85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
import os
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import sys
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import unittest
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import torch
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PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
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MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
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sys.path.append(MODULE_ROOT)
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from pytorch_wrapper.pt_std import PtStd
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class TestPtStd(unittest.TestCase):
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def setUp(self):
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"""Set up test instance."""
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self.node = PtStd()
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# Define test cases with string-based dimensions
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self.test_cases = [
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# Sample standard deviation (Bessel's correction) along dim=0
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"0",
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1,
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False,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), dim=0, correction=1, keepdim=False)
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),
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# Sample standard deviation along dim=1, with keepdim=True
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"1",
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1,
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True,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), dim=1, correction=1, keepdim=True)
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),
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# Sample standard deviation across both axes (dim=(0,1))
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"(0, 1)",
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1,
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False,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), dim=(0, 1), correction=1, keepdim=False)
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),
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# Sample standard deviation across all elements (dim=None) - Entire tensor
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"",
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1,
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False,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), correction=1, keepdim=False)
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),
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# Population standard deviation along dim=0 (correction=0)
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"0",
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0,
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False,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), dim=0, correction=0, keepdim=False)
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),
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# Population standard deviation along dim=1 with keepdim=True (correction=0)
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(
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torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
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"1",
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0,
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True,
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torch.std(torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), dim=1, correction=0, keepdim=True)
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)
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]
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def test_std(self):
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"""Test torch.std behavior."""
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for tens, dim, correction, keepdim, expected in self.test_cases:
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with self.subTest(tens=tens, dim=dim, correction=correction, keepdim=keepdim):
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result, = self.node.f(tens, dim, correction, keepdim)
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# Ensure return value is a tensor
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self.assertTrue(isinstance(result, torch.Tensor), "Output is not a tensor")
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# Ensure result matches expected standard deviation values
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self.assertTrue(torch.allclose(result, expected, atol=1e-6), f"Expected {expected}, got {result}")
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if __name__ == "__main__":
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unittest.main()
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