Python deployment.model_deploy.deploy() Examples

The following are 30 code examples of deployment.model_deploy.deploy(). 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 TwinGAN with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #2
Source File: model_deploy_test.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #3
Source File: model_deploy_test.py    From Action_Recognition_Zoo with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #4
Source File: model_deploy_test.py    From Cross-Modal-Projection-Learning with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #5
Source File: model_deploy_test.py    From YOLO2TensorFlow with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #6
Source File: model_deploy_test.py    From YOLO2TensorFlow with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #7
Source File: model_deploy_test.py    From Cross-Modal-Projection-Learning with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #8
Source File: model_deploy_test.py    From Machine-Learning-with-TensorFlow-1.x with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #9
Source File: model_deploy_test.py    From Machine-Learning-with-TensorFlow-1.x with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #10
Source File: model_deploy_test.py    From hands-detection with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #11
Source File: model_deploy_test.py    From hands-detection with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #12
Source File: model_deploy_test.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #13
Source File: model_deploy_test.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #14
Source File: model_deploy_test.py    From MBMD with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #15
Source File: model_deploy_test.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #16
Source File: model_deploy_test.py    From TwinGAN with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #17
Source File: model_deploy_test.py    From tumblr-emotions with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #18
Source File: model_deploy_test.py    From tumblr-emotions with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #19
Source File: model_deploy_test.py    From tensorflow_yolo2 with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #20
Source File: model_deploy_test.py    From tensorflow_yolo2 with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #21
Source File: model_deploy_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #22
Source File: model_deploy_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #23
Source File: model_deploy_test.py    From MobileNet with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #24
Source File: model_deploy_test.py    From MobileNet with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #25
Source File: model_deploy_test.py    From hops-tensorflow with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #26
Source File: model_deploy_test.py    From terngrad with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #27
Source File: model_deploy_test.py    From terngrad with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #28
Source File: model_deploy_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #29
Source File: model_deploy_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)
Example #30
Source File: model_deploy_test.py    From Creative-Adversarial-Networks with MIT License 6 votes vote down vote up 
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device)