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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 "Div.h"
#include "Convert.h"
#include "IRBuilder.h"
#include "GraphBuilder.h"
#include "Padding.h"
#include "Activation.h"
#include <morph/tflite.h>
#include <coco/IR/Module.h>
#include <coco/IR/FeatureLayouts.h>
#include <nncc/core/ADT/tensor/Shape.h>
#include <schema_generated.h>
#include <cassert>
using namespace nncc::core::ADT;
using namespace morph::tflite;
namespace tflimport
{
void DivGraphBuilder::build(const tflite::Operator *op, GraphBuilderContext *context) const
{
assert(context != nullptr);
coco::Module *m = context->m();
coco::Block *blk = context->block();
TensorContext &tensor_context = context->tensor();
TensorBags &bags = context->bags();
IndexVector opinputs = as_index_vector(op->inputs());
IndexVector opoutputs = as_index_vector(op->outputs());
// these are fixed in tflite
// input index 0 : numerator
// input index 1 : denominator
// output index 0 : result
assert(opinputs.size() == 2);
assert(opoutputs.size() == 1);
tflite::ActivationFunctionType activation;
if (auto *options = op->builtin_options_as_DivOptions())
{
activation = options->fused_activation_function();
}
else
{
activation = tflite::ActivationFunctionType_NONE;
}
// TODO activation, e.g. ReLU
assert(activation == tflite::ActivationFunctionType_NONE);
auto num_idx = opinputs.at(0);
auto denom_idx = opinputs.at(1);
auto out_idx = opoutputs.at(0);
const tensor::Shape &num_shape = tensor_context.shape(num_idx);
const tensor::Shape &denom_shape = tensor_context.shape(denom_idx);
const tensor::Shape &out_shape = tensor_context.shape(out_idx);
// TODO Now input/output assumes Feature map, but Div should support generic object type
// Create an object for an input
auto *num_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *num_bag = bags.bag(num_idx);
num_obj->bag(num_bag);
num_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(num_shape)));
auto *denom_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *denom_bag = bags.bag(denom_idx);
denom_obj->bag(denom_bag);
denom_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(denom_shape)));
// Create an object for an output
auto *out_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *out_bag = bags.bag(out_idx);
out_obj->bag(out_bag);
out_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(out_shape)));
// Create a Load ops for each input
auto coco_load_num = op_builder(m).load(num_obj).pop();
auto coco_load_denom = op_builder(m).load(denom_obj).pop();
// Create a Div op
auto coco_div = m->entity()->op()->create<coco::Div>();
// Link ops
coco_div->left(coco_load_num);
coco_div->right(coco_load_denom);
// Create an Eval instruction
auto eval_ins = instr_builder(m).eval(out_obj, coco_div);
// Append the instruction to the block
blk->instr()->append(eval_ins);
}
} // namespace tflimport
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