134 lines
4.4 KiB
Python
134 lines
4.4 KiB
Python
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Common utilities used across this package."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import re
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.ops import variable_scope
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# Skip all operations that are backprop related or export summaries.
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SKIPPED_PREFIXES = (
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'gradients/', 'RMSProp/', 'Adagrad/', 'Const_', 'HistogramSummary',
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'ScalarSummary')
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# Valid activation ops for quantization end points.
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_ACTIVATION_OP_SUFFIXES = ['/Relu6', '/Relu', '/Identity']
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# Regular expression for recognizing nodes that are part of batch norm group.
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_BATCHNORM_RE = re.compile(r'^(.*)/BatchNorm/batchnorm')
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def BatchNormGroups(graph):
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"""Finds batch norm layers, returns their prefixes as a list of strings.
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Args:
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graph: Graph to inspect.
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Returns:
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List of strings, prefixes of batch norm group names found.
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"""
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bns = []
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for op in graph.get_operations():
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match = _BATCHNORM_RE.search(op.name)
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if match:
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bn = match.group(1)
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if not bn.startswith(SKIPPED_PREFIXES):
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bns.append(bn)
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# Filter out duplicates.
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return list(collections.OrderedDict.fromkeys(bns))
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def GetEndpointActivationOp(graph, prefix):
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"""Returns an Operation with the given prefix and a valid end point suffix.
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Args:
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graph: Graph where to look for the operation.
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prefix: String, prefix of Operation to return.
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Returns:
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The Operation with the given prefix and a valid end point suffix or None if
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there are no matching operations in the graph for any valid suffix
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"""
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for suffix in _ACTIVATION_OP_SUFFIXES:
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activation = _GetOperationByNameDontThrow(graph, prefix + suffix)
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if activation:
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return activation
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return None
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def _GetOperationByNameDontThrow(graph, name):
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"""Returns an Operation with the given name.
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Args:
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graph: Graph where to look for the operation.
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name: String, name of Operation to return.
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Returns:
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The Operation with the given name. None if the name does not correspond to
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any operation in the graph
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"""
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try:
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return graph.get_operation_by_name(name)
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except KeyError:
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return None
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def CreateOrGetQuantizationStep():
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"""Returns a Tensor of the number of steps the quantized graph has run.
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Returns:
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Quantization step Tensor.
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"""
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quantization_step_name = 'fake_quantization_step'
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quantization_step_tensor_name = quantization_step_name + '/Identity:0'
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g = ops.get_default_graph()
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try:
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return g.get_tensor_by_name(quantization_step_tensor_name)
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except KeyError:
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# Create in proper graph and base name_scope.
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with g.name_scope(None):
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quantization_step_tensor = variable_scope.get_variable(
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quantization_step_name,
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shape=[],
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dtype=dtypes.int64,
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initializer=init_ops.zeros_initializer(),
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trainable=False,
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collections=[ops.GraphKeys.GLOBAL_VARIABLES])
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with g.name_scope(quantization_step_tensor.op.name + '/'):
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# We return the incremented variable tensor. Since this is used in conds
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# for quant_delay and freeze_bn_delay, it will run once per graph
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# execution. We return an identity to force resource variables and
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# normal variables to return a tensor of the same name.
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return array_ops.identity(
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state_ops.assign_add(quantization_step_tensor, 1))
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def DropStringPrefix(s, prefix):
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"""If the string starts with this prefix, drops it."""
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if s.startswith(prefix):
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return s[len(prefix):]
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else:
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return s
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