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