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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 "NodeExecution.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include "Validation.h"
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <stdexcept>
#include <cassert>
namespace
{
using nncc::core::ADT::tensor::Buffer;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::IndexEnumerator;
template <typename T>
std::unique_ptr<locomotiv::NodeData> feature_encode(const loco::FeatureEncode *node,
const Buffer<T> *input_buf)
{
auto encoder = node->encoder();
// Make TensorShape from input
loco::TensorShape input_shape;
input_shape.rank(input_buf->shape().rank());
assert(input_shape.rank() == 4);
for (uint32_t i = 0; i < input_shape.rank(); ++i)
{
input_shape.dim(i) = input_buf->shape().dim(i);
}
loco::FeatureShape node_shape = encoder->shape(input_shape);
// Make NHWC buffer from FeatureShape
Buffer<T> node_buf =
make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(),
node_shape.width().value(), node_shape.depth().value()});
// Copy buffer in an order arranged by encoder
for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance())
{
loco::FeatureIndex index;
index.batch() = e.current().at(0);
index.row() = e.current().at(1);
index.column() = e.current().at(2);
index.channel() = e.current().at(3);
node_buf.at(e.current()) = input_buf->at(encoder->value(index));
}
return locomotiv::make_data(node_buf);
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::FeatureEncode *enc)
{
auto input_data = annot_data(enc->input());
validate(input_data, "Input of FeatureEncode not ready");
validate(annot_domain(enc->input()) == loco::Domain::Tensor,
"Input of FeatureEncode is not Tensor");
validate(input_data->shape()->rank() == 4, "Input shape mismatch");
std::unique_ptr<NodeData> enc_data = nullptr;
switch (input_data->dtype())
{
case loco::DataType::S32:
{
auto input_buf = input_data->as_s32_bufptr();
enc_data = feature_encode<int32_t>(enc, input_buf);
break;
}
case loco::DataType::FLOAT32:
{
auto input_buf = input_data->as_f32_bufptr();
enc_data = feature_encode<float>(enc, input_buf);
break;
}
default:
throw std::runtime_error("NYI for this DataType");
}
assert(enc_data != nullptr);
annot_data(enc, std::move(enc_data));
annot_domain(enc, loco::Domain::Feature);
}
} // namespace locomotiv
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