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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 "Conv2D.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/KernelLayouts.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 Conv2DGraphBuilder::validate(const tflite::Operator *op) const
{
auto const options = op->builtin_options_as_Conv2DOptions();
if ((options->stride_h() == 0) || (options->stride_w() == 0))
{
return false;
}
return true;
}
void Conv2DGraphBuilder::build(const tflite::Operator *op, GraphBuilderContext *context) const
{
assert(context != nullptr);
// preparation
coco::Module *m = context->m();
coco::Data *d = context->d();
coco::Block *blk = context->block();
TensorContext &tensor_context = context->tensor();
TensorBags &bags = context->bags();
TflBufferContext &buffer_context = context->buffer();
const tflite::SubGraph *graph = context->graph();
IndexVector opinputs = as_index_vector(op->inputs());
IndexVector opoutputs = as_index_vector(op->outputs());
// these are fixed in tflite
// input index 0 : input feature
// input index 1 : kernel
// input index 2 : bias (optional)
bool hasBias = (opinputs.size() == 3);
assert(opinputs.size() == 2 || hasBias);
assert(opoutputs.size() == 1);
int ifm_idx = opinputs.at(0);
int ker_idx = opinputs.at(1);
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);
const tensor::Shape &ker_shape = tensor_context.shape(ker_idx);
// Create an input feature map object
auto *ifm_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *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 an output feature map object
auto *ofm_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *ofm_bag = bags.bag(ofm_idx);
ofm_obj->bag(ofm_bag);
ofm_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ofm_shape)));
// Create an kernel object
auto *ker_obj = m->entity()->object()->create<coco::KernelObject>();
auto *ker_bag = bags.bag(ker_idx);
ker_obj->bag(ker_bag);
ker_obj->layout(coco::KernelLayouts::NHWC::create(as_kernel_shape(ker_shape)));
// Create a Load op
auto load = op_builder(m).load(ifm_obj).pop();
// Create a Conv2D op
auto coco_conv2d = m->entity()->op()->create<coco::Conv2D>();
// populating Conv2D objects and options such as stride and padding
coco_conv2d->ker(ker_obj);
auto *conv_params = op->builtin_options_as_Conv2DOptions();
coco_conv2d->stride()->vertical(conv_params->stride_h());
coco_conv2d->stride()->horizontal(conv_params->stride_w());
// conv_params->padding() to left, top, right, bottom
coco::Padding2D padding = conv2D_padding(conv_params, ifm_shape, ker_shape);
coco_conv2d->pad()->top(padding.top());
coco_conv2d->pad()->bottom(padding.bottom());
coco_conv2d->pad()->left(padding.left());
coco_conv2d->pad()->right(padding.right());
// Link ops
coco_conv2d->arg(load);
// Object to store Conv2D output
auto *conv2d_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *conv2d_bag = m->entity()->bag()->create(num_elements(ofm_shape));
conv2d_obj->bag(conv2d_bag);
conv2d_obj->layout(coco::FeatureLayouts::BHWC::create(as_feature_shape(ofm_shape)));
// Create an Eval instruction for Conv2D
auto conv2d_ins = instr_builder(m).eval(conv2d_obj, coco_conv2d);
// Append the instruction to the block
blk->instr()->append(conv2d_ins);
// Last Object to make a copy to Output Object
coco::FeatureObject *last_obj = conv2d_obj;
if (hasBias)
{
// When there is a bias, use btmp_obj as bias add output
// Bias is adding last_obj with bias weight values
auto *btmp_obj = m->entity()->object()->create<coco::FeatureObject>();
auto *btmp_bag = m->entity()->bag()->create(num_elements(ofm_shape));
btmp_obj->bag(btmp_bag);
btmp_obj->layout(coco::FeatureLayouts::BHWC::create(ofm_obj->shape()));
int bias_idx = opinputs.at(2);
// Create an object for bias
auto bias_obj = m->entity()->object()->create<coco::FeatureObject>();
coco::Bag *bias_bag = bags.bag(bias_idx);
bias_obj->bag(bias_bag);
bias_obj->layout(coco::FeatureLayouts::BC::create(ofm_obj->shape()));
// Create Op of conv2d output (last_obj) + bias values(bias_obj)
auto bias_add = op_builder(m).load(last_obj).load(bias_obj).add().pop();
// Create Instr as bias add result write to btmp_obj
auto bias_add_ins = instr_builder(m).eval(btmp_obj, bias_add);
// Append the instruction
blk->instr()->append(bias_add_ins);
// Update last_obj to btmp_obj
last_obj = btmp_obj;
}
// fused activation
coco::FeatureObject *act_output =
build_activation(conv_params->fused_activation_function(), blk, last_obj);
// Create Copy Instr of last_obj to Output Object
auto copy_ins = instr_builder(m).copy(ofm_obj, act_output);
blk->instr()->append(copy_ins);
}
} // namespace tflimport
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