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import unittest
import numpy as np
import torch
from training.tools import pad_1D, pad_2D, pad_3D
class TestPad(unittest.TestCase):
def test_pad_1D(self):
# Test case 1: Pad a list of 1D numpy arrays with different lengths
inputs = [torch.tensor([1, 2, 3]), torch.tensor([4, 5]), torch.tensor([6])]
expected_output = torch.tensor([[1, 2, 3], [4, 5, 0], [6, 0, 0]])
self.assertTrue(torch.allclose(pad_1D(inputs), expected_output))
# Test case 2: Pad a list of 1D numpy arrays with the same length
inputs = [torch.tensor([1, 2, 3]), torch.tensor([4, 5, 6]), torch.tensor([7, 8, 9])]
expected_output = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
self.assertTrue(torch.allclose(pad_1D(inputs), expected_output))
# Test case 3: Pad a list of 1D numpy arrays with a non-zero pad value
inputs = [torch.tensor([1, 2]), torch.tensor([3, 4, 5]), torch.tensor([6, 7, 8, 9])]
expected_output = torch.tensor([[1, 2, 0, 0], [3, 4, 5, 0], [6, 7, 8, 9]])
self.assertTrue(torch.allclose(pad_1D(inputs, pad_value=0.0), expected_output))
# Test case 4: Pad a list of 1D numpy arrays with a non-zero pad value
inputs = [torch.tensor([1, 2]), torch.tensor([3, 4, 5]), torch.tensor([6, 7, 8, 9])]
expected_output = torch.tensor([[1, 2, 1, 1], [3, 4, 5, 1], [6, 7, 8, 9]])
self.assertTrue(torch.allclose(pad_1D(inputs, pad_value=1.0), expected_output))
# Test case 5: Pad a list of 1D numpy arrays with a single non-empty array
inputs = [torch.tensor([1, 2, 3])]
expected_output = torch.tensor([[1, 2, 3]])
self.assertTrue(torch.allclose(pad_1D(inputs), expected_output))
def test_pad_2D(self):
# Test case 1: Pad a list of 2D numpy arrays with different shapes
inputs = [torch.tensor([[1, 2], [3, 4]]), torch.tensor([[5, 6, 7], [8, 9, 10]])]
expected_output = torch.tensor([[[1, 2, 0], [3, 4, 0]], [[5, 6, 7], [8, 9, 10]]])
self.assertTrue(torch.allclose(pad_2D(inputs), expected_output))
# Test case 2: Pad a list of 2D numpy arrays with the same shape
inputs = [torch.tensor([[1, 2], [3, 4]]), torch.tensor([[5, 6], [7, 8]])]
expected_output = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
self.assertTrue(torch.allclose(pad_2D(inputs), expected_output))
# Test case 3: Pad a list of 2D numpy arrays with a non-zero pad value
inputs = [torch.tensor([[1, 2], [3, 4]]), torch.tensor([[5, 6, 7], [8, 9, 10]])]
expected_output = torch.tensor([[[1, 2, 1], [3, 4, 1]], [[5, 6, 7], [8, 9, 10]]])
self.assertTrue(torch.allclose(pad_2D(inputs, pad_value=1.0), expected_output))
# Test case 4: Pad a list of 2D numpy arrays with a maximum length
inputs = [torch.tensor([[1, 2], [3, 4]]), torch.tensor([[5, 6, 7], [8, 9, 10]])]
expected_output = torch.tensor([[[1, 2, 0], [3, 4, 0]], [[5, 6, 7], [8, 9, 10]]])
self.assertTrue(torch.allclose(pad_2D(inputs, maxlen=3), expected_output))
# Test case 5: Pad a list of 2D numpy arrays with a single non-empty array
inputs = [torch.tensor([[1, 2], [3, 4]])]
expected_output = torch.tensor([[[1, 2], [3, 4]]])
self.assertTrue(torch.allclose(pad_2D(inputs), expected_output))
def test_pad_3D(self):
# Test case 1: Pad a 3D numpy array with different dimensions
inputs = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]])
expected_output = torch.tensor(
[
[[1, 2, 0], [3, 4, 0], [0, 0, 0]],
[[5, 6, 0], [7, 8, 0], [0, 0, 0]],
[[9, 10, 0], [11, 12, 0], [0, 0, 0]],
],
)
self.assertTrue(torch.allclose(pad_3D(inputs, B=3, T=3, L=3), expected_output))
# Test case 2: Pad a 3D numpy array with the same dimensions
inputs = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]])
expected_output = torch.tensor(
[
[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[9, 10], [11, 12]],
],
)
self.assertTrue(torch.allclose(pad_3D(inputs, B=3, T=2, L=2), expected_output))
# Test case 3: Pad a 3D numpy array with a single element
inputs = torch.tensor([[[1, 2], [3, 4]]])
expected_output = torch.tensor([[[1, 2, 0], [3, 4, 0]]])
self.assertTrue(torch.allclose(pad_3D(inputs, B=1, T=2, L=3), expected_output))
# Test case: Pad a list of 3D numpy arrays with different dimensions
inputs = [
torch.tensor([[1, 2], [3, 4]]),
torch.tensor([[5, 6], [7, 8], [9, 10]]),
torch.tensor([[11, 12], [13, 14], [15, 16]]),
]
expected_output = torch.tensor(
[
[[1, 2, 0], [3, 4, 0], [0, 0, 0], [0, 0, 0]],
[[5, 6, 0], [7, 8, 0], [9, 10, 0], [0, 0, 0]],
[[11, 12, 0], [13, 14, 0], [15, 16, 0], [0, 0, 0]],
],
)
self.assertTrue(torch.allclose(pad_3D(inputs, B=3, T=4, L=3), expected_output))
if __name__ == "__main__":
unittest.main()
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