000c8f09ea
This change modifies these includes to point to "tensorflow/core/common_runtime/graph_constructor.h" instead. This change will enable us to remove the accidental dependency from //tensorflow/core/graph to //tensorflow/core/common_runtime. PiperOrigin-RevId: 309035649 Change-Id: I2af0fdd6a6ccc4ae8d351a9117a69b6fc80c22e9
99 lines
3.5 KiB
C++
99 lines
3.5 KiB
C++
/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "tensorflow/core/common_runtime/constant_folding.h"
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#include "tensorflow/core/common_runtime/graph_constructor.h"
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#include "tensorflow/core/graph/node_builder.h"
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#include "tensorflow/core/graph/subgraph.h"
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#include "tensorflow/core/platform/init_main.h"
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#include "tensorflow/core/public/session.h"
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#include "tensorflow/tools/graph_transforms/fold_constants_lib.h"
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#include "tensorflow/tools/graph_transforms/transform_utils.h"
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namespace tensorflow {
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namespace graph_transforms {
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// Renames all nodes not uses as graph inputs or outputs to short numerical
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// forms.
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Status ObfuscateNames(const GraphDef& input_graph_def,
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const TransformFuncContext& context,
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GraphDef* output_graph_def) {
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std::unordered_set<string> required_nodes;
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for (const string& input : context.input_names) {
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required_nodes.insert(input);
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}
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for (const string& output : context.output_names) {
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required_nodes.insert(output);
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}
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const string valid_chars =
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"0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
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const int64 chars_size = valid_chars.size();
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std::map<string, string> new_names;
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int64 name_index = 0;
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for (const NodeDef& input_node : input_graph_def.node()) {
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const string& old_name = input_node.name();
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string new_name;
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if (required_nodes.count(old_name)) {
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new_name = old_name;
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} else {
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do {
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int64 remaining = name_index;
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new_name = "";
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while (true) {
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const int64 remainder = (remaining % chars_size);
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const char current_char = valid_chars[remainder];
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new_name = current_char + new_name;
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remaining /= chars_size;
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if (remaining <= 0) {
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break;
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}
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}
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++name_index;
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} while (required_nodes.count(new_name));
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}
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new_names[old_name] = new_name;
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}
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output_graph_def->Clear();
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for (const NodeDef& input_node : input_graph_def.node()) {
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NodeDef* node = output_graph_def->mutable_node()->Add();
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*node = input_node;
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const string& old_name = input_node.name();
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node->set_name(new_names[old_name]);
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node->mutable_input()->Clear();
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for (const string& input_name : input_node.input()) {
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string prefix;
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string input_node_name;
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string suffix;
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NodeNamePartsFromInput(input_name, &prefix, &input_node_name, &suffix);
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if (new_names.count(input_node_name) == 0) {
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return errors::InvalidArgument("No node named ", input_node_name,
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" for input to ", old_name);
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}
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string new_input_name = prefix + new_names[input_node_name] + suffix;
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*(node->mutable_input()->Add()) = new_input_name;
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}
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}
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return Status::OK();
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}
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REGISTER_GRAPH_TRANSFORM("obfuscate_names", ObfuscateNames);
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} // namespace graph_transforms
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} // namespace tensorflow
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