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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 <OperationsUtils.h>
#include <arm_compute/core/TensorShape.h>
#include <arm_compute/core/TensorInfo.h>
#include <cassert>
// TODO: fix include path in CMakeFiles
#include "../IO_accessor.h"
#include "../shape.h"
namespace nnfw {
namespace kernel {
namespace acl {
namespace neon {
bool concatenationFloat32(const std::vector<const float*>& inputDataPtrs,
const std::vector<nnfw::rt::Shape>& inputShapes, int32_t axis,
float* outputData, const nnfw::rt::Shape& outputShape)
{
if (axis != 3)
{
assert("Only support axis=3 for ACL" && 0);
return false;
}
assert(inputDataPtrs.size() == inputShapes.size());
std::vector<arm_compute::Tensor*> inputPtrs;
std::vector<arm_compute::ITensor*> inputIptrs;
arm_compute::Tensor output;
// init Tensors
std::vector<nnfw::rt::Shape>::const_iterator it_inputShape = inputShapes.begin();
for (auto inputData : inputDataPtrs)
{
const nnfw::rt::Shape& inputShape = *it_inputShape;
arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
arm_compute::Tensor* inputPtr = new arm_compute::Tensor();
inputPtr->allocator()->init(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
inputPtrs.push_back(inputPtr);
inputIptrs.push_back(inputPtr);
it_inputShape++;
}
arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
output.allocator()->init(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
// prepare ACL Concatenate and configure tensors
auto concat = std::make_shared<arm_compute::NEDepthConcatenateLayer>();
concat->configure(inputIptrs, &output);
// allocate Tensors
it_inputShape = inputShapes.begin();
std::vector<const float*>::const_iterator it_inputData = inputDataPtrs.begin();
for (auto inputPtr : inputPtrs)
{
inputPtr->allocator()->allocate();
const float* inputData = *it_inputData;
const nnfw::rt::Shape& inputShape = *it_inputShape;
TensorAccess<InputAccessor>(*inputPtr, inputData, inputShape);
it_inputShape++;
it_inputData++;
}
output.allocator()->allocate();
// run
concat->run();
// get output
TensorAccess<OutputAccessor>(output, outputData, outputShape);
// cleanup
for (auto inputPtr : inputPtrs)
{
inputPtr->allocator()->free();
delete inputPtr;
}
output.allocator()->free();
return true;
}
} // namespace neon
} // namespace acl
} // namespace kernel
} // namespace nnfw
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