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/*
* Copyright (c) 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/FusedDepthwiseConvolutionBatchNormalizationNode.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
#include "arm_compute/graph/Utils.h"
namespace arm_compute
{
namespace graph
{
FusedDepthwiseConvolutionBatchNormalizationNode::FusedDepthwiseConvolutionBatchNormalizationNode(float epsilon,
PadStrideInfo info,
unsigned int depth_multiplier,
DepthwiseConvolutionMethod method,
ActivationLayerInfo fused_activation)
: _epsilon(epsilon), _info(std::move(info)), _depth_multiplier(depth_multiplier), _method(method), _fused_activation(fused_activation)
{
_input_edges.resize(7, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
}
void FusedDepthwiseConvolutionBatchNormalizationNode::set_depthwise_convolution_method(DepthwiseConvolutionMethod method)
{
_method = method;
}
DepthwiseConvolutionMethod FusedDepthwiseConvolutionBatchNormalizationNode::depthwise_convolution_method() const
{
return _method;
}
float FusedDepthwiseConvolutionBatchNormalizationNode::epsilon() const
{
return _epsilon;
}
PadStrideInfo FusedDepthwiseConvolutionBatchNormalizationNode::convolution_info() const
{
return _info;
}
unsigned int FusedDepthwiseConvolutionBatchNormalizationNode::depth_multiplier() const
{
return _depth_multiplier;
}
ActivationLayerInfo FusedDepthwiseConvolutionBatchNormalizationNode::fused_activation() const
{
return _fused_activation;
}
void FusedDepthwiseConvolutionBatchNormalizationNode::set_fused_activation(ActivationLayerInfo fused_activation)
{
_fused_activation = fused_activation;
}
TensorDescriptor FusedDepthwiseConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
const TensorDescriptor &weights_descriptor,
const PadStrideInfo &info,
int depth_multiplier)
{
unsigned int output_width = 0;
unsigned int output_height = 0;
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_channels = get_dimension_size(input_descriptor, DataLayoutDimension::CHANNEL);
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::HEIGHT), output_height);
output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier);
return output_descriptor;
}
bool FusedDepthwiseConvolutionBatchNormalizationNode::forward_descriptors()
{
if((input_id(0) != NullTensorID) && (input_id(1) != 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 FusedDepthwiseConvolutionBatchNormalizationNode::configure_output(size_t idx) const
{
ARM_COMPUTE_UNUSED(idx);
const Tensor *src = input(0);
const Tensor *weights = input(1);
ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info, _depth_multiplier);
return output_info;
}
NodeType FusedDepthwiseConvolutionBatchNormalizationNode::type() const
{
return FusedDepthwiseConvolutionBatchNormalizationNode::node_type;
}
void FusedDepthwiseConvolutionBatchNormalizationNode::accept(INodeVisitor &v)
{
v.visit(*this);
}
} // namespace graph
} // namespace arm_compute
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