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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 "tflite_loader.h"
#include "base_loader.h"
#include "tflite_schema_generated.h"
namespace onert
{
namespace tflite_loader
{
namespace
{
struct LoaderDomain
{
using Verifier = flatbuffers::Verifier;
using ActivationFunctionType = onert_tflite::ActivationFunctionType;
using Buffer = onert_tflite::Buffer;
using BuiltinOperator = onert_tflite::BuiltinOperator;
using CustomOptionsFormat = onert_tflite::CustomOptionsFormat;
using Model = onert_tflite::Model;
using Operator = onert_tflite::Operator;
using Padding = onert_tflite::Padding;
using Pool2DOptions = onert_tflite::Pool2DOptions;
using Tensor = onert_tflite::Tensor;
using TensorType = onert_tflite::TensorType;
using SubGraph = onert_tflite::SubGraph;
using DimensionType = onert_tflite::DimensionType;
using SparseIndexVector = onert_tflite::SparseIndexVector;
static const char *EnumNameBuiltinOperator(BuiltinOperator e)
{
return onert_tflite::EnumNameBuiltinOperator(e);
}
static const char *EnumNameActivationFunctionType(ActivationFunctionType e)
{
return onert_tflite::EnumNameActivationFunctionType(e);
}
static const char *EnumNameTensorType(TensorType e)
{
return onert_tflite::EnumNameTensorType(e);
}
static const Model *GetModel(const void *buf) { return onert_tflite::GetModel(buf); }
static bool VerifyModelBuffer(Verifier &verifier)
{
return onert_tflite::VerifyModelBuffer(verifier);
}
};
class TFLiteLoader final : public base_loader::BaseLoader<LoaderDomain>
{
protected:
// Different option name
// Circle: adjoint_lhs, adjoint_rhs
// TFLite: adj_x, adj_y
void loadBatchMatMul(const Operator *op, ir::Graph &subg);
public:
using BaseLoader::BaseLoader;
bool allowOptionalInputTensor(BuiltinOperator op) override
{
switch (op)
{
case BuiltinOperator::BuiltinOperator_FULLY_CONNECTED:
case BuiltinOperator::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
return true;
default:
return false;
}
}
private:
std::unique_ptr<ir::Graph> loadSubgraph(const onert_tflite::SubGraph *tflite_subg) override
{
auto subg = std::make_unique<ir::Graph>();
// Load tensors
_tensor_to_operand.resize(tflite_subg->tensors()->size());
for (flatbuffers::uoffset_t i = 0; i < tflite_subg->tensors()->size(); ++i)
{
_tensor_to_operand[i] = loadOperand(tflite_subg->tensors()->Get(i), *subg);
}
// Set inputs
for (const std::int32_t input_ind : *tflite_subg->inputs())
{
subg->addInput(tensorIdxToOperandIdx(input_ind),
_tensor_names->at(_tensor_to_operand[input_ind]));
}
// Set outputs
for (const std::int32_t output_ind : *tflite_subg->outputs())
{
subg->addOutput(tensorIdxToOperandIdx(output_ind),
_tensor_names->at(_tensor_to_operand[output_ind]));
}
// Create operations
for (const auto *op : *tflite_subg->operators())
{
loadOperation(op, *subg);
}
subg->setTensorName(_tensor_names);
subg->verify();
return subg;
}
void loadOperation(const onert_tflite::Operator *op, ir::Graph &subg)
{
auto const builtin_op = getBuiltinOperator(op);
switch (builtin_op)
{
case onert_tflite::BuiltinOperator::BuiltinOperator_BATCH_MATMUL:
loadBatchMatMul(op, subg);
return;
default:
BaseLoader::loadOperation(op, subg);
return;
}
}
};
void TFLiteLoader::loadBatchMatMul(const Operator *op, ir::Graph &subg)
{
ir::OperandIndexSequence inputs;
ir::OperandIndexSequence outputs;
loadOperationIO(op, inputs, outputs);
ir::operation::BatchMatMul::Param param;
const auto *options = op->builtin_options_as_BatchMatMulOptions();
param.adj_x = options->adj_x();
param.adj_y = options->adj_y();
std::unique_ptr<ir::Operation> new_op(new ir::operation::BatchMatMul(inputs, outputs, param));
subg.addOperation(std::move(new_op));
}
} // namespace
std::unique_ptr<ir::Model> loadModel(const std::string &filename)
{
auto model = std::make_unique<ir::Model>();
TFLiteLoader loader(model);
loader.loadFromFile(filename);
return model;
}
} // namespace tflite_loader
} // namespace onert
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