Initial phase of adding PyTorch support for data analysis. Added conversion nodes.

tests/pytorch_wrapper/pt_float_create_test.py

@@ -0,0 +1,65 @@
+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_float_create import PtFloatCreate
+
+class TestPtFloatCreate(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.node = PtFloatCreate()
+
+        # Test cases covering rank 0 (scalar) to rank 5 tensors
+        self.test_cases = [
+            # Rank 0 (scalar)
+            ("12345", torch.tensor(12345, dtype=torch.float32)),
+            ("12345.6", torch.tensor(12345.6, dtype=torch.float32)),
+
+            # Rank 1 (1D array)
+            ("[1, 2, 3]", torch.tensor([1, 2, 3], dtype=torch.float32)),
+            ("[1.1, 2.2, 3.3]", torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32)),
+
+            # Rank 2 (2D matrix)
+            ("[[1, 2, 3], [4, 5, 6]]", torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32)),
+            ("[[1.1, 2.2], [3.3, 4.4]]", torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32)),
+
+            # Rank 3 (3D tensor)
+            ("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]", 
+             torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.float32)),
+            
+            # Rank 4 (4D tensor)
+            ("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]", 
+             torch.tensor([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=torch.float32)),
+
+            # Rank 5 (5D tensor)
+            ("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]", 
+             torch.tensor([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=torch.float32)),
+        ]
+
+    def test_create_tensor(self):
+        """Test PyTorch tensor creation from various structured inputs."""
+        for data_str, expected_tensor in self.test_cases:
+            with self.subTest(data=data_str):
+                retval = self.node.f(data_str)[0]
+
+                # Ensure the returned value is a PyTorch tensor
+                self.assertTrue(isinstance(retval, torch.Tensor), f"Expected tensor, got {type(retval)}")
+
+                # Ensure the dtype is float32
+                self.assertEqual(retval.dtype, torch.float32, f"Dtype mismatch for input: {data_str}")
+
+                # Ensure the shape matches
+                self.assertEqual(retval.shape, expected_tensor.shape, 
+                                 f"Shape mismatch for input: {data_str} (Expected {expected_tensor.shape}, got {retval.shape})")
+
+                # Ensure values match
+                torch.testing.assert_close(retval, expected_tensor, 
+                                           msg=f"Value mismatch for input: {data_str}")
+
+if __name__ == "__main__":
+    unittest.main()