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

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HowToSD
2025-02-24 21:50:18 -08:00
parent 4a0d561d8f
commit 17a40d7798
30 changed files with 1042 additions and 1 deletions
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tests/pytorch_wrapper/__init__.py
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tests/pytorch_wrapper/pt_float_create_test.py
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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()
tests/pytorch_wrapper/pt_from_numpy_test.py
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import os
import sys
import unittest
import torch
import numpy as np
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_from_numpy import PtFromNumpy
class TestPtFromNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtFromNumpy()
# Test cases covering rank 0 (scalar) to rank 3 tensors
self.test_cases = [
# Rank 0 (scalar) int32
(np.array(12345, dtype=np.int32), torch.tensor(12345, dtype=torch.int32)),
# Rank 0 (scalar) float32
(np.array(12345.6, dtype=np.float32), torch.tensor(12345.6, dtype=torch.float32)),
# Rank 1 (1D array) int32
(np.array([1, 2, 3], dtype=np.int32), torch.tensor([1, 2, 3], dtype=torch.int32)),
# Rank 1 (1D array) float32
(np.array([1.1, 2.2, 3.3], dtype=np.float32), torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32)),
# Rank 2 (2D matrix) int32
(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32), torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32)),
# Rank 2 (2D matrix) float32
(np.array([[1.1, 2.2], [3.3, 4.4]], dtype=np.float32), torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32)),
# Rank 3 (3D tensor) int32
(np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.int32),
torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32)),
# Rank 3 (3D tensor) float32
(np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=np.float32),
torch.tensor([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=torch.float32)),
]
def test_convert_numpy_to_tensor(self):
"""Test conversion of NumPy ndarray to PyTorch tensor."""
for np_array, expected_tensor in self.test_cases:
with self.subTest(data=np_array):
retval = self.node.f(np_array)[0]
# Ensure the returned value is a PyTorch tensor
self.assertTrue(isinstance(retval, torch.Tensor), f"Expected tensor, got {type(retval)}")
# Ensure the dtype matches
self.assertEqual(retval.dtype, expected_tensor.dtype, f"Dtype mismatch for input: {np_array}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_tensor.shape,
f"Shape mismatch for input: {np_array} (Expected {expected_tensor.shape}, got {retval.shape})")
# Ensure values match
torch.testing.assert_close(retval, expected_tensor,
msg=f"Value mismatch for input: {np_array}")
if __name__ == "__main__":
unittest.main()
tests/pytorch_wrapper/pt_int_create_test.py
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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_int_create import PtIntCreate
class TestPtIntCreate(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtIntCreate()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar)
("12345", torch.tensor(12345, dtype=torch.int32)),
# Rank 1 (1D array)
("[1, 2, 3]", torch.tensor([1, 2, 3], dtype=torch.int32)),
# Rank 2 (2D matrix)
("[[1, 2, 3], [4, 5, 6]]", torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32)),
# Rank 3 (3D tensor)
("[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]",
torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32)),
# Rank 4 (4D tensor)
("[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]]",
torch.tensor([[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]], dtype=torch.int32)),
# Rank 5 (5D tensor)
("[[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]]",
torch.tensor([[[[[1], [2]], [[3], [4]]]], [[[[5], [6]], [[7], [8]]]]], dtype=torch.int32)),
]
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 int32
self.assertEqual(retval.dtype, torch.int32, 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()
tests/pytorch_wrapper/pt_to_numpy_test.py
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import os
import sys
import unittest
import torch
import numpy as np
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_to_numpy import PtToNumpy
class TestPtToNumpy(unittest.TestCase):
def setUp(self):
"""Set up test data."""
self.node = PtToNumpy()
# Test cases covering rank 0 (scalar) to rank 5 tensors
self.test_cases = [
# Rank 0 (scalar) int32
(torch.tensor(12345, dtype=torch.int32), np.array(12345, dtype=np.int32)),
# Rank 0 (scalar) float32
(torch.tensor(12345.6, dtype=torch.float32), np.array(12345.6, dtype=np.float32)),
# Rank 1 (1D array) int32
(torch.tensor([1, 2, 3], dtype=torch.int32), np.array([1, 2, 3], dtype=np.int32)),
# Rank 1 (1D array) float32
(torch.tensor([1.1, 2.2, 3.3], dtype=torch.float32), np.array([1.1, 2.2, 3.3], dtype=np.float32)),
# Rank 2 (2D matrix) int32
(torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32), np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)),
# Rank 2 (2D matrix) float32
(torch.tensor([[1.1, 2.2], [3.3, 4.4]], dtype=torch.float32), np.array([[1.1, 2.2], [3.3, 4.4]], dtype=np.float32)),
# Rank 3 (3D tensor) int32
(torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=torch.int32),
np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.int32)),
# Rank 3 (3D tensor) float32
(torch.tensor([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=torch.float32),
np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]], dtype=np.float32)),
]
def test_convert_tensor_to_numpy(self):
"""Test conversion of PyTorch tensor to NumPy ndarray."""
for torch_tensor, expected_array in self.test_cases:
with self.subTest(data=torch_tensor):
retval = self.node.f(torch_tensor)[0]
# Ensure the returned value is a NumPy array
self.assertTrue(isinstance(retval, np.ndarray), f"Expected ndarray, got {type(retval)}")
# Ensure the dtype matches
self.assertEqual(retval.dtype, expected_array.dtype, f"Dtype mismatch for input: {torch_tensor}")
# Ensure the shape matches
self.assertEqual(retval.shape, expected_array.shape,
f"Shape mismatch for input: {torch_tensor} (Expected {expected_array.shape}, got {retval.shape})")
# Ensure values match
np.testing.assert_array_equal(retval, expected_array,
f"Value mismatch for input: {torch_tensor}")
if __name__ == "__main__":
unittest.main()