fix(test): random test was failing. Fix better seed.

The power iteration test for Dense was sometimes failing due to a random
choice of kernel and initialization vector u. Even if np.random.seed(42)
was set at the beginning of the file.

To ensure a fully deterministic test (deterministic kernel and
initialization u), a random generator np.random.default_rng(42) was created
and used to create both kernel and u.
The results for Dense test are then deterministic, either for a single test
file or for the whole unittest. Note that this is the only test modified;
other tests of the file pass, they have not been changed with deterministic
initializations.

tests/test_normalizers.py

@@ -15,7 +15,7 @@
 )
 from deel.lip.utils import _padding_circular
 
-np.random.seed(42)
+rng = np.random.default_rng(42)
 
 
 class TestSpectralNorm(unittest.TestCase):
@@ -24,11 +24,11 @@ class TestSpectralNorm(unittest.TestCase):
     def test_spectral_normalization(self):
         # Dense kernel
         kernel_shape = (15, 32)
-        kernel = np.random.normal(size=kernel_shape).astype("float32")
+        kernel = rng.normal(size=kernel_shape).astype("float32")
         self._test_kernel(kernel)
         # Dense kernel projection
         kernel_shape = (32, 15)
-        kernel = np.random.normal(size=kernel_shape).astype("float32")
+        kernel = rng.normal(size=kernel_shape).astype("float32")
         self._test_kernel(kernel)
 
     def _test_kernel(self, kernel):
@@ -40,7 +40,8 @@ def _test_kernel(self, kernel):
         ).numpy()
         SVmax = np.max(sigmas_svd)
 
-        W_bar, _u, sigma = spectral_normalization(kernel, u=None, eps=1e-6)
+        u = rng.normal(size=(1, kernel.shape[-1]))
+        W_bar, _u, sigma = spectral_normalization(kernel, u=u, eps=1e-6)
         # Test sigma is close to the one computed with svd first run @ 1e-1
         np.testing.assert_approx_equal(
             sigma, SVmax, 1, "test failed with kernel_shape " + str(kernel.shape)