function¶

dragon.vm.tensorflow.function(
  func=None,
  input_signature=None
)[source]¶

Create a callable graph from the python function.

Tensor operations could be compiled into graph:

def foo(x, y):
    return tf.add(x + y, x)

bar = tf.function(foo)
print(bar(1, 2))
print(bar(tf.constant([1, 2]), tf.constant([2, 3])))

Above usages which can simplified:

@tf.function
def foo(x, y):
    return tf.add(x + y, x)

print(foo(1, 2))
print(foo(tf.constant([1, 2]), tf.constant([2, 3])))

Some advanced layers require the tensor shape before compiling:

@tf.function
def foo(x):
    return tf.keras.layers.Conv2D(5, 3)(x)

print(foo(tf.constant(np.ones((1, 4, 4, 2)))))  # Missing shape

@tf.function(input_signature=[tf.TensorSpec([None, 4, 4, 2])])
def bar(x):
    return tf.keras.layers.Conv2D(5, 3)(x)

print(bar(tf.constant(np.ones((1, 4, 4, 2)))))  # Ok

Parameters:

• func (callable, optional) – The builtin python function.
• input_signature (Sequence[dragon.vm.tensorflow.TensorSpec], optional) – The indicators to the inputs.

Returns:

callable – The function to run the graph once.