58 lines
2.1 KiB
Python
58 lines
2.1 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_squeeze import PtSqueeze # Importing the PtSqueeze class
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class TestPtSqueeze(unittest.TestCase):
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def setUp(self):
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"""Set up test instance."""
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self.node = PtSqueeze()
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# Define test cases: (input_tensor, dim, expected_shape)
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self.test_cases = [
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# Case 1: Squeezing a singleton dimension
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(torch.ones(1, 3, 4), 0, (3, 4)), # (1,3,4) -> (3,4)
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(torch.ones(2, 1, 4), 1, (2, 4)), # (2,1,4) -> (2,4)
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# Case 2: No change if dim is not a singleton
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(torch.ones(2, 3, 4), 1, (2, 3, 4)), # (2,3,4) -> (2,3,4)
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# Case 3: Multiple singleton dimensions, squeezing one
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(torch.ones(1, 2, 1, 4), 2, (1, 2, 4)), # (1,2,1,4) -> (1,2,4)
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# Case 4: Squeezing a negative index
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(torch.ones(2, 3, 1), -1, (2, 3)), # (2,3,1) -> (2,3)
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# Case 5: Squeezing all size 1 dimensions
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(torch.ones(1, 1, 1), 0, (1, 1)), # (1,1,1) -> (1,1)
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(torch.ones(1, 1, 1), 1, (1, 1)), # (1,1,1) -> (1,1)
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(torch.ones(1, 1, 1), 2, (1, 1)), # (1,1,1) -> (1,1)
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]
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def test_squeeze(self):
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"""Test tensor squeeze operation on various dimensions."""
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for input_tensor, dim, expected_shape in self.test_cases:
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with self.subTest(input_shape=input_tensor.shape, dim=dim):
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result, = self.node.f(input_tensor, dim)
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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 has correct shape
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self.assertEqual(result.shape, torch.Size(expected_shape), f"Expected shape {expected_shape}, got {result.shape}")
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# Ensure result has the same dtype
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self.assertEqual(result.dtype, input_tensor.dtype, "Dtype mismatch after squeeze operation")
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if __name__ == "__main__":
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unittest.main()
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