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# Copyright 2017 The TensorFlow Authors. 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.
# ==============================================================================
"""Common utilities used across this package."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
# Skip all operations that are backprop related or export summaries.
SKIPPED_PREFIXES = (
'gradients/', 'RMSProp/', 'Adagrad/', 'Const_', 'HistogramSummary',
'ScalarSummary')
# Valid activation ops for quantization end points.
_ACTIVATION_OP_SUFFIXES = ['/Relu6', '/Relu', '/Identity']
# Regular expression for recognizing nodes that are part of batch norm group.
_BATCHNORM_RE = re.compile(r'^(.*)/BatchNorm/batchnorm')
def BatchNormGroups(graph):
"""Finds batch norm layers, returns their prefixes as a list of strings.
Args:
graph: Graph to inspect.
Returns:
List of strings, prefixes of batch norm group names found.
"""
bns = []
for op in graph.get_operations():
match = _BATCHNORM_RE.search(op.name)
if match:
bn = match.group(1)
if not bn.startswith(SKIPPED_PREFIXES):
bns.append(bn)
# Filter out duplicates.
return list(collections.OrderedDict.fromkeys(bns))
def GetEndpointActivationOp(graph, prefix):
"""Returns an Operation with the given prefix and a valid end point suffix.
Args:
graph: Graph where to look for the operation.
prefix: String, prefix of Operation to return.
Returns:
The Operation with the given prefix and a valid end point suffix or None if
there are no matching operations in the graph for any valid suffix
"""
for suffix in _ACTIVATION_OP_SUFFIXES:
activation = _GetOperationByNameDontThrow(graph, prefix + suffix)
if activation:
return activation
return None
def _GetOperationByNameDontThrow(graph, name):
"""Returns an Operation with the given name.
Args:
graph: Graph where to look for the operation.
name: String, name of Operation to return.
Returns:
The Operation with the given name. None if the name does not correspond to
any operation in the graph
"""
try:
return graph.get_operation_by_name(name)
except KeyError:
return None
def CreateOrGetQuantizationStep():
"""Returns a Tensor of the number of steps the quantized graph has run.
Returns:
Quantization step Tensor.
"""
quantization_step_name = 'fake_quantization_step'
quantization_step_tensor_name = quantization_step_name + '/Identity:0'
g = ops.get_default_graph()
try:
return g.get_tensor_by_name(quantization_step_tensor_name)
except KeyError:
# Create in proper graph and base name_scope.
with g.name_scope(None):
quantization_step_tensor = variable_scope.get_variable(
quantization_step_name,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
with g.name_scope(quantization_step_tensor.op.name + '/'):
# We return the incremented variable tensor. Since this is used in conds
# for quant_delay and freeze_bn_delay, it will run once per graph
# execution. We return an identity to force resource variables and
# normal variables to return a tensor of the same name.
return array_ops.identity(
state_ops.assign_add(quantization_step_tensor, 1))
def DropStringPrefix(s, prefix):
"""If the string starts with this prefix, drops it."""
if s.startswith(prefix):
return s[len(prefix):]
else:
return s
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