Python v1.function方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def get_stylize_fn():
  """Creates a tf.function for stylization."""
  input_spec = [
      tf.TensorSpec((None, None, None, 3), tf.float32),
      tf.TensorSpec((None, None, None, 3), tf.float32)
  ]
  predict_feeds = []
  predict_fetches = []

  def umbrella_function(content_img, style_img):
    predict_feeds.extend([content_img, style_img])
    predict_result = build_network(content_img, style_img)
    predict_fetches.extend([
        predict_result,
    ])
    return predict_result

  umbrella_wrapped = tf.compat.v1.wrap_function(umbrella_function, input_spec)
  fn = umbrella_wrapped.prune(predict_feeds, predict_fetches)
  return fn 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def test_context_manager(self, noise, epsilon, class_weights):
    """Tests the context manager functionality of the optimizer.

    Args:
      noise: noise distribution to pick
      epsilon: epsilon privacy parameter to use
      class_weights: class_weights to use
    """
    @tf.function
    def test_run():
      loss = TestLoss(1, 1, 1)
      bolton = opt.BoltOn(TestOptimizer(), loss)
      model = TestModel(1, (1,), 1)
      model.compile(bolton, loss)
      model.layers[0].kernel = \
        model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                            model.n_outputs))
      with bolton(noise, epsilon, model.layers, class_weights, 1, 1) as _:
        pass
      return _ops.convert_to_tensor_v2(bolton.epsilon, dtype=tf.float32)
    epsilon = test_run()
    self.assertEqual(epsilon.numpy(), -1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def test_context_domains(self, noise, epsilon, err_msg):
    """Tests the context domains.

    Args:
      noise: noise distribution to pick
      epsilon: epsilon privacy parameter to use
      err_msg: the expected error message

    """

    @tf.function
    def test_run(noise, epsilon):
      loss = TestLoss(1, 1, 1)
      bolton = opt.BoltOn(TestOptimizer(), loss)
      model = TestModel(1, (1,), 1)
      model.compile(bolton, loss)
      model.layers[0].kernel = \
        model.layers[0].kernel_initializer((model.layer_input_shape[0],
                                            model.n_outputs))
      with bolton(noise, epsilon, model.layers, 1, 1, 1) as _:
        pass
    with self.assertRaisesRegexp(ValueError, err_msg):  # pylint: disable=deprecated-method
      test_run(noise, epsilon) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def __init__(self, weight_path):
    helpers.ensure_lpips_weights_exist(weight_path)

    def wrap_frozen_graph(graph_def, inputs, outputs):
      def _imports_graph_def():
        tf.graph_util.import_graph_def(graph_def, name="")
      wrapped_import = tf.wrap_function(_imports_graph_def, [])
      import_graph = wrapped_import.graph
      return wrapped_import.prune(
          tf.nest.map_structure(import_graph.as_graph_element, inputs),
          tf.nest.map_structure(import_graph.as_graph_element, outputs))

    # Pack LPIPS network into a tf function
    graph_def = tf.GraphDef()
    with open(weight_path, "rb") as f:
      graph_def.ParseFromString(f.read())
    self._lpips_func = tf.function(
        wrap_frozen_graph(
            graph_def, inputs=("0:0", "1:0"), outputs="Reshape_10:0")) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def test_blackout_pixel_weights_by_box_regions(self):
    def graph_fn():
      boxes = tf.constant(
          [[0.0, 0.0, 5, 5], [0.0, 0.0, 10.0, 20.0], [6.0, 12.0, 8.0, 18.0]],
          dtype=tf.float32)
      blackout = tf.constant([True, False, True], dtype=tf.bool)
      blackout_pixel_weights_by_box_regions = tf.function(
          ta_utils.blackout_pixel_weights_by_box_regions)
      output = blackout_pixel_weights_by_box_regions(10, 20, boxes, blackout)
      return output

    output = self.execute(graph_fn, [])
    # All zeros in region [0:6, 0:6].
    self.assertAlmostEqual(np.sum(output[0:6, 0:6]), 0.0)
    # All zeros in region [12:19, 6:9].
    self.assertAlmostEqual(np.sum(output[6:9, 12:19]), 0.0)
    # All other pixel weights should be 1.0.
    # 20 * 10 - 6 * 6 - 3 * 7 = 143.0
    self.assertAlmostEqual(np.sum(output), 143.0) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def execute_cpu(self, compute_fn, inputs, graph=None):
    """Executes compute_fn on CPU.

    Depending on the underlying TensorFlow installation (build deps) runs in
    either TF 1.X or TF 2.X style.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single tensor.
    """
    if self.is_tf2():
      return self.execute_cpu_tf2(compute_fn, inputs)
    else:
      return self.execute_cpu_tf1(compute_fn, inputs, graph) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def execute_tpu(self, compute_fn, inputs, graph=None):
    """Executes compute_fn on TPU.

    Depending on the underlying TensorFlow installation (build deps) runs in
    either TF 1.X or TF 2.X style.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single tensor.
    """
    if not self.has_tpu():
      raise ValueError('No TPU Device found.')
    if self.is_tf2():
      return self.execute_tpu_tf2(compute_fn, inputs)
    else:
      return self.execute_tpu_tf1(compute_fn, inputs, graph) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def execute_tf2(self, compute_fn, inputs):
    """Runs compute_fn with TensorFlow 2.0.

    Executes on TPU if available, otherwise executes on CPU.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.

    Returns:
      A list of numpy arrays or a single tensor.
    """
    if not self.is_tf2():
      raise ValueError('Required version TensorFlow 2.0 is not available.')
    if self.has_tpu():
      return self.execute_tpu_tf2(compute_fn, inputs)
    else:
      return self.execute_cpu_tf2(compute_fn, inputs) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def execute_tf1(self, compute_fn, inputs, graph=None):
    """Runs compute_fn with TensorFlow 1.X.

    Executes on TPU if available, otherwise executes on CPU.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single tensor.
    """
    if self.is_tf2():
      raise ValueError('Required version Tenforflow 1.X is not available.')
    if self.has_tpu():
      return self.execute_tpu_tf1(compute_fn, inputs, graph)
    else:
      return self.execute_cpu_tf1(compute_fn, inputs, graph) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def execute(self, compute_fn, inputs, graph=None):
    """Runs compute_fn with inputs and returns results.

    * Executes in either TF1.X or TF2.X style based on the TensorFlow version.
    * Executes on TPU if available, otherwise executes on CPU.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single tensor.
    """
    if self.has_tpu() and tf2.enabled():
      return self.execute_tpu_tf2(compute_fn, inputs)
    elif not self.has_tpu() and tf2.enabled():
      return self.execute_cpu_tf2(compute_fn, inputs)
    elif self.has_tpu() and not tf2.enabled():
      return self.execute_tpu_tf1(compute_fn, inputs, graph)
    else:
      return self.execute_cpu_tf1(compute_fn, inputs, graph) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def _test_spop_placeholder_without_shape_info():
    with tf.Graph().as_default():

        @function.Defun(*[tf.int32]*2)
        def Forward(x,y):
            print(x.name)
            print(y.name)
            b = tf.add(x, y)
            return b
        pl1 = tf.placeholder(tf.int32,name="pl1")
        pl2 = tf.placeholder(tf.int32,name="pl2")
        pl3 = tf.placeholder(tf.int32, name="pl3")
        data = np.array([[-1, 1], [2, -2]], dtype=np.int32)
        data2 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
        data3 = np.array([[-2, 3], [4, -6]], dtype=np.int32)
        z1 = gen_functional_ops.StatefulPartitionedCall(args=[pl1,pl2], Tout=[tf.int32],f=Forward)
        z2 = z1 + pl3
        compare_tf_with_tvm([data, data2, data3], ['pl1:0', 'pl2:0', 'pl3:0'],
                            ['StatefulPartitionedCall:0',z2.name],  mode='vm', init_global_variables=True) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def _test_spop_function_invocation_basic():
    with tf.Graph().as_default():

        def fun1(a):
            return tf.multiply(a,a)

        def fun2(b):
            return tf.multiply(b,10)

        @tf.function
        def fun3(x,y):
            x = fun2(x)
            y = fun1(y)
            z = tf.add(x,y)
            return z

        t3 = fun3(tf.constant(10.5), tf.constant(20.4))

        compare_tf_with_tvm([], [], [t3.name], mode='vm', init_global_variables=True) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def _test_spop_function_invocation_nested():
    with tf.Graph().as_default():
        t1 = tf.placeholder(tf.int32, (3, 3, 3), name="t1")
        t1_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))
        t2 = tf.placeholder(tf.int32, name="t2")
        t2_data = np.arange(27, dtype=np.int32).reshape((3, 3, 3))

        @tf.function
        def myfunc(x, y):
            return tf.add(x, y, "myfunc")

        @tf.function
        def myfunc2(x, y):
            z = myfunc(x, y)
            l = myfunc(z, y)
            m = myfunc(l,z)
            return tf.add(l, m, "myfunc2")

        res1 = myfunc(t1, t2)
        res2 = myfunc2(res1, t1)

        compare_tf_with_tvm([t1_data, t2_data], ['t1:0', 't2:0'], [res2.name], mode='vm', init_global_variables=True) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def _test_spop_function_invocation_no_autograph():
    with tf.Graph().as_default():

        @tf.function(autograph=False)
        def fun1(a):
            return tf.multiply(a,a)

        @tf.function(autograph=False)
        def fun2(b):
            return tf.multiply(b,10)

        @tf.function
        def fun3(x,y):
            x = fun2(x)
            y = fun1(y)
            z = tf.add(x,y)
            return z

        t3 = fun3(tf.constant(10.5), tf.constant(20.4))

        compare_tf_with_tvm([], [], [t3.name], mode='vm', init_global_variables=True) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import function [as 別名]
def _test_spop_function_invocation_defun():
    with tf.Graph().as_default():

        def fun1(a):
            return tf.multiply(a,a)

        def fun2(b):
            return tf.multiply(b,b)

        @function.Defun(dtypes.float32, dtypes.float32, func_name="Fun3")
        def fun3(x,y):
            x = fun2(x)
            y = fun1(y)
            z = tf.add(x,y)
            return z

        op = gen_functional_ops.StatefulPartitionedCall(args=[tf.constant(10.5),tf.constant(20.4)],
                                                        Tout=[dtypes.float32], f=fun3, name="SpopFnInvocation")
        compare_tf_with_tvm([],[], 'SpopFnInvocation:0', mode='vm', init_global_variables=True)