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def_function.py
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def_function.py
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# ------------------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech. All Rights Reserved.
#
# Licensed under the BSD 2-Clause License,
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-2-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Function engine."""
from dragon.core.autograph import function_lib
def function(func=None, input_signature=None):
"""Create a callable graph from the python function.
Tensor operations could be compiled into graph:
```python
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:
```python
@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:
```python
@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.
"""
return function_lib.function(func, input_signature)