def tf_buffer(data=None):
"""Context manager that creates and deletes TF_Buffer.
Example usage:
with tf_buffer() as buf:
# get serialized graph def into buf
...
proto_data = c_api.TF_GetBuffer(buf)
graph_def.ParseFromString(compat.as_bytes(proto_data))
# buf has been deleted
with tf_buffer(some_string) as buf:
c_api.TF_SomeFunction(buf)
# buf has been deleted
Args:
data: An optional `bytes`, `str`, or `unicode` object. If not None, the
yielded buffer will contain this data.
Yields:
Created TF_Buffer
"""
if data:
buf = c_api.TF_NewBufferFromString(compat.as_bytes(data))
else:
buf = c_api.TF_NewBuffer()
try:
yield buf
finally:
c_api.TF_DeleteBuffer(buf)
def get_api_def(self, op_name):
api_def_proto = api_def_pb2.ApiDef()
buf = c_api.TF_ApiDefMapGet(self._api_def_map, op_name, len(op_name))
try:
api_def_proto.ParseFromString(c_api.TF_GetBuffer(buf))
finally:
c_api.TF_DeleteBuffer(buf)
return api_def_proto
def __init__(self):
op_def_proto = op_def_pb2.OpList()
buf = c_api.TF_GetAllOpList()
try:
op_def_proto.ParseFromString(c_api.TF_GetBuffer(buf))
self._api_def_map = c_api.TF_NewApiDefMap(buf)
finally:
c_api.TF_DeleteBuffer(buf)
self._op_per_name = {}
for op in op_def_proto.op:
self._op_per_name[op.name] = op
def __hook__(self, session, handle, args, status, run_metadata_ptr):
assert run_metadata_ptr is None # TODO(levosos): handle this
run_metadata_ptr = tf_c_api.TF_NewBuffer()
result = self.__original__(session, handle, args, status,
run_metadata_ptr)
proto = tf_c_api.TF_GetBuffer(run_metadata_ptr)
tf_c_api.TF_DeleteBuffer(run_metadata_ptr)
run_metadata = tensorflow.RunMetadata()
run_metadata.ParseFromString(compat.as_bytes(proto))
self._update(run_metadata)
return result
def tf_buffer():
"""Context manager that creates and deletes TF_Buffer.
Example usage:
wtih tf_buffer() as buf:
# get serialized graph def into buf
...
proto_data = c_api.TF_GetBuffer(buf)
graph_def.ParseFromString(compat.as_bytes(proto_data))
# buf has been deleted
Yields:
Created TF_Buffer
"""
buf = c_api.TF_NewBuffer()
try:
yield buf
finally:
c_api.TF_DeleteBuffer(buf)
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""Runs operations and evaluates tensors in `fetches`.
This method runs one "step" of TensorFlow computation, by
running the necessary graph fragment to execute every `Operation`
and evaluate every `Tensor` in `fetches`, substituting the values in
`feed_dict` for the corresponding input values.
The `fetches` argument may be a single graph element, or an arbitrarily
nested list, tuple, namedtuple, dict, or OrderedDict containing graph
elements at its leaves. A graph element can be one of the following types:
* An [`Operation`](../../api_docs/python/framework.md#Operation).
The corresponding fetched value will be `None`.
* A [`Tensor`](../../api_docs/python/framework.md#Tensor).
The corresponding fetched value will be a numpy ndarray containing the
value of that tensor.
* A [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor).
The corresponding fetched value will be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue)
containing the value of that sparse tensor.
* A `get_tensor_handle` op. The corresponding fetched value will be a
numpy ndarray containing the handle of that tensor.
* A `string` which is the name of a tensor or operation in the graph.
The value returned by `run()` has the same shape as the `fetches` argument,
where the leaves are replaced by the corresponding values returned by
TensorFlow.
Example:
```python
a = tf.constant([10, 20])
b = tf.constant([1.0, 2.0])
# 'fetches' can be a singleton
v = session.run(a)
# v is the numpy array [10, 20]
# 'fetches' can be a list.
v = session.run([a, b])
# v a Python list with 2 numpy arrays: the numpy array [10, 20] and the
# 1-D array [1.0, 2.0]
# 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:
MyData = collections.namedtuple('MyData', ['a', 'b'])
v = session.run({'k1': MyData(a, b), 'k2': [b, a]})
# v is a dict with
# v['k1'] is a MyData namedtuple with 'a' the numpy array [10, 20] and
# 'b' the numpy array [1.0, 2.0]
# v['k2'] is a list with the numpy array [1.0, 2.0] and the numpy array
# [10, 20].
```
The optional `feed_dict` argument allows the caller to override
the value of tensors in the graph. Each key in `feed_dict` can be
one of the following types:
* If the key is a [`Tensor`](../../api_docs/python/framework.md#Tensor), the
value may be a Python scalar, string, list, or numpy ndarray
that can be converted to the same `dtype` as that
tensor. Additionally, if the key is a
[placeholder](../../api_docs/python/io_ops.md#placeholder), the shape of
the value will be checked for compatibility with the placeholder.
* If the key is a
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
the value should be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue).
* If the key is a nested tuple of `Tensor`s or `SparseTensor`s, the value
should be a nested tuple with the same structure that maps to their
corresponding values as above.
Each value in `feed_dict` must be convertible to a numpy array of the dtype
of the corresponding key.
The optional `options` argument expects a [`RunOptions`] proto. The options
allow controlling the behavior of this particular step (e.g. turning tracing
on).
The optional `run_metadata` argument expects a [`RunMetadata`] proto. When
appropriate, the non-Tensor output of this step will be collected there. For
example, when users turn on tracing in `options`, the profiled info will be
collected into this argument and passed back.
Args:
fetches: A single graph element, a list of graph elements,
or a dictionary whose values are graph elements or lists of graph
elements (described above).
feed_dict: A dictionary that maps graph elements to values
(described above).
options: A [`RunOptions`] protocol buffer
run_metadata: A [`RunMetadata`] protocol buffer
Returns:
Either a single value if `fetches` is a single graph element, or
a list of values if `fetches` is a list, or a dictionary with the
same keys as `fetches` if that is a dictionary (described above).
Raises:
RuntimeError: If this `Session` is in an invalid state (e.g. has been
closed).
TypeError: If `fetches` or `feed_dict` keys are of an inappropriate type.
ValueError: If `fetches` or `feed_dict` keys are invalid or refer to a
`Tensor` that doesn't exist.
"""
run_metadata_ptr = tf_session.TF_NewBuffer()
if options:
options_ptr = tf_session.TF_NewBufferFromString(
compat.as_bytes(options.SerializeToString()))
else:
options_ptr = None
try:
result = self._run(None, fetches, feed_dict, options_ptr,
run_metadata_ptr)
if run_metadata:
proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
run_metadata.ParseFromString(compat.as_bytes(proto_data))
finally:
tf_session.TF_DeleteBuffer(run_metadata_ptr)
if options:
tf_session.TF_DeleteBuffer(options_ptr)
return result
def run(self, fetches, feed_dict=None, options=None, run_outputs=None):
"""Runs the operations and evaluates the tensors in `fetches`.
This method runs one "step" of TensorFlow computation, by
running the necessary graph fragment to execute every `Operation`
and evaluate every `Tensor` in `fetches`, substituting the values in
`feed_dict` for the corresponding input values.
The `fetches` argument may be a list of graph elements or a single
graph element, and these determine the return value of this
method. A graph element can be one of the following types:
* If the *i*th element of `fetches` is an
[`Operation`](../../api_docs/python/framework.md#Operation), the *i*th
return value will be `None`.
* If the *i*th element of `fetches` is a
[`Tensor`](../../api_docs/python/framework.md#Tensor), the *i*th return
value will be a numpy ndarray containing the value of that tensor.
* If the *i*th element of `fetches` is a
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
the *i*th return value will be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue)
containing the value of that sparse tensor.
The optional `feed_dict` argument allows the caller to override
the value of tensors in the graph. Each key in `feed_dict` can be
one of the following types:
* If the key is a [`Tensor`](../../api_docs/python/framework.md#Tensor), the
value may be a Python scalar, string, list, or numpy ndarray
that can be converted to the same `dtype` as that
tensor. Additionally, if the key is a
[placeholder](../../api_docs/python/io_ops.md#placeholder), the shape of
the value will be checked for compatibility with the placeholder.
* If the key is a
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
the value should be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue).
The optional `options` argument expects a [`RunOptions`] proto. The options
allow controlling the behavior of this particular step (e.g. turning tracing
on).
The optional `run_outputs` argument expects a [`RunOutputs`] proto. When
appropriate, the non-Tensor output of this step will be collected there. For
example, when users turn on tracing in `options`, the profiled info will be
collected into this argument and passed back.
Args:
fetches: A single graph element, or a list of graph elements
(described above).
feed_dict: A dictionary that maps graph elements to values
(described above).
options: A [`RunOptions`] protocol buffer
run_outputs: A [`RunOutputs`] protocol buffer
Returns:
Either a single value if `fetches` is a single graph element, or
a list of values if `fetches` is a list (described above).
Raises:
RuntimeError: If this `Session` is in an invalid state (e.g. has been
closed).
TypeError: If `fetches` or `feed_dict` keys are of an inappropriate type.
ValueError: If `fetches` or `feed_dict` keys are invalid or refer to a
`Tensor` that doesn't exist.
"""
run_outputs_ptr = tf_session.TF_NewBuffer()
if options:
options_ptr = tf_session.TF_NewBufferFromString(
compat.as_bytes(options.SerializeToString()))
else:
options_ptr = None
try:
result = self._run(None, fetches, feed_dict, options_ptr,
run_outputs_ptr)
if run_outputs:
proto_data = tf_session.TF_GetBuffer(run_outputs_ptr)
run_outputs.ParseFromString(compat.as_bytes(proto_data))
finally:
tf_session.TF_DeleteBuffer(run_outputs_ptr)
if options:
tf_session.TF_DeleteBuffer(options_ptr)
return result
