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/*
* Copyright (c) 2018 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 "MaxPool2D.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
{
bool MaxPool2DGraphBuilder::validate(const tflite::Operator *op) const
{
auto const options = op->builtin_options_as_Pool2DOptions();
if ((options->stride_h() == 0) || (options->stride_w() == 0))
{
return false;
}
return true;
}
void MaxPool2DGraphBuilder::build(const tflite::Operator *op, GraphBuilderContext *context) const
{
assert(context != nullptr); // check if init(..) is called
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 : input feature
// output index 0 : output feature
assert(opinputs.size() == 1);
assert(opoutputs.size() == 1);
int ifm_idx = opinputs.at(0);
int ofm_idx = opoutputs.at(0);
const tensor::Shape &ifm_shape = tensor_context.shape(ifm_idx);
const tensor::Shape &ofm_shape = tensor_context.shape(ofm_idx);
// Create an object for an input feature map
coco::FeatureObject *ifm_obj = m->entity()->object()->create<coco::FeatureObject>();
coco::Bag *ifm_bag = bags.bag(ifm_idx);
ifm_obj->bag(ifm_bag);
ifm_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ifm_shape)));
// Create an object for an output feature map
coco::FeatureObject *ofm_obj = m->entity()->object()->create<coco::FeatureObject>();
coco::Bag *ofm_bag = bags.bag(ofm_idx);
ofm_obj->bag(ofm_bag);
ofm_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ofm_shape)));
// Create a Load op
coco::Op *coco_load = op_builder(m).load(ifm_obj).pop();
// Create a MaxPool2D
coco::MaxPool2D *coco_maxpool2d = m->entity()->op()->create<coco::MaxPool2D>();
const tflite::Pool2DOptions *params = op->builtin_options_as_Pool2DOptions();
coco_maxpool2d->window()->height(params->filter_height());
coco_maxpool2d->window()->width(params->filter_width());
coco_maxpool2d->stride()->vertical(params->stride_h());
coco_maxpool2d->stride()->horizontal(params->stride_w());
coco::Padding2D padding =
pool2D_padding(params, ifm_shape, params->filter_width(), params->filter_height());
coco_maxpool2d->pad()->top(padding.top());
coco_maxpool2d->pad()->bottom(padding.bottom());
coco_maxpool2d->pad()->left(padding.left());
coco_maxpool2d->pad()->right(padding.right());
// Link ops
coco_maxpool2d->arg(coco_load);
// Create an Eval instruction
coco::Eval *ins = instr_builder(m).eval(ofm_obj, coco_maxpool2d);
// Append the instruction to the block
blk->instr()->append(ins);
// TODO activation, e.g., relu
assert(params->fused_activation_function() ==
tflite::ActivationFunctionType::ActivationFunctionType_NONE);
}
} // namespace tflimport
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