75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
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])
|
|
|
|
@ndarray_lru_cache(max_size=128)
|
|
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() |