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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 "Conv2DConverter.h"
#include "Dialect/IR/TFLNodes.h"
#include "GraphBlock.h"
#include "Check.h"
#include <loco.h>
#include <loco/Service/TypeInference.h>
#include <loco/Service/ShapeInference.h>
namespace exo
{
/**
* @brief Converts loco::Conv2D to locoex::TFLConv2D
* @note Because TFLConv2D accepts input and filter of loco::Domain::Tensor,
* loco::FeatureDecode and loco::FilterDecode will be inserted as an inputs
* to meet domain invariant.
* Please refer to the comment in AvgPool2DConvert.
*/
bool Conv2DConverter::convert(loco::Conv2D *origin)
{
auto *graph = origin->graph();
assert(origin->ifm());
assert(origin->ker());
auto tfl_conv2d = graph->nodes()->create<locoex::TFLConv2D>();
{
tfl_conv2d->stride()->w(origin->stride()->horizontal());
tfl_conv2d->stride()->h(origin->stride()->vertical());
auto pad = origin->pad();
if (pad->bottom() == 0 && pad->top() == 0 && pad->left() == 0 && pad->right() == 0)
tfl_conv2d->padding(locoex::Padding::VALID);
else
// TODO This is necessary, but not sufficient condition. More rigorous check required
tfl_conv2d->padding(locoex::Padding::SAME);
tfl_conv2d->fusedActivationFunction(locoex::FusedActFunc::NONE);
}
// let's create a new graph connection with tfl_conv2d
{
// input
auto feature_dec = make_feature_decode<FeatureLayout::NHWC>(origin->ifm());
tfl_conv2d->input(feature_dec);
// filter
auto filter_dec = make_filter_decode<FilterLayout::OHWI>(origin->ker());
tfl_conv2d->filter(filter_dec);
// bias
auto zero_const = graph->nodes()->create<locoex::TFLConst>();
{
assert(loco::shape_known(origin));
assert(loco::dtype_known(origin) && loco::dtype_get(origin) == loco::DataType::FLOAT32);
auto output_depth = loco::shape_get(origin->ker()).as<loco::FilterShape>().count();
zero_const->dtype(loco::DataType::FLOAT32);
zero_const->rank(1);
zero_const->dim(0) = output_depth;
zero_const->size<loco::DataType::FLOAT32>(output_depth.value());
for (uint32_t x = 0; x < output_depth.value(); x++)
zero_const->at<loco::DataType::FLOAT32>(x) = 0.0;
}
tfl_conv2d->bias(zero_const);
// output
auto feature_enc = make_feature_encode<FeatureLayout::NHWC>(tfl_conv2d);
// replace canonical node
loco::replace(origin).with(feature_enc);
origin->ifm(nullptr);
}
return true;
}
} // namespace exo
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