45 lines
1.7 KiB
Python
45 lines
1.7 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_ones import PtOnes # Importing the PtOnes class
|
|
from pytorch_wrapper.utils import DTYPE_MAPPING # Importing the dtype mapping
|
|
|
|
|
|
class TestPtOnes(unittest.TestCase):
|
|
def setUp(self):
|
|
"""Set up test instance."""
|
|
self.node = PtOnes()
|
|
|
|
# Tensor size to test
|
|
self.size_str = "[2, 3]" # Using a small 2D tensor size for all tests
|
|
self.expected_shape = (2, 3)
|
|
|
|
def test_ones_tensor_all_dtypes(self):
|
|
"""Test tensor creation with ones for all supported data types."""
|
|
for data_type, torch_dtype in DTYPE_MAPPING.items():
|
|
with self.subTest(data_type=data_type):
|
|
result, = self.node.f(self.size_str, data_type)
|
|
|
|
# Ensure return value is a tensor
|
|
self.assertTrue(isinstance(result, torch.Tensor), "Output is not a tensor")
|
|
|
|
# Ensure result has correct shape
|
|
self.assertEqual(result.shape, torch.Size(self.expected_shape), f"Expected shape {self.expected_shape}, got {result.shape}")
|
|
|
|
# Ensure result has correct dtype
|
|
self.assertEqual(result.dtype, torch_dtype, f"Expected dtype {torch_dtype}, got {result.dtype}")
|
|
|
|
# Ensure all elements are ones
|
|
expected = torch.ones(self.expected_shape, dtype=torch_dtype)
|
|
self.assertTrue(torch.equal(result, expected), f"Tensor values do not match expected ones tensor for dtype {data_type}")
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|