blob: 21ad451c3e1607a149cc10773eba62eca4218b54 [file] [log] [blame]
/*
* Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/graph/nodes/ReorgLayerNode.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
#include "arm_compute/graph/Utils.h"
namespace arm_compute
{
namespace graph
{
ReorgLayerNode::ReorgLayerNode(int stride)
: _stride(stride)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
}
int ReorgLayerNode::stride() const
{
return _stride;
}
TensorDescriptor ReorgLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, int stride)
{
const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT);
const unsigned int input_channel = get_dimension_size(input_descriptor, DataLayoutDimension::CHANNEL);
ARM_COMPUTE_ERROR_ON(stride <= 0);
ARM_COMPUTE_ERROR_ON_MSG((input_width % stride != 0), "The width of the input tensor must be a multiple of stride");
ARM_COMPUTE_ERROR_ON_MSG((input_height % stride != 0), "The height of the input tensor must be a multiple of stride");
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), input_width / stride);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), input_height / stride);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channel * stride * stride);
return output_descriptor;
}
bool ReorgLayerNode::forward_descriptors()
{
if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
dst->desc() = configure_output(0);
return true;
}
return false;
}
TensorDescriptor ReorgLayerNode::configure_output(size_t idx) const
{
ARM_COMPUTE_UNUSED(idx);
ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
const Tensor *src = input(0);
ARM_COMPUTE_ERROR_ON(src == nullptr);
return compute_output_descriptor(src->desc(), _stride);
}
NodeType ReorgLayerNode::type() const
{
return NodeType::ReorgLayer;
}
void ReorgLayerNode::accept(INodeVisitor &v)
{
v.visit(*this);
}
} // namespace graph
} // namespace arm_compute