Python tensorflow.report_uninitialized_variables方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_detection_ckpt(self, use_keras):
    model, _, _, _ = self._create_model(use_keras=use_keras)
    model.predict(tf.constant(np.array([[[[0, 0], [1, 1]], [[1, 0], [0, 1]]]],
                                       dtype=np.float32)),
                  true_image_shapes=None)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      var_map = model.restore_map(
          fine_tune_checkpoint_type='detection',
          load_all_detection_checkpoint_vars=False)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      saver.restore(sess, saved_model_path)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_detection_ckpt(self):
    model, _, _, _ = self._create_model()
    model.predict(tf.constant(np.array([[[0, 0], [1, 1]], [[1, 0], [0, 1]]],
                                       dtype=np.float32)),
                  true_image_shapes=None)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      var_map = model.restore_map(
          fine_tune_checkpoint_type='detection',
          load_all_detection_checkpoint_vars=False)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      saver.restore(sess, saved_model_path)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_detection_ckpt(self):
    model, _, _, _ = self._create_model()
    model.predict(tf.constant(np.array([[[[0, 0], [1, 1]], [[1, 0], [0, 1]]]],
                                       dtype=np.float32)),
                  true_image_shapes=None)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      var_map = model.restore_map(
          fine_tune_checkpoint_type='detection',
          load_all_detection_checkpoint_vars=False)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      saver.restore(sess, saved_model_path)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_detection_ckpt(self):
    model, _, _, _ = self._create_model()
    model.predict(tf.constant(np.array([[[0, 0], [1, 1]], [[1, 0], [0, 1]]],
                                       dtype=np.float32)),
                  true_image_shapes=None)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      var_map = model.restore_map(
          from_detection_checkpoint=True,
          load_all_detection_checkpoint_vars=False)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      saver.restore(sess, saved_model_path)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def testWaitForSessionWithReadyForLocalInitOpFailsToReadyLocal(self):
    with tf.Graph().as_default() as graph:
      v = tf.Variable(1, name="v")
      w = tf.Variable(
          v,
          trainable=False,
          collections=[tf.GraphKeys.LOCAL_VARIABLES],
          name="w")
      sm = tf.train.SessionManager(
          graph=graph,
          ready_op=tf.report_uninitialized_variables(),
          ready_for_local_init_op=tf.report_uninitialized_variables(),
          local_init_op=w.initializer)

      with self.assertRaises(tf.errors.DeadlineExceededError):
        # Time-out because w fails to be initialized,
        # because of overly restrictive ready_for_local_init_op
        sm.wait_for_session("", max_wait_secs=3) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def testWaitForSessionInsufficientReadyForLocalInitCheck(self):
    with tf.Graph().as_default() as graph:
      v = tf.Variable(1, name="v")
      w = tf.Variable(
          v,
          trainable=False,
          collections=[tf.GraphKeys.LOCAL_VARIABLES],
          name="w")
      sm = tf.train.SessionManager(
          graph=graph,
          ready_op=tf.report_uninitialized_variables(),
          ready_for_local_init_op=None,
          local_init_op=w.initializer)
    with self.assertRaisesRegexp(tf.errors.FailedPreconditionError,
                                 "Attempting to use uninitialized value v"):
      sm.wait_for_session("", max_wait_secs=3) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def testPrepareSessionDidNotInitLocalVariable(self):
    with tf.Graph().as_default():
      v = tf.Variable(1, name="v")
      w = tf.Variable(
          v,
          trainable=False,
          collections=[tf.GraphKeys.LOCAL_VARIABLES],
          name="w")
      with self.test_session():
        self.assertEqual(False, tf.is_variable_initialized(v).eval())
        self.assertEqual(False, tf.is_variable_initialized(w).eval())
      sm2 = tf.train.SessionManager(
          ready_op=tf.report_uninitialized_variables())
      with self.assertRaisesRegexp(RuntimeError,
                                   "Init operations did not make model ready"):
        sm2.prepare_session("", init_op=v.initializer) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def testPrepareSessionWithReadyNotReadyForLocal(self):
    with tf.Graph().as_default():
      v = tf.Variable(1, name="v")
      w = tf.Variable(
          v,
          trainable=False,
          collections=[tf.GraphKeys.LOCAL_VARIABLES],
          name="w")
      with self.test_session():
        self.assertEqual(False, tf.is_variable_initialized(v).eval())
        self.assertEqual(False, tf.is_variable_initialized(w).eval())
      sm2 = tf.train.SessionManager(
          ready_op=tf.report_uninitialized_variables(),
          ready_for_local_init_op=tf.report_uninitialized_variables(
              tf.all_variables()),
          local_init_op=w.initializer)
      with self.assertRaisesRegexp(
          RuntimeError,
          "Init operations did not make model ready for local_init"):
        sm2.prepare_session("", init_op=None) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_fn_detection(self):
    init_op = tf.global_variables_initializer()
    saver = tf_saver.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      restore_fn = self._model.restore_fn(saved_model_path,
                                          from_detection_checkpoint=True)
      restore_fn(sess)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var.name) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_fn_classification(self):
    # Define mock tensorflow classification graph and save variables.
    test_graph_classification = tf.Graph()
    with test_graph_classification.as_default():
      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
      with tf.variable_scope('mock_model'):
        net = slim.conv2d(image, num_outputs=32, kernel_size=1, scope='layer1')
        slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')

      init_op = tf.global_variables_initializer()
      saver = tf.train.Saver()
      save_path = self.get_temp_dir()
      with self.test_session() as sess:
        sess.run(init_op)
        saved_model_path = saver.save(sess, save_path)

    # Create tensorflow detection graph and load variables from
    # classification checkpoint.
    test_graph_detection = tf.Graph()
    with test_graph_detection.as_default():
      inputs_shape = [2, 2, 2, 3]
      inputs = tf.to_float(tf.random_uniform(
          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs = self._model.preprocess(inputs)
      prediction_dict = self._model.predict(preprocessed_inputs)
      self._model.postprocess(prediction_dict)
      restore_fn = self._model.restore_fn(saved_model_path,
                                          from_detection_checkpoint=False)
      with self.test_session() as sess:
        restore_fn(sess)
        for var in sess.run(tf.report_uninitialized_variables()):
          self.assertNotIn('FeatureExtractor', var.name) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_fn_detection(self):
    # Define first detection graph and save variables.
    test_graph_detection1 = tf.Graph()
    with test_graph_detection1.as_default():
      model = self._build_model(
          is_training=False, first_stage_only=False, second_stage_batch_size=6)
      inputs_shape = (2, 20, 20, 3)
      inputs = tf.to_float(tf.random_uniform(
          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs = model.preprocess(inputs)
      prediction_dict = model.predict(preprocessed_inputs)
      model.postprocess(prediction_dict)
      init_op = tf.global_variables_initializer()
      saver = tf.train.Saver()
      save_path = self.get_temp_dir()
      with self.test_session() as sess:
        sess.run(init_op)
        saved_model_path = saver.save(sess, save_path)

    # Define second detection graph and restore variables.
    test_graph_detection2 = tf.Graph()
    with test_graph_detection2.as_default():
      model2 = self._build_model(is_training=False, first_stage_only=False,
                                 second_stage_batch_size=6, num_classes=42)

      inputs_shape2 = (2, 20, 20, 3)
      inputs2 = tf.to_float(tf.random_uniform(
          inputs_shape2, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs2 = model2.preprocess(inputs2)
      prediction_dict2 = model2.predict(preprocessed_inputs2)
      model2.postprocess(prediction_dict2)
      restore_fn = model2.restore_fn(saved_model_path,
                                     from_detection_checkpoint=True)
      with self.test_session() as sess:
        restore_fn(sess)
        for var in sess.run(tf.report_uninitialized_variables()):
          self.assertNotIn(model2.first_stage_feature_extractor_scope, var.name)
          self.assertNotIn(model2.second_stage_feature_extractor_scope,
                           var.name) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_classification_ckpt(self):
    # Define mock tensorflow classification graph and save variables.
    test_graph_classification = tf.Graph()
    with test_graph_classification.as_default():
      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
      with tf.variable_scope('mock_model'):
        net = slim.conv2d(image, num_outputs=3, kernel_size=1, scope='layer1')
        slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')

      init_op = tf.global_variables_initializer()
      saver = tf.train.Saver()
      save_path = self.get_temp_dir()
      with self.test_session(graph=test_graph_classification) as sess:
        sess.run(init_op)
        saved_model_path = saver.save(sess, save_path)

    # Create tensorflow detection graph and load variables from
    # classification checkpoint.
    test_graph_detection = tf.Graph()
    with test_graph_detection.as_default():
      model = self._build_model(
          is_training=False, number_of_stages=2, second_stage_batch_size=6)

      inputs_shape = (2, 20, 20, 3)
      inputs = tf.to_float(tf.random_uniform(
          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
      model.postprocess(prediction_dict, true_image_shapes)
      var_map = model.restore_map(fine_tune_checkpoint_type='classification')
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      with self.test_session(graph=test_graph_classification) as sess:
        saver.restore(sess, saved_model_path)
        for var in sess.run(tf.report_uninitialized_variables()):
          self.assertNotIn(model.first_stage_feature_extractor_scope, var)
          self.assertNotIn(model.second_stage_feature_extractor_scope, var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_classification_ckpt(self):
    # Define mock tensorflow classification graph and save variables.
    test_graph_classification = tf.Graph()
    with test_graph_classification.as_default():
      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
      with tf.variable_scope('mock_model'):
        net = slim.conv2d(image, num_outputs=32, kernel_size=1, scope='layer1')
        slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')

      init_op = tf.global_variables_initializer()
      saver = tf.train.Saver()
      save_path = self.get_temp_dir()
      with self.test_session(graph=test_graph_classification) as sess:
        sess.run(init_op)
        saved_model_path = saver.save(sess, save_path)

    # Create tensorflow detection graph and load variables from
    # classification checkpoint.
    test_graph_detection = tf.Graph()
    with test_graph_detection.as_default():
      model, _, _, _ = self._create_model()
      inputs_shape = [2, 2, 2, 3]
      inputs = tf.to_float(tf.random_uniform(
          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs, true_image_shapes = model.preprocess(inputs)
      prediction_dict = model.predict(preprocessed_inputs, true_image_shapes)
      model.postprocess(prediction_dict, true_image_shapes)
      another_variable = tf.Variable([17.0], name='another_variable')  # pylint: disable=unused-variable
      var_map = model.restore_map(fine_tune_checkpoint_type='classification')
      self.assertNotIn('another_variable', var_map)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      with self.test_session(graph=test_graph_detection) as sess:
        saver.restore(sess, saved_model_path)
        for var in sess.run(tf.report_uninitialized_variables()):
          self.assertNotIn('FeatureExtractor', var) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_detection_ckpt(self):
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    save_path = self.get_temp_dir()
    with self.test_session() as sess:
      sess.run(init_op)
      saved_model_path = saver.save(sess, save_path)
      var_map = self._model.restore_map(from_detection_checkpoint=True)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      saver.restore(sess, saved_model_path)
      for var in sess.run(tf.report_uninitialized_variables()):
        self.assertNotIn('FeatureExtractor', var.name) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import report_uninitialized_variables [as 别名]
def test_restore_map_for_classification_ckpt(self):
    # Define mock tensorflow classification graph and save variables.
    test_graph_classification = tf.Graph()
    with test_graph_classification.as_default():
      image = tf.placeholder(dtype=tf.float32, shape=[1, 20, 20, 3])
      with tf.variable_scope('mock_model'):
        net = slim.conv2d(image, num_outputs=32, kernel_size=1, scope='layer1')
        slim.conv2d(net, num_outputs=3, kernel_size=1, scope='layer2')

      init_op = tf.global_variables_initializer()
      saver = tf.train.Saver()
      save_path = self.get_temp_dir()
      with self.test_session() as sess:
        sess.run(init_op)
        saved_model_path = saver.save(sess, save_path)

    # Create tensorflow detection graph and load variables from
    # classification checkpoint.
    test_graph_detection = tf.Graph()
    with test_graph_detection.as_default():
      inputs_shape = [2, 2, 2, 3]
      inputs = tf.to_float(tf.random_uniform(
          inputs_shape, minval=0, maxval=255, dtype=tf.int32))
      preprocessed_inputs = self._model.preprocess(inputs)
      prediction_dict = self._model.predict(preprocessed_inputs)
      self._model.postprocess(prediction_dict)
      var_map = self._model.restore_map(from_detection_checkpoint=False)
      self.assertIsInstance(var_map, dict)
      saver = tf.train.Saver(var_map)
      with self.test_session() as sess:
        saver.restore(sess, saved_model_path)
        for var in sess.run(tf.report_uninitialized_variables()):
          self.assertNotIn('FeatureExtractor', var.name)