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graph_util.py
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# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
# ==============================================================================
"""Helpers to manipulate a tensor graph in python.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow.python.platform
from tensorflow.core.framework import graph_pb2
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.platform import logging
_VARIABLE_OPS = {
"Assign",
"AssignAdd",
"AssignSub",
"Queue",
"RandomParameters",
"ScatterAdd",
"ScatterSub",
"ScatterUpdate",
"Variable",
}
def _is_variable_op(op):
"""Returns true if 'op' refers to a Variable node."""
return op in _VARIABLE_OPS
def set_cpu0(device_string):
"""Creates a new device string based on `device_string' but using /CPU:0.
If the device is already on /CPU:0, this is a no-op.
Args:
device_string: A device string.
Returns:
A device string.
"""
parsed_device = pydev.from_string(device_string)
parsed_device.device_type = "CPU"
parsed_device.device_index = 0
return parsed_device.to_string()
def must_run_on_cpu(node, pin_variables_on_cpu=False):
"""Returns True if the given node_def must run on CPU, otherwise False.
Args:
node: The node to be assigned to a device. Could be either an ops.Operation
or NodeDef.
pin_variables_on_cpu: If True, this function will return False if node_def
represents a variable-related op.
Returns:
True if the given node must run on CPU, otherwise False.
"""
if isinstance(node, ops.Operation):
node_def = node.node_def
else:
assert isinstance(node, graph_pb2.NodeDef)
node_def = node
# If the op is a variable-related op, should we pin it on CPU?
if pin_variables_on_cpu and _is_variable_op(node_def.op):
return True
# Constant operations producing a string or int32 must run on CPU.
if node_def.op == "Const":
# Get the value of the 'dtype' attr
dtype = node_def.attr["dtype"].type
if dtype == dtypes.string or dtype == dtypes.int32:
return True
if node_def.op == "DynamicStitch":
dtype = node_def.attr["T"].type
if dtype == dtypes.int32:
# DynamicStitch on GPU only works for int32 values.
return True
if node_def.op in ["Cast"]:
dtype = node_def.attr["SrcT"].type
if dtype == dtypes.int32:
# Cast on GPU does not works for int32 values.
return True
return False
################################################################################
#
# device functions for use in with g.device(...)
#
################################################################################
def pin_variables_on_cpu(op):
"""Returns a CPU device for Variable nodes if the device is not specified.
Args:
op: The ops.Operation object describing the node for which a device
should be chosen. The op.device field is respected.
Returns:
A device containing "/device:CPU:0" if the node is related to a variable.
"""
device = op.device if op.device is not None else ""
dev = pydev.from_string(device)
# If a device type exists already, do not override.
if dev.device_type:
return device
if isinstance(op, ops.Operation):
node_def = op.node_def
else:
assert isinstance(op, graph_pb2.NodeDef)
node_def = op
if _is_variable_op(node_def.op):
return set_cpu0(device)
return device
def pin_to_cpu(op):
"""Returns a CPU device for the given node."""
device = op.device if op.device is not None else ""
dev = pydev.from_string(device)
if not dev.device_type:
return set_cpu0(device)
if dev.device_type == "CPU":
return device
logging.info("Operation %s has been assigned to a non-CPU (%s), so "
"it will not be pinned to the CPU.", op.name, dev.device_type)
return device
def _node_name(n):
if n.startswith("^"):
return n[1:]
else:
return n.split(":")[0]
def extract_sub_graph(graph_def, dest_nodes):
"""Extract the subgraph that can reach any of the nodes in 'dest_nodes'.
Args:
graph_def: A graph_pb2.GraphDef proto.
dest_nodes: A list of strings specifying the destination node names.
Returns:
The GraphDef of the sub-graph.
Raises:
TypeError: If 'graph_def' is not a graph_pb2.GraphDef proto.
"""
if not isinstance(graph_def, graph_pb2.GraphDef):
raise TypeError("graph_def must be a graph_pb2.GraphDef proto.")
edges = {} # Keyed by the dest node name.
name_to_node_map = {} # Keyed by node name.
# Keeps track of node sequences. It is important to still output the
# operations in the original order.
node_seq = {} # Keyed by node name.
seq = 0
for node in graph_def.node:
n = _node_name(node.name)
name_to_node_map[n] = node
edges[n] = [_node_name(x) for x in node.input]
node_seq[n] = seq
seq += 1
for d in dest_nodes:
assert d in name_to_node_map, "%d is not in graph" % d
nodes_to_keep = set()
# Breadth first search to find all the nodes that we should keep.
next_to_visit = dest_nodes[:]
while next_to_visit:
n = next_to_visit[0]
del next_to_visit[0]
if n in nodes_to_keep:
# Already visited this node.
continue
nodes_to_keep.add(n)
next_to_visit += edges[n]
nodes_to_keep_list = sorted(list(nodes_to_keep), key=lambda n: node_seq[n])
# Now construct the output GraphDef
out = graph_pb2.GraphDef()
for n in nodes_to_keep_list:
out.node.extend([copy.deepcopy(name_to_node_map[n])])
return out
def tensor_shape_from_node_def_name(graph, input_name):
"""Convenience function to get a shape from a NodeDef's input string."""
# To get a tensor, the name must be in the form <input>:<port>, for example
# 'Mul:0'. The GraphDef input strings don't always have the port specified
# though, so if there isn't a colon we need to add a default ':0' to the end.
if ":" not in input_name:
canonical_name = input_name + ":0"
else:
canonical_name = input_name
tensor = graph.get_tensor_by_name(canonical_name)
shape = tensor.get_shape()
return shape