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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. 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.
*/
#include "moco/Importer.h"
#include "moco/Import/GraphBuilder.h"
#include "moco/Import/GraphBuilderContext.h"
#include "moco/Import/GraphBuilderRegistry.h"
#include <moco/IR/Nodes/TFPlaceholder.h>
#include <moco/IR/TFNode.h>
#include <stdex/Memory.h>
#include <oops/UserExn.h>
#include <cassert>
#include <sstream>
#include <stdexcept>
namespace
{
void convert_graph(const moco::GraphBuilderSource &source, const moco::ModelSignature &signature,
tensorflow::GraphDef &tf_graph_def, loco::Graph *graph)
{
auto nodedef = stdex::make_unique<moco::NodeDefTable>();
auto tensor_names = stdex::make_unique<moco::SymbolTable>();
auto updates = stdex::make_unique<moco::UpdateQueue>();
moco::GraphBuilderContext gb_context(graph, nodedef.get(), tensor_names.get(), updates.get());
// Building a loco graph
// 1. Convert all the nodes to moco::TFNode
// 2. Connect inputs: set all node input(from a string) to actual node object
// 3. Set graph input
// 4. Create moco::TFPush node and set graph output
/**
* @brief Prepare tensorflow::NodeDef search table from name
*/
for (const auto &n : tf_graph_def.node())
{
nodedef->enroll(n.name(), &n);
}
/**
* @brief 1. Convert all the nodes to moco::TFNode
*
* @note In each build for a TF node, four things happen
* 1) create corresponding moco::TFNode(s)
* 2) read and set the attributes to created moco::TFNode(s)
* 3) register name-moco::TFNode(last one of Nodes) that will be used as the output
* 4) queue a task to set the input of the moco::TFNode(first one of the Nodes)
* this is done only for required nodes depending on the operator
*
* @example Placeholder("in") - Identity("out")
* %1 = Placeholder --> 0x1001 (moco::TFNode* object address)
* (symboltable: register %1, after the registeration table will contain as below;
* "in" : 0x1001
* )
* (queue: this will be empty as Pull does not queue a task to set input;
* )
*
* %2 = Forward --> 0x1002
* (symboltable: register %2 and table will look like below;
* "in" : 0x1001
* "out" : 0x1002
* )
* (queue: Forward will queue a task with input "in";
* 0x1002: {"in"}
* )
*/
for (const auto &n : tf_graph_def.node())
{
if (const auto *graph_builder = source.lookup(n.op()))
{
if (!graph_builder->validate(n))
{
throw oops::UserExn("Invalid operator", n.op());
}
graph_builder->build(n, &gb_context);
}
else
{
throw oops::UserExn("Not supported", n.op());
}
}
/**
* @brief 2. Connect inputs: Iterate updates and call each update input method
*
* @note Continue from above example graph, connecting inputs is done in following steps
* a) iterate queue
* b) call the input method for each update
* c) each update has the moco::TFNode *node and names of the input to connect
* node = 0x1002 and names = {"in"}
* d) from symbol table, "in" will return 0x1001
* e) set input of 0x1002 with 0x1001
*/
for (auto &update : updates->queue())
{
update->input(tensor_names.get());
}
/**
* @brief 3. Set graph input
*/
for (auto input : signature.inputs())
{
auto node = tensor_names->node(input);
assert(node != nullptr);
auto graph_input = graph->inputs()->create();
auto placeholder_node = dynamic_cast<moco::TFPlaceholder *>(node);
assert(placeholder_node != nullptr);
graph_input->name(input.nodeName());
// annotate index that should be passed to loco::Pull
moco::index(placeholder_node, graph_input->index());
// This implementation works as "PlaceholderGraphBuilder in Nodes/Placeholder.cpp"
// accepts only TF_FLOAT32 as of now.
//
// TODO Support other types
graph_input->dtype(loco::DataType::FLOAT32);
}
/**
* @brief 4. Create moco::TFPush node and set graph output
*/
for (auto output : signature.outputs())
{
auto output_node = tensor_names->node(output);
assert(output_node);
// create moco::TFPush for output of graph
auto push_node = graph->nodes()->create<moco::TFPush>();
push_node->from(output_node); // set input of TFPush to output node
// set the graph output name and node object
auto graph_output = graph->outputs()->create();
graph_output->name(output.nodeName());
push_node->index(graph_output->index());
// TODO Support other types
graph_output->dtype(loco::DataType::FLOAT32);
}
// validate graph
assert(loco::valid(graph));
}
} // namespace
namespace moco
{
Importer::Importer()
{
// DO NOTHING
}
std::unique_ptr<loco::Graph> Importer::import(const ModelSignature &signature,
tensorflow::GraphDef &tf_graph_def) const
{
auto graph = loco::make_graph();
const GraphBuilderSource *source_ptr = &moco::GraphBuilderRegistry::get();
if (_source != nullptr)
{
// Use user-defined GraphBuilderSource
source_ptr = _source;
}
convert_graph(*source_ptr, signature, tf_graph_def, graph.get());
return std::move(graph);
}
} // namespace moco
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