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How to use _setup_session method in Molotov

Best Python code snippet using molotov_python

sparse_utils_test.py

Source:sparse_utils_test.py

...21import numpy as np22from rigl import sparse_utils23import tensorflow as tf24class GetMaskRandomTest(tf.test.TestCase, parameterized.TestCase):25  def _setup_session(self):26    """Resets the graph and returns a fresh session."""27    tf.reset_default_graph()28    sess = tf.Session()29    return sess30  @parameterized.parameters(((30, 40), 0.5), ((1, 2, 1, 4), 0.8), ((3,), 0.1))31  def testMaskConnectionDeterminism(self, shape, sparsity):32    sess = self._setup_session()33    mask = tf.ones(shape)34    mask1 = sparse_utils.get_mask_random(mask, sparsity, tf.int32)35    mask2 = sparse_utils.get_mask_random(mask, sparsity, tf.int32)36    mask1_array, = sess.run([mask1])37    mask2_array, = sess.run([mask2])38    self.assertEqual(np.sum(mask1_array), np.sum(mask2_array))39  @parameterized.parameters(((30, 4), 0.5, 60), ((1, 2, 1, 4), 0.8, 1),40                            ((30,), 0.1, 27))41  def testMaskFraction(self, shape, sparsity, expected_ones):42    sess = self._setup_session()43    mask = tf.ones(shape)44    mask1 = sparse_utils.get_mask_random(mask, sparsity, tf.int32)45    mask1_array, = sess.run([mask1])46    self.assertEqual(np.sum(mask1_array), expected_ones)47  @parameterized.parameters(tf.int32, tf.float32, tf.int64, tf.float64)48  def testMaskDtype(self, dtype):49    _ = self._setup_session()50    mask = tf.ones((3, 2))51    mask1 = sparse_utils.get_mask_random(mask, 0.5, dtype)52    self.assertEqual(mask1.dtype, dtype)53class GetSparsitiesTest(tf.test.TestCase, parameterized.TestCase):54  def _setup_session(self):55    """Resets the graph and returns a fresh session."""56    tf.reset_default_graph()57    sess = tf.Session()58    return sess59  @parameterized.parameters(0., 0.4, 0.9)60  def testSparsityDictRandom(self, default_sparsity):61    _ = self._setup_session()62    all_masks = [tf.get_variable(shape=(2, 3), name='var1/mask'),63                 tf.get_variable(shape=(2, 3), name='var2/mask'),64                 tf.get_variable(shape=(1, 1, 3), name='var3/mask')]65    custom_sparsity = {'var1': 0.8}66    sparsities = sparse_utils.get_sparsities(67        all_masks, 'random', default_sparsity, custom_sparsity)68    self.assertEqual(sparsities[all_masks[0].name], 0.8)69    self.assertEqual(sparsities[all_masks[1].name], default_sparsity)70    self.assertEqual(sparsities[all_masks[2].name], default_sparsity)71  @parameterized.parameters(0.1, 0.4, 0.9)72  def testSparsityDictErdosRenyiCustom(self, default_sparsity):73    _ = self._setup_session()74    all_masks = [tf.get_variable(shape=(2, 4), name='var1/mask'),75                 tf.get_variable(shape=(2, 3), name='var2/mask'),76                 tf.get_variable(shape=(1, 1, 3), name='var3/mask')]77    custom_sparsity = {'var3': 0.8}78    sparsities = sparse_utils.get_sparsities(79        all_masks, 'erdos_renyi', default_sparsity, custom_sparsity)80    self.assertEqual(sparsities[all_masks[2].name], 0.8)81  @parameterized.parameters(0.1, 0.4, 0.9)82  def testSparsityDictErdosRenyiError(self, default_sparsity):83    _ = self._setup_session()84    all_masks = [tf.get_variable(shape=(2, 4), name='var1/mask'),85                 tf.get_variable(shape=(2, 3), name='var2/mask'),86                 tf.get_variable(shape=(1, 1, 3), name='var3/mask')]87    custom_sparsity = {'var3': 0.8}88    sparsities = sparse_utils.get_sparsities(89        all_masks, 'erdos_renyi', default_sparsity, custom_sparsity)90    self.assertEqual(sparsities[all_masks[2].name], 0.8)91  @parameterized.parameters(((2, 3), (2, 3), 0.5),92                            ((1, 1, 2, 3), (1, 1, 2, 3), 0.3),93                            ((8, 6), (4, 3), 0.7),94                            ((80, 4), (20, 20), 0.8),95                            ((2, 6), (2, 3), 0.8))96  def testSparsityDictErdosRenyiSparsitiesScale(97      self, shape1, shape2, default_sparsity):98    _ = self._setup_session()99    all_masks = [tf.get_variable(shape=shape1, name='var1/mask'),100                 tf.get_variable(shape=shape2, name='var2/mask')]101    custom_sparsity = {}102    sparsities = sparse_utils.get_sparsities(103        all_masks, 'erdos_renyi', default_sparsity, custom_sparsity)104    sparsity1 = sparsities[all_masks[0].name]105    size1 = np.prod(shape1)106    sparsity2 = sparsities[all_masks[1].name]107    size2 = np.prod(shape2)108    # Ensure that total number of connections are similar.109    expected_zeros_uniform = (110        sparse_utils.get_n_zeros(size1, default_sparsity) +111        sparse_utils.get_n_zeros(size2, default_sparsity))112    # Ensure that total number of connections are similar....

session.py

Source:session.py

...10        fernet_key = fernet.Fernet.generate_key()11        secret_key = base64.urlsafe_b64decode(fernet_key)12        app['config']['SECRET_KEY'] = secret_key13    storage = EncryptedCookieStorage(secret_key, cookie_name='API_SESSION')...

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