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| import importlib | |
| utils = importlib.import_module('extensions.sd-webui-controlnet.tests.utils', 'utils') | |
| utils.setup_test_env() | |
| from scripts.utils import ndarray_lru_cache, get_unique_axis0 | |
| import unittest | |
| import numpy as np | |
| class TestNumpyLruCache(unittest.TestCase): | |
| def setUp(self): | |
| self.arr1 = np.array([1, 2, 3, 4, 5]) | |
| self.arr2 = np.array([1, 2, 3, 4, 5]) | |
| def add_one(self, arr): | |
| return arr + 1 | |
| def test_same_array(self): | |
| # Test that the decorator works with numpy arrays. | |
| result1 = self.add_one(self.arr1) | |
| result2 = self.add_one(self.arr1) | |
| # If caching is working correctly, these should be the same object. | |
| self.assertIs(result1, result2) | |
| def test_different_array_same_data(self): | |
| # Test that the decorator works with different numpy arrays with the same data. | |
| result1 = self.add_one(self.arr1) | |
| result2 = self.add_one(self.arr2) | |
| # If caching is working correctly, these should be the same object. | |
| self.assertIs(result1, result2) | |
| def test_cache_size(self): | |
| # Test that the cache size limit is respected. | |
| arrs = [np.array([i]) for i in range(150)] | |
| # Add all arrays to the cache. | |
| result1 = self.add_one(arrs[0]) | |
| for arr in arrs[1:]: | |
| self.add_one(arr) | |
| # Check that the first array is no longer in the cache. | |
| result2 = self.add_one(arrs[0]) | |
| # If the cache size limit is working correctly, these should not be the same object. | |
| self.assertIsNot(result1, result2) | |
| def test_large_array(self): | |
| # Create two large arrays with the same elements in the beginning and end, but one different element in the middle. | |
| arr1 = np.ones(10000) | |
| arr2 = np.ones(10000) | |
| arr2[len(arr2)//2] = 0 | |
| result1 = self.add_one(arr1) | |
| result2 = self.add_one(arr2) | |
| # If hashing is working correctly, these should not be the same object because the input arrays are not equal. | |
| self.assertIsNot(result1, result2) | |
| class TestUniqueFunctions(unittest.TestCase): | |
| def test_get_unique_axis0(self): | |
| data = np.random.randint(0, 100, size=(100000, 3)) | |
| data = np.concatenate((data, data)) | |
| numpy_unique_res = np.unique(data, axis=0) | |
| get_unique_axis0_res = get_unique_axis0(data) | |
| self.assertEqual(np.array_equal( | |
| np.sort(numpy_unique_res, axis=0), np.sort(get_unique_axis0_res, axis=0), | |
| ), True) | |
| if __name__ == '__main__': | |
| unittest.main() |