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from pytorch_caney.loss.utils import to_tensor
import unittest
import numpy as np
import torch
class TestToTensorFunction(unittest.TestCase):
def test_tensor_input(self):
tensor = torch.tensor([1, 2, 3])
result = to_tensor(tensor)
self.assertTrue(torch.equal(result, tensor))
def test_tensor_input_with_dtype(self):
tensor = torch.tensor([1, 2, 3])
result = to_tensor(tensor, dtype=torch.float32)
self.assertTrue(torch.equal(result, tensor.float()))
def test_numpy_array_input(self):
numpy_array = np.array([1, 2, 3])
expected_tensor = torch.tensor([1, 2, 3])
result = to_tensor(numpy_array)
self.assertTrue(torch.equal(result, expected_tensor))
def test_numpy_array_input_with_dtype(self):
numpy_array = np.array([1, 2, 3])
expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
result = to_tensor(numpy_array, dtype=torch.float32)
self.assertTrue(torch.equal(result, expected_tensor))
def test_list_input(self):
input_list = [1, 2, 3]
expected_tensor = torch.tensor([1, 2, 3])
result = to_tensor(input_list)
self.assertTrue(torch.equal(result, expected_tensor))
def test_list_input_with_dtype(self):
input_list = [1, 2, 3]
expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
result = to_tensor(input_list, dtype=torch.float32)
self.assertTrue(torch.equal(result, expected_tensor))
if __name__ == '__main__':
unittest.main()
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