)
# Check if the output shape is as expected
self.assertEqual(output.shape, expected_output.shape)
# Check if the output values are as expected
self.assertTrue(torch.all(output.eq(expected_output)))
def test_stride_lens_default_stride(self):
# Define test case inputs. Here, we do not provide the stride.
input_lengths = torch.tensor([10, 20, 4, 11])
# Correct output for this would be ceil([10, 20, 4, 11] / 2) => [5, 10, 2, 6]
expected_output = torch.tensor([5, 10, 2, 6])
# Call the function with the test cases
output = stride_lens_downsampling(input_lengths)
# Check if the output is a tensor
self.assertIsInstance(output, torch.Tensor)
# Check if the output shape is as expected
self.assertEqual(output.shape, expected_output.shape)
# Check if the output values are as expected
self.assertTrue(torch.all(output.eq(expected_output)))
if __name__ == "__main__":
unittest.main()
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