Python deployment.model_deploy.optimize_clones() Examples

The following are 30 code examples of deployment.model_deploy.optimize_clones(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module deployment.model_deploy , or try the search function .
Example #1
Source File: model_deploy_test.py    From CBAM-tensorflow-slim with MIT License 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #2
Source File: model_deploy_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #3
Source File: model_deploy_test.py    From edafa with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #4
Source File: model_deploy_test.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #5
Source File: model_deploy_test.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #6
Source File: model_deploy_test.py    From morph-net with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #7
Source File: model_deploy_test.py    From morph-net with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #8
Source File: model_deploy_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #9
Source File: model_deploy_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #10
Source File: model_deploy_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #11
Source File: model_deploy_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #12
Source File: model_deploy_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #13
Source File: model_deploy_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #14
Source File: model_deploy_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #15
Source File: model_deploy_test.py    From CBAM-tensorflow-slim with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #16
Source File: model_deploy_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #17
Source File: model_deploy_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #18
Source File: model_deploy_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #19
Source File: model_deploy_test.py    From Creative-Adversarial-Networks with MIT License 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #20
Source File: model_deploy_test.py    From Creative-Adversarial-Networks with MIT License 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #21
Source File: model_deploy_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #22
Source File: model_deploy_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #23
Source File: model_deploy_test.py    From terngrad with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #24
Source File: model_deploy_test.py    From terngrad with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #25
Source File: model_deploy_test.py    From hops-tensorflow with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #26
Source File: model_deploy_test.py    From hops-tensorflow with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #27
Source File: model_deploy_test.py    From MobileNet with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #28
Source File: model_deploy_test.py    From MobileNet with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, '')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #29
Source File: model_deploy_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 2)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, [])

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')
Example #30
Source File: model_deploy_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(slim.get_variables()), 5)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2)

      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
      total_loss, grads_and_vars = model_deploy.optimize_clones(clones,
                                                                optimizer)
      self.assertEqual(len(grads_and_vars), len(tf.trainable_variables()))
      self.assertEqual(total_loss.op.name, 'total_loss')
      for g, v in grads_and_vars:
        self.assertDeviceEqual(g.device, 'GPU:0')
        self.assertDeviceEqual(v.device, 'CPU:0')