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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/Import/Nodes/Relu6.h"
#include <moco/IR/Nodes/TFRelu6.h>
#include <stdex/Memory.h>
namespace
{
using namespace moco;
class TFRelu6GraphUpdate final : public GraphUpdate
{
public:
TFRelu6GraphUpdate(TFRelu6 *node, const TensorName &&name) : _node(node), _name(name) {}
void input(const SymbolTable *) const override;
private:
TFRelu6 *_node;
const TensorName _name;
};
void TFRelu6GraphUpdate::input(const SymbolTable *table) const
{
loco::Node *target = table->node(_name);
_node->features(target);
}
} // namespace
namespace moco
{
bool Relu6GraphBuilder::validate(const tensorflow::NodeDef &node) const
{
// ReLU6 node SHOULD have only one input
if (node.input_size() != 1)
return false;
return true;
}
void Relu6GraphBuilder::build(const tensorflow::NodeDef &node, GraphBuilderContext *context) const
{
assert(context != nullptr);
loco::Graph *graph = context->graph();
SymbolTable *tensor_names = context->tensor_names();
UpdateQueue *updates = context->updates();
// Create a "TFRelu6" node for Relu
auto relu_node = graph->nodes()->create<TFRelu6>();
relu_node->name(node.name());
// register string-name to node
TensorName output_name(node.name(), 0);
tensor_names->enroll(output_name, relu_node);
// Queue node input update
auto update = stdex::make_unique<TFRelu6GraphUpdate>(relu_node, TensorName(node.input(0)));
updates->enroll(std::move(update));
}
} // namespace moco
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