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import unittest
import torch
from training.loss.utils import sample_wise_min_max
class TestSampleWiseMinMax(unittest.TestCase):
def test_sample_wise_min_max(self):
# Test case 1: Test with batch size 1
x = torch.tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
normalized = sample_wise_min_max(x)
expected = torch.tensor(
[[[0.0, 0.125, 0.25], [0.375, 0.5, 0.625], [0.75, 0.875, 1.0]]],
)
self.assertTrue(torch.allclose(normalized, expected))
# Test case 2: Test with batch size 2
x = torch.tensor(
[
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
[[2, 4, 6], [8, 10, 12], [14, 16, 18]],
],
)
normalized = sample_wise_min_max(x)
expected = torch.tensor(
[
[[0.0, 0.125, 0.25], [0.375, 0.5, 0.625], [0.75, 0.875, 1.0]],
[[0.0, 0.125, 0.25], [0.375, 0.5, 0.625], [0.75, 0.875, 1.0]],
],
)
self.assertTrue(torch.allclose(normalized, expected))
# Test case 3: Test with negative values
x = torch.tensor(
[
[[-1, -2, -3], [-4, -5, -6], [-7, -8, -9]],
[[-2, -4, -6], [-8, -10, -12], [-14, -16, -18]],
],
)
normalized = sample_wise_min_max(x)
expected = torch.tensor(
[
[[1.0, 0.875, 0.75], [0.625, 0.5, 0.375], [0.25, 0.125, 0.0]],
[[1.0, 0.875, 0.75], [0.625, 0.5, 0.375], [0.25, 0.125, 0.0]],
],
)
self.assertTrue(torch.allclose(normalized, expected))
if __name__ == "__main__":
unittest.main()
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