1517 lines
52 KiB
C++
1517 lines
52 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_GRAPH_LAYERS_H
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#define ARM_COMPUTE_GRAPH_LAYERS_H
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#include "arm_compute/graph/GraphBuilder.h"
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#include "arm_compute/graph/Types.h"
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#include "arm_compute/graph/frontend/ILayer.h"
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#include "arm_compute/graph/frontend/IStream.h"
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#include "arm_compute/graph/frontend/SubStream.h"
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#include "arm_compute/core/utils/misc/Utility.h"
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#include <memory>
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#include <string>
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namespace arm_compute
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{
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namespace graph
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{
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namespace frontend
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{
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/** Input Layer */
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class InputLayer final : public ILayer
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{
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public:
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/** Construct an input layer.
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*
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* @param[in] desc Description of input tensor.
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* @param[in] accessor Accessor to get input tensor data from.
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*/
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InputLayer(TensorDescriptor desc, ITensorAccessorUPtr accessor)
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: _desc(desc), _accessor(std::move(accessor))
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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return GraphBuilder::add_input_node(s.graph(), common_params, _desc, std::move(_accessor));
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}
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private:
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TensorDescriptor _desc;
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ITensorAccessorUPtr _accessor;
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};
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/** Constant Layer */
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class ConstantLayer final : public ILayer
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{
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public:
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/** Construct a constant layer.
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*
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* @param[in] desc Description of input tensor.
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* @param[in] accessor Accessor to get input tensor data from.
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*/
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ConstantLayer(TensorDescriptor desc, ITensorAccessorUPtr accessor)
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: _desc(desc), _accessor(std::move(accessor))
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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return GraphBuilder::add_const_node(s.graph(), common_params, _desc, std::move(_accessor));
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}
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private:
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TensorDescriptor _desc;
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ITensorAccessorUPtr _accessor;
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};
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/** Output Layer */
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class OutputLayer final : public ILayer
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{
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public:
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/** Construct an output layer.
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*
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* @param[in] accessor Accessor to give output tensor data to.
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* @param[in] connection_idx (Optional) Input connection index
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*/
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OutputLayer(ITensorAccessorUPtr accessor, unsigned int connection_idx = 0)
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: _accessor(std::move(accessor)), _connection_idx(connection_idx)
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { s.tail_node(), _connection_idx };
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return GraphBuilder::add_output_node(s.graph(), common_params, input, std::move(_accessor));
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}
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private:
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ITensorAccessorUPtr _accessor;
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unsigned int _connection_idx;
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};
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/** Activation Layer */
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class ActivationLayer final : public ILayer
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{
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public:
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/** Construct an activation layer.
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*
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* @param[in] act_info Activation information
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* @param[in] out_quant_info (Optional) Output quantization info
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*/
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ActivationLayer(ActivationLayerInfo act_info,
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const QuantizationInfo out_quant_info = QuantizationInfo())
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: _act_info(act_info),
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_out_quant_info(std::move(out_quant_info))
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { s.tail_node(), 0 };
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return GraphBuilder::add_activation_node(s.graph(), common_params, input, _act_info, std::move(_out_quant_info));
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}
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private:
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ActivationLayerInfo _act_info;
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const QuantizationInfo _out_quant_info;
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};
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/** ArgMinMax Layer */
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class ArgMinMaxLayer final : public ILayer
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{
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public:
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/** Construct an activation layer.
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*
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* @param[in] op Reduction Operation: min or max
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* @param[in] axis Axis to perform reduction along
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* @param[in] out_data_type (Optional) Output tensor data type
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* @param[in] out_quant_info (Optional) Output quantization info
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*/
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ArgMinMaxLayer(ReductionOperation op,
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unsigned int axis,
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DataType out_data_type = DataType::UNKNOWN,
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const QuantizationInfo out_quant_info = QuantizationInfo())
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: _op(op),
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_axis(axis),
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_out_data_type(out_data_type),
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_out_quant_info(std::move(out_quant_info))
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{
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}
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/** Create layer and add to the given stream.
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*
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* @param[in] s Stream to add layer to.
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*
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* @return ID of the created node.
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*/
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { s.tail_node(), 0 };
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return GraphBuilder::add_arg_min_max_node(s.graph(), common_params, input, _op, _axis, _out_data_type, std::move(_out_quant_info));
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}
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private:
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ReductionOperation _op;
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unsigned int _axis;
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DataType _out_data_type;
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QuantizationInfo _out_quant_info;
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};
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/** Batchnormalization Layer */
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class BatchNormalizationLayer final : public ILayer
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{
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public:
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/** Construct a batch normalization layer.
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*
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* @param[in] mean Accessor to get mean tensor data from.
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* @param[in] var Accessor to get var tensor data from.
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* @param[in] gamma (Optional) Accessor to get gamma tensor data from. Default: nullptr.
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* @param[in] beta (Optional) Accessor to get beta tensor data from. Default: nullptr.
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* @param[in] epsilon (Optional) Epsilon value. Default: 0.001.
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*/
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BatchNormalizationLayer(ITensorAccessorUPtr mean,
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ITensorAccessorUPtr var,
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ITensorAccessorUPtr gamma = nullptr,
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ITensorAccessorUPtr beta = nullptr,
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float epsilon = 0.001f)
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: _mean(std::move(mean)), _var(std::move(var)), _gamma(std::move(gamma)), _beta(std::move(beta)), _epsilon(epsilon)
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{
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}
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NodeID create_layer(IStream &s) override
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{
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ARM_COMPUTE_ERROR_ON(_mean == nullptr);
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ARM_COMPUTE_ERROR_ON(_var == nullptr);
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { s.tail_node(), 0 };
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return GraphBuilder::add_batch_normalization_node(s.graph(), common_params, input, _epsilon,
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std::move(_mean), std::move(_var), std::move(_beta), std::move(_gamma));
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}
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private:
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ITensorAccessorUPtr _mean;
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ITensorAccessorUPtr _var;
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ITensorAccessorUPtr _gamma;
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ITensorAccessorUPtr _beta;
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float _epsilon;
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};
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/** Bounding Box Transform Layer */
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class BoundingBoxTransformLayer final : public ILayer
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{
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public:
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/** Construct a bounding box transform layer.
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*
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* @param[in] sub_stream_input Graph sub-stream for the input
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* @param[in] sub_stream_deltas Graph sub-stream for the deltas
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* @param[in] info Contains BoundingBox operation information described in @ref BoundingBoxTransformInfo.
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*/
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BoundingBoxTransformLayer(SubStream &&sub_stream_input, SubStream &&sub_stream_deltas, BoundingBoxTransformInfo info)
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: _ss_input(sub_stream_input), _ss_deltas(sub_stream_deltas), _bbox_info(info)
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{
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}
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/** Create layer and add to the given stream.
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*
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* @param[in] s Stream to add layer to.
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*
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* @return ID of the created node.
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*/
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { _ss_input.tail_node(), 0 };
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NodeIdxPair deltas = { _ss_deltas.tail_node(), 0 };
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return GraphBuilder::add_bounding_box_transform_node(s.graph(), common_params, input, deltas, _bbox_info);
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}
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private:
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SubStream _ss_input;
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SubStream _ss_deltas;
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BoundingBoxTransformInfo _bbox_info;
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};
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/** Channel Shuffle Layer */
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class ChannelShuffleLayer final : public ILayer
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{
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public:
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/** Construct a Channel Shuffle layer.
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*
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* @param[in] num_groups Number of groups
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*/
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ChannelShuffleLayer(unsigned int num_groups)
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: _num_groups(num_groups)
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeParams common_params = { name(), s.hints().target_hint };
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NodeIdxPair input = { s.tail_node(), 0 };
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return GraphBuilder::add_channel_shuffle_node(s.graph(), common_params, input, _num_groups);
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}
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private:
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unsigned int _num_groups;
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};
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/** Concat Layer */
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class ConcatLayer final : public ILayer
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{
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public:
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/** Construct a concatenation layer
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*
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* @param[in] sub_stream1 First graph branch
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* @param[in] sub_stream2 Second graph branch
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* @param[in] rest_sub_streams Rest sub-graph branches
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*/
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template <typename... Ts>
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ConcatLayer(SubStream &&sub_stream1, SubStream &&sub_stream2, Ts &&... rest_sub_streams)
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: _sub_streams(), _concat_descriptor(DataLayoutDimension::CHANNEL)
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{
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream1)));
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream2)));
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utility::for_each([&](SubStream && sub_stream)
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{
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
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},
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std::move(rest_sub_streams)...);
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}
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/** Construct a concatenation layer
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*
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* @param[in] concat_descriptor Concat layer descriptor
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* @param[in] sub_stream1 First graph branch
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* @param[in] sub_stream2 Second graph branch
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* @param[in] rest_sub_streams Rest sub-graph branches
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*/
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template <typename... Ts>
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ConcatLayer(descriptors::ConcatLayerDescriptor concat_descriptor, SubStream &&sub_stream1, SubStream &&sub_stream2, Ts &&... rest_sub_streams)
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: _sub_streams(), _concat_descriptor(concat_descriptor)
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{
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream1)));
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream2)));
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utility::for_each([&](SubStream && sub_stream)
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{
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
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},
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std::move(rest_sub_streams)...);
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}
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/** Construct a concat layer
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*
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* @param[in] sub_stream Sub-stream
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*/
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template <typename... Ts>
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ConcatLayer(SubStream &&sub_stream)
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: _sub_streams(), _concat_descriptor(DataLayoutDimension::CHANNEL)
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{
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_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
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}
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NodeID create_layer(IStream &s) override
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{
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NodeID nid = EmptyNodeID;
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NodeParams common_params = { name(), s.hints().target_hint };
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if(_sub_streams.size() == 1 && _sub_streams.at(0) != nullptr)
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{
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nid = _sub_streams[0]->tail_node();
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}
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else
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{
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// Collect tail nodes and concatenate
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std::vector<NodeIdxPair> nodes;
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for(auto &ss : _sub_streams)
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{
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if(ss && (ss->tail_node() != EmptyNodeID))
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{
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const auto tail_node = s.graph().node(ss->tail_node());
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if(tail_node != nullptr && tail_node->type() != NodeType::Output)
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{
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nodes.push_back({ ss->tail_node(), 0 });
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}
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}
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}
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nid = GraphBuilder::add_concatenate_node(s.graph(), common_params, nodes, _concat_descriptor);
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}
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return nid;
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}
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private:
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std::vector<std::unique_ptr<SubStream>> _sub_streams;
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descriptors::ConcatLayerDescriptor _concat_descriptor;
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};
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/** Convolution Layer */
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class ConvolutionLayer final : public ILayer
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{
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public:
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/** Construct a convolution layer.
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*
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* @param[in] conv_width Convolution width.
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* @param[in] conv_height Convolution height.
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* @param[in] ofm Output feature map.
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* @param[in] weights Accessor to get kernel weights from.
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* @param[in] bias Accessor to get kernel bias from.
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* @param[in] conv_info Padding and stride information.
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* @param[in] num_groups (Optional) Number of groups. Default: 1.
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* @param[in] weights_quant_info (Optional) Weights quantization information
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* @param[in] out_quant_info (Optional) Output quantization info
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*/
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ConvolutionLayer(unsigned int conv_width,
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unsigned int conv_height,
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unsigned int ofm,
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ITensorAccessorUPtr weights,
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ITensorAccessorUPtr bias,
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PadStrideInfo conv_info,
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unsigned int num_groups = 1,
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const QuantizationInfo weights_quant_info = QuantizationInfo(),
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const QuantizationInfo out_quant_info = QuantizationInfo())
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: _conv_width(conv_width),
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_conv_height(conv_height),
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_ofm(ofm),
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_conv_info(std::move(conv_info)),
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_num_groups(num_groups),
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_weights(std::move(weights)),
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_bias(std::move(bias)),
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_weights_quant_info(std::move(weights_quant_info)),
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_out_quant_info(std::move(out_quant_info))
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeIdxPair input = { s.tail_node(), 0 };
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NodeParams common_params = { name(), s.hints().target_hint };
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return GraphBuilder::add_convolution_node(s.graph(), common_params, input,
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Size2D(_conv_width, _conv_height), _ofm, _conv_info, _num_groups,
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s.hints().convolution_method_hint, s.hints().fast_math_hint,
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std::move(_weights), std::move(_bias), std::move(_weights_quant_info), std::move(_out_quant_info));
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}
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private:
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unsigned int _conv_width;
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unsigned int _conv_height;
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unsigned int _ofm;
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const PadStrideInfo _conv_info;
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unsigned int _num_groups;
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ITensorAccessorUPtr _weights;
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ITensorAccessorUPtr _bias;
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const QuantizationInfo _weights_quant_info;
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const QuantizationInfo _out_quant_info;
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};
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/** Deconvolution Layer */
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class DeconvolutionLayer final : public ILayer
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{
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public:
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/** Construct a convolution layer.
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*
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* @param[in] conv_width Convolution width.
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* @param[in] conv_height Convolution height.
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* @param[in] ofm Output feature map.
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* @param[in] weights Accessor to get kernel weights from.
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* @param[in] bias Accessor to get kernel bias from.
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* @param[in] deconv_info Padding and stride information.
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*/
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DeconvolutionLayer(unsigned int conv_width,
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unsigned int conv_height,
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unsigned int ofm,
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ITensorAccessorUPtr weights,
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ITensorAccessorUPtr bias,
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PadStrideInfo deconv_info)
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: _conv_width(conv_width),
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_conv_height(conv_height),
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_ofm(ofm),
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_deconv_info(std::move(deconv_info)),
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_weights(std::move(weights)),
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_bias(std::move(bias))
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{
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}
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NodeID create_layer(IStream &s) override
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{
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NodeIdxPair input = { s.tail_node(), 0 };
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NodeParams common_params = { name(), s.hints().target_hint };
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return GraphBuilder::add_deconvolution_node(s.graph(), common_params, input,
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Size2D(_conv_width, _conv_height), _ofm, _deconv_info,
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std::move(_weights), std::move(_bias));
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}
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private:
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unsigned int _conv_width;
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unsigned int _conv_height;
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unsigned int _ofm;
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const PadStrideInfo _deconv_info;
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ITensorAccessorUPtr _weights;
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ITensorAccessorUPtr _bias;
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};
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/** Depthwise Convolution Layer */
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class DepthwiseConvolutionLayer final : public ILayer
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{
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public:
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/** Construct a depthwise convolution layer.
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|
*
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* @param[in] conv_width Convolution width.
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* @param[in] conv_height Convolution height.
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* @param[in] weights Accessor to get kernel weights from.
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* @param[in] bias Accessor to get kernel bias from.
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* @param[in] conv_info Padding and stride information.
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* @param[in] depth_multiplier (Optional) Depth multiplier parameter.
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* @param[in] weights_quant_info (Optional) Quantization info used for weights
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* @param[in] out_quant_info (Optional) Output quantization info
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|
*/
|
|
DepthwiseConvolutionLayer(unsigned int conv_width,
|
|
unsigned int conv_height,
|
|
ITensorAccessorUPtr weights,
|
|
ITensorAccessorUPtr bias,
|
|
PadStrideInfo conv_info,
|
|
int depth_multiplier = 1,
|
|
const QuantizationInfo weights_quant_info = QuantizationInfo(),
|
|
const QuantizationInfo out_quant_info = QuantizationInfo())
|
|
: _conv_width(conv_width),
|
|
_conv_height(conv_height),
|
|
_conv_info(std::move(conv_info)),
|
|
_weights(std::move(weights)),
|
|
_bias(std::move(bias)),
|
|
_depth_multiplier(depth_multiplier),
|
|
_weights_quant_info(std::move(weights_quant_info)),
|
|
_out_quant_info(std::move(out_quant_info))
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
return GraphBuilder::add_depthwise_convolution_node(s.graph(), common_params,
|
|
input, Size2D(_conv_width, _conv_height), _conv_info, _depth_multiplier,
|
|
s.hints().depthwise_convolution_method_hint,
|
|
std::move(_weights), std::move(_bias), std::move(_weights_quant_info), std::move(_out_quant_info));
|
|
}
|
|
|
|
private:
|
|
unsigned int _conv_width;
|
|
unsigned int _conv_height;
|
|
const PadStrideInfo _conv_info;
|
|
ITensorAccessorUPtr _weights;
|
|
ITensorAccessorUPtr _bias;
|
|
int _depth_multiplier;
|
|
const QuantizationInfo _weights_quant_info;
|
|
const QuantizationInfo _out_quant_info;
|
|
};
|
|
|
|
/** DepthToSpace Layer */
|
|
class DepthToSpaceLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct an DepthToSpace layer.
|
|
*
|
|
* @param[in] block_shape Block size to rearranged
|
|
*/
|
|
DepthToSpaceLayer(int32_t block_shape)
|
|
: _block_shape(block_shape)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_depth_to_space_node(s.graph(), common_params, input, _block_shape);
|
|
}
|
|
|
|
private:
|
|
int32_t _block_shape;
|
|
};
|
|
|
|
/** Dequantization Layer */
|
|
class DequantizationLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a dequantization layer.
|
|
*
|
|
*/
|
|
DequantizationLayer()
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_dequantization_node(s.graph(), common_params, input);
|
|
}
|
|
};
|
|
|
|
/** DetectionOutput Layer */
|
|
class DetectionOutputLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a detection output layer.
|
|
*
|
|
* @param[in] sub_stream_conf Confidence graph sub-stream.
|
|
* @param[in] sub_stream_prior PriorBox graph sub-stream.
|
|
* @param[in] detect_info DetectionOutput parameters.
|
|
*/
|
|
DetectionOutputLayer(SubStream &&sub_stream_conf, SubStream &&sub_stream_prior, const DetectionOutputLayerInfo &detect_info)
|
|
: _ss_conf(std::move(sub_stream_conf)), _ss_prior(std::move(sub_stream_prior)), _detect_info(detect_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input_loc = { s.tail_node(), 0 };
|
|
NodeIdxPair input_conf = { _ss_conf.tail_node(), 0 };
|
|
NodeIdxPair input_priorbox = { _ss_prior.tail_node(), 0 };
|
|
return GraphBuilder::add_detection_output_node(s.graph(), common_params, input_loc, input_conf, input_priorbox, _detect_info);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss_conf;
|
|
SubStream _ss_prior;
|
|
DetectionOutputLayerInfo _detect_info;
|
|
};
|
|
/** DetectionOutputPostProcess Layer */
|
|
class DetectionPostProcessLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a detection output layer.
|
|
*
|
|
* @param[in] sub_stream_class_prediction Class prediction graph sub-stream.
|
|
* @param[in] detect_info DetectionOutput parameters.
|
|
* @param[in] anchors Accessor to get anchors tensor data from.
|
|
* @param[in] out_quant_info (Optional) Output quantization info
|
|
*/
|
|
DetectionPostProcessLayer(SubStream &&sub_stream_class_prediction, DetectionPostProcessLayerInfo detect_info, ITensorAccessorUPtr anchors,
|
|
const QuantizationInfo out_quant_info = QuantizationInfo())
|
|
: _sub_stream_class_prediction(std::move(sub_stream_class_prediction)), _detect_info(detect_info), _anchors(std::move(anchors)), _out_quant_info(std::move(out_quant_info))
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
ARM_COMPUTE_ERROR_ON(_anchors == nullptr);
|
|
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input_box_encoding = { s.tail_node(), 0 };
|
|
NodeIdxPair input_class_prediction = { _sub_stream_class_prediction.tail_node(), 0 };
|
|
return GraphBuilder::add_detection_post_process_node(s.graph(), common_params, input_box_encoding, input_class_prediction, _detect_info, std::move(_anchors), std::move(_out_quant_info));
|
|
}
|
|
|
|
private:
|
|
SubStream _sub_stream_class_prediction;
|
|
DetectionPostProcessLayerInfo _detect_info;
|
|
ITensorAccessorUPtr _anchors;
|
|
const QuantizationInfo _out_quant_info;
|
|
};
|
|
/** Dummy Layer */
|
|
class DummyLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a dummy layer.
|
|
*
|
|
* @param[in] shape Output shape
|
|
*/
|
|
DummyLayer(TensorShape shape)
|
|
: _shape(shape)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_dummy_node(s.graph(), common_params, input, _shape);
|
|
}
|
|
|
|
private:
|
|
TensorShape _shape;
|
|
};
|
|
|
|
class EltwiseLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct an element-wise operation layer
|
|
*
|
|
* @param[in] sub_stream0 First graph sub-stream
|
|
* @param[in] sub_stream1 First graph sub-stream
|
|
* @param[in] op Element-wise operation to perform
|
|
*/
|
|
EltwiseLayer(SubStream &&sub_stream0, SubStream &&sub_stream1, EltwiseOperation op)
|
|
: _ss0(std::move(sub_stream0)), _ss1(std::move(sub_stream1)), _op(op)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input0 = { _ss0.tail_node(), 0 };
|
|
NodeIdxPair input1 = { _ss1.tail_node(), 0 };
|
|
|
|
return GraphBuilder::add_elementwise_node(s.graph(), common_params, input0, input1, _op);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss0;
|
|
SubStream _ss1;
|
|
EltwiseOperation _op;
|
|
};
|
|
/** Flatten Layer */
|
|
class FlattenLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a flatten layer. */
|
|
FlattenLayer()
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_flatten_node(s.graph(), common_params, input);
|
|
}
|
|
};
|
|
|
|
/** Fully Connected Layer */
|
|
class FullyConnectedLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a fully connected layer.
|
|
*
|
|
* @param[in] num_outputs Number of outputs.
|
|
* @param[in] weights Accessor to get weights from.
|
|
* @param[in] bias Accessor to get bias from.
|
|
* @param[in] fc_info (Optional) Fully connected layer metadata
|
|
* @param[in] weights_quant_info (Optional) Weights quantization information
|
|
* @param[in] out_quant_info (Optional) Output quantization info
|
|
*/
|
|
FullyConnectedLayer(unsigned int num_outputs,
|
|
ITensorAccessorUPtr weights,
|
|
ITensorAccessorUPtr bias,
|
|
const FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
|
|
const QuantizationInfo weights_quant_info = QuantizationInfo(),
|
|
const QuantizationInfo out_quant_info = QuantizationInfo())
|
|
: _num_outputs(num_outputs),
|
|
_weights(std::move(weights)),
|
|
_bias(std::move(bias)),
|
|
_weights_ss(nullptr),
|
|
_bias_ss(nullptr),
|
|
_fc_info(fc_info),
|
|
_weights_quant_info(std::move(weights_quant_info)),
|
|
_out_quant_info(std::move(out_quant_info))
|
|
{
|
|
}
|
|
|
|
/** Construct a fully connected layer.
|
|
*
|
|
* @param[in] num_outputs Number of outputs.
|
|
* @param[in] sub_stream_weights Graph sub-stream for the weights.
|
|
* @param[in] sub_stream_bias Graph sub-stream for the bias.
|
|
* @param[in] fc_info (Optional) Fully connected layer metadata
|
|
* @param[in] weights_quant_info (Optional) Weights quantization information
|
|
* @param[in] out_quant_info (Optional) Output quantization info
|
|
*/
|
|
FullyConnectedLayer(unsigned int num_outputs,
|
|
SubStream sub_stream_weights,
|
|
SubStream sub_stream_bias,
|
|
const FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
|
|
const QuantizationInfo weights_quant_info = QuantizationInfo(),
|
|
const QuantizationInfo out_quant_info = QuantizationInfo())
|
|
: _num_outputs(num_outputs),
|
|
_weights(nullptr),
|
|
_bias(nullptr),
|
|
_weights_ss(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream_weights))),
|
|
_bias_ss(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream_bias))),
|
|
_fc_info(fc_info),
|
|
_weights_quant_info(std::move(weights_quant_info)),
|
|
_out_quant_info(std::move(out_quant_info))
|
|
{
|
|
}
|
|
|
|
/** Create layer and add to the given stream.
|
|
*
|
|
* @param[in] s Stream to add layer to.
|
|
*
|
|
* @return ID of the created node.
|
|
*/
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
if(_weights != nullptr)
|
|
{
|
|
return GraphBuilder::add_fully_connected_layer(s.graph(), common_params, input, _num_outputs,
|
|
std::move(_weights), std::move(_bias), _fc_info,
|
|
std::move(_weights_quant_info), std::move(_out_quant_info));
|
|
}
|
|
else
|
|
{
|
|
ARM_COMPUTE_ERROR_ON(_weights_ss == nullptr);
|
|
|
|
NodeID bias_nid = (_bias_ss == nullptr) ? EmptyNodeID : _bias_ss->tail_node();
|
|
return GraphBuilder::add_fully_connected_layer(s.graph(), common_params, input, _num_outputs,
|
|
_weights_ss->tail_node(), bias_nid, _fc_info,
|
|
std::move(_out_quant_info));
|
|
}
|
|
}
|
|
|
|
private:
|
|
unsigned int _num_outputs;
|
|
ITensorAccessorUPtr _weights;
|
|
ITensorAccessorUPtr _bias;
|
|
std::unique_ptr<SubStream> _weights_ss;
|
|
std::unique_ptr<SubStream> _bias_ss;
|
|
const FullyConnectedLayerInfo _fc_info;
|
|
const QuantizationInfo _weights_quant_info;
|
|
const QuantizationInfo _out_quant_info;
|
|
};
|
|
|
|
/** Generate Proposals Layer */
|
|
class GenerateProposalsLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a generate proposals layer.
|
|
*
|
|
* @param[in] ss_scores Graph sub-stream for the scores.
|
|
* @param[in] ss_deltas Graph sub-stream for the deltas.
|
|
* @param[in] ss_anchors Graph sub-stream for the anchors.
|
|
* @param[in] info Generate Proposals operation information.
|
|
*/
|
|
GenerateProposalsLayer(SubStream &&ss_scores, SubStream &&ss_deltas, SubStream &&ss_anchors, GenerateProposalsInfo info)
|
|
: _ss_scores(std::move(ss_scores)), _ss_deltas(std::move(ss_deltas)), _ss_anchors(std::move(ss_anchors)), _info(info)
|
|
{
|
|
}
|
|
|
|
/** Create layer and add to the given stream.
|
|
*
|
|
* @param[in] s Stream to add layer to.
|
|
*
|
|
* @return ID of the created node.
|
|
*/
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair scores = { _ss_scores.tail_node(), 0 };
|
|
NodeIdxPair deltas = { _ss_deltas.tail_node(), 0 };
|
|
NodeIdxPair anchors = { _ss_anchors.tail_node(), 0 };
|
|
return GraphBuilder::add_generate_proposals_node(s.graph(), common_params, scores, deltas, anchors, _info);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss_scores;
|
|
SubStream _ss_deltas;
|
|
SubStream _ss_anchors;
|
|
GenerateProposalsInfo _info;
|
|
};
|
|
|
|
/** L2 Normalize Layer */
|
|
class L2NormalizeLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a L2 Normalize layer.
|
|
*
|
|
* @param[in] axis Axis to perform normalization on
|
|
* @param[in] epsilon Lower bound value for the normalization
|
|
*/
|
|
L2NormalizeLayer(int axis, float epsilon)
|
|
: _axis(axis), _epsilon(epsilon)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_l2_normalize_node(s.graph(), common_params, input, _axis, _epsilon);
|
|
}
|
|
|
|
private:
|
|
int _axis;
|
|
float _epsilon;
|
|
};
|
|
|
|
/** Normalization Layer */
|
|
class NormalizationLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a normalization layer.
|
|
*
|
|
* @param[in] norm_info Normalization information.
|
|
*/
|
|
NormalizationLayer(NormalizationLayerInfo norm_info)
|
|
: _norm_info(norm_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_normalization_node(s.graph(), common_params, input, _norm_info);
|
|
}
|
|
|
|
private:
|
|
NormalizationLayerInfo _norm_info;
|
|
};
|
|
|
|
/** Normalize planar YUV Layer */
|
|
class NormalizePlanarYUVLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a normalize planar YUV layer.
|
|
*
|
|
* @param[in] mean Accessor to get mean tensor data from.
|
|
* @param[in] std Accessor to get std tensor data from.
|
|
*/
|
|
NormalizePlanarYUVLayer(ITensorAccessorUPtr mean,
|
|
ITensorAccessorUPtr std)
|
|
: _mean(std::move(mean)), _std(std::move(std))
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
ARM_COMPUTE_ERROR_ON(_mean == nullptr);
|
|
ARM_COMPUTE_ERROR_ON(_std == nullptr);
|
|
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_normalize_planar_yuv_node(s.graph(), common_params, input,
|
|
std::move(_mean), std::move(_std));
|
|
}
|
|
|
|
private:
|
|
ITensorAccessorUPtr _mean;
|
|
ITensorAccessorUPtr _std;
|
|
};
|
|
|
|
/** Pad Layer */
|
|
class PadLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a pad layer.
|
|
*
|
|
* @param[in] padding The padding for each spatial dimension of the input tensor. The pair padding[i]
|
|
* specifies the front and the end padding in the i-th dimension.
|
|
* @param[in] pad_value Padding value to use. Defaults to 0.
|
|
*/
|
|
PadLayer(PaddingList padding, PixelValue pad_value = PixelValue())
|
|
: _padding(padding), _pad_value(pad_value)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_pad_node(s.graph(), common_params, input, _padding, _pad_value);
|
|
}
|
|
|
|
private:
|
|
PaddingList _padding;
|
|
PixelValue _pad_value;
|
|
};
|
|
|
|
/** Permute Layer */
|
|
class PermuteLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a permute layer.
|
|
*
|
|
* @param[in] perm Permutation vector.
|
|
* @param[in] layout (Optional) Data layout to assign to permuted tensor.
|
|
* If UNKNOWN then the input's layout will be used.
|
|
*/
|
|
PermuteLayer(PermutationVector perm, DataLayout layout = DataLayout::UNKNOWN)
|
|
: _perm(perm), _layout(layout)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_permute_node(s.graph(), common_params, input, _perm, _layout);
|
|
}
|
|
|
|
private:
|
|
PermutationVector _perm;
|
|
DataLayout _layout;
|
|
};
|
|
|
|
/** Pooling Layer */
|
|
class PoolingLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a pooling layer.
|
|
*
|
|
* @param[in] pool_info Pooling information.
|
|
*/
|
|
PoolingLayer(PoolingLayerInfo pool_info)
|
|
: _pool_info(pool_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_pooling_node(s.graph(), common_params, input, _pool_info);
|
|
}
|
|
|
|
private:
|
|
PoolingLayerInfo _pool_info;
|
|
};
|
|
|
|
/** PRelu Layer */
|
|
class PReluLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct an PRelu operation layer
|
|
*
|
|
* @param[in] sub_stream0 First graph sub-stream
|
|
* @param[in] sub_stream1 First graph sub-stream
|
|
*/
|
|
PReluLayer(SubStream &&sub_stream0, SubStream &&sub_stream1)
|
|
: _ss0(std::move(sub_stream0)), _ss1(std::move(sub_stream1))
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { _ss0.tail_node(), 0 };
|
|
NodeIdxPair alpha = { _ss1.tail_node(), 0 };
|
|
|
|
return GraphBuilder::add_prelu_node(s.graph(), common_params, input, alpha);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss0;
|
|
SubStream _ss1;
|
|
};
|
|
|
|
/** Print Layer */
|
|
class PrintLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a print layer.
|
|
*
|
|
* Example usage to locally dequantize and print a tensor:
|
|
*
|
|
* Tensor *output = new Tensor();
|
|
* const auto transform = [output](ITensor *input)
|
|
* {
|
|
* output->allocator()->init(*input->info());
|
|
* output->info()->set_data_type(DataType::F32);
|
|
* output->allocator()->allocate();
|
|
*
|
|
* Window win;
|
|
* win.use_tensor_dimensions(input->info()->tensor_shape());
|
|
* Iterator in(input, win);
|
|
* Iterator out(output, win);
|
|
* execute_window_loop(win, [&](const Coordinates &)
|
|
* {
|
|
* *(reinterpret_cast<float *>(out.ptr())) = dequantize_qasymm8(*in.ptr(), input->info()->quantization_info().uniform());
|
|
* }, in, out);
|
|
*
|
|
* return output;
|
|
* };
|
|
*
|
|
* graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), get_input_accessor(common_params, nullptr, false))
|
|
* << ...
|
|
* << \\ CNN Layers
|
|
* << ...
|
|
* << PrintLayer(std::cout, IOFormatInfo(), transform)
|
|
* << ...
|
|
* << OutputLayer(get_output_accessor(common_params, 5));
|
|
*
|
|
* @param[in] stream Output stream.
|
|
* @param[in] format_info (Optional) Format info.
|
|
* @param[in] transform (Optional) Input transform function.
|
|
*/
|
|
PrintLayer(std::ostream &stream, const IOFormatInfo &format_info = IOFormatInfo(), const std::function<ITensor *(ITensor *)> transform = nullptr)
|
|
: _stream(stream), _format_info(format_info), _transform(transform)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_print_node(s.graph(), common_params, input, _stream, _format_info, _transform);
|
|
}
|
|
|
|
private:
|
|
std::ostream &_stream;
|
|
const IOFormatInfo &_format_info;
|
|
const std::function<ITensor *(ITensor *)> _transform;
|
|
};
|
|
|
|
/** PriorBox Layer */
|
|
class PriorBoxLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a priorbox layer.
|
|
*
|
|
* @param[in] sub_stream First graph sub-stream
|
|
* @param[in] prior_info PriorBox parameters.
|
|
*/
|
|
PriorBoxLayer(SubStream &&sub_stream, const PriorBoxLayerInfo &prior_info)
|
|
: _ss(std::move(sub_stream)), _prior_info(prior_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input0 = { s.tail_node(), 0 };
|
|
NodeIdxPair input1 = { _ss.tail_node(), 0 };
|
|
return GraphBuilder::add_priorbox_node(s.graph(), common_params, input0, input1, _prior_info);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss;
|
|
PriorBoxLayerInfo _prior_info;
|
|
};
|
|
|
|
/** Quantization Layer */
|
|
class QuantizationLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a quantization layer.
|
|
*
|
|
* @param[in] out_quant_info Output tensor quantization info
|
|
*/
|
|
QuantizationLayer(QuantizationInfo out_quant_info)
|
|
: _out_quant_info(out_quant_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_quantization_node(s.graph(), common_params, input, _out_quant_info);
|
|
}
|
|
|
|
private:
|
|
QuantizationInfo _out_quant_info;
|
|
};
|
|
|
|
/** Reduction Layer */
|
|
class ReductionLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a reduction layer.
|
|
*
|
|
* @param[in] op Reduction operation
|
|
* @param[in] axis Reduction axis
|
|
* @param[in] keep_dims (Optional) Whether to keep the reduced dimension after the operation. Defaults to true.
|
|
*/
|
|
ReductionLayer(ReductionOperation op, unsigned int axis, bool keep_dims)
|
|
: _op(op), _axis(axis), _keep_dims(keep_dims)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_reduction_operation_node(s.graph(), common_params, input, _op, _axis, _keep_dims);
|
|
}
|
|
|
|
private:
|
|
ReductionOperation _op;
|
|
unsigned int _axis;
|
|
bool _keep_dims;
|
|
};
|
|
|
|
/** Reorg Layer */
|
|
class ReorgLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a reorg layer.
|
|
*
|
|
* @param[in] stride Stride value to use for reorganizing the values in the output tensor.
|
|
* It defines the spatial distance between 2 consecutive pixels in the x and y direction
|
|
*/
|
|
ReorgLayer(int stride)
|
|
: _stride(stride)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_reorg_node(s.graph(), common_params, input, _stride);
|
|
}
|
|
|
|
private:
|
|
int _stride;
|
|
};
|
|
|
|
/** Reshape Layer */
|
|
class ReshapeLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a reshape layer.
|
|
*
|
|
* @param[in] shape Target shape.
|
|
*/
|
|
ReshapeLayer(TensorShape shape)
|
|
: _shape(shape)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_reshape_node(s.graph(), common_params, input, _shape);
|
|
}
|
|
|
|
private:
|
|
TensorShape _shape;
|
|
};
|
|
|
|
/** Resize Layer */
|
|
class ResizeLayer final : public ILayer
|
|
{
|
|
public:
|
|
ResizeLayer(InterpolationPolicy policy, float width_scale, float height_scale)
|
|
: _policy(policy), _width_scale(width_scale), _height_scale(height_scale)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_resize_node(s.graph(), common_params, input, _policy, _width_scale, _height_scale);
|
|
}
|
|
|
|
private:
|
|
InterpolationPolicy _policy;
|
|
float _width_scale;
|
|
float _height_scale;
|
|
};
|
|
|
|
/** ROIAlign Layer */
|
|
class ROIAlignLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a RoiAlign layer.
|
|
*
|
|
* @param[in] sub_stream_input Graph sub-stream for the input
|
|
* @param[in] sub_stream_rois Graph sub-stream for the rois
|
|
* @param[in] pool_info Pooling information.
|
|
*/
|
|
ROIAlignLayer(SubStream &&sub_stream_input, SubStream &&sub_stream_rois, ROIPoolingLayerInfo pool_info)
|
|
: _ss_input(sub_stream_input), _ss_rois(sub_stream_rois), _pool_info(pool_info)
|
|
{
|
|
}
|
|
|
|
/** Prevent instances of this class from being copy constructed */
|
|
ROIAlignLayer(const ROIAlignLayer &) = delete;
|
|
/** Prevent instances of this class from being copied */
|
|
ROIAlignLayer &operator=(const ROIAlignLayer &) = delete;
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { _ss_input.tail_node(), 0 };
|
|
NodeIdxPair rois = { _ss_rois.tail_node(), 0 };
|
|
return GraphBuilder::add_roi_align_node(s.graph(), common_params, input, rois, _pool_info);
|
|
}
|
|
|
|
private:
|
|
SubStream _ss_input;
|
|
SubStream _ss_rois;
|
|
ROIPoolingLayerInfo _pool_info;
|
|
};
|
|
|
|
/** Scale Layer */
|
|
class ScaleLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a scale layer.
|
|
*
|
|
* @param[in] mul_w Accessor to get mul weight from.
|
|
* @param[in] add_w Accessor to get add weight from.
|
|
*/
|
|
ScaleLayer(ITensorAccessorUPtr mul_w,
|
|
ITensorAccessorUPtr add_w)
|
|
: _mul_w(std::move(mul_w)), _add_w(std::move(add_w))
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_scale_layer(s.graph(), common_params, input, std::move(_mul_w), std::move(_add_w));
|
|
}
|
|
|
|
private:
|
|
ITensorAccessorUPtr _mul_w;
|
|
ITensorAccessorUPtr _add_w;
|
|
};
|
|
|
|
/** Slice Layer */
|
|
class SliceLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a slice layer.
|
|
*
|
|
* @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
|
|
* @param[in] ends The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
|
|
*/
|
|
SliceLayer(Coordinates &starts, Coordinates &ends)
|
|
: _starts(starts), _ends(ends)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_slice_node(s.graph(), common_params, input, _starts, _ends);
|
|
}
|
|
|
|
private:
|
|
Coordinates _starts;
|
|
Coordinates _ends;
|
|
};
|
|
|
|
/** Softmax Layer */
|
|
class SoftmaxLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a softmax layer.
|
|
*
|
|
* @param[in] beta (Optional) Beta value. Default 1.0.
|
|
*/
|
|
SoftmaxLayer(float beta = 1.0f)
|
|
: _beta(beta)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_softmax_node(s.graph(), common_params, input, _beta);
|
|
}
|
|
|
|
private:
|
|
float _beta;
|
|
};
|
|
|
|
/** Stack Layer */
|
|
class StackLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a concatenation layer
|
|
*
|
|
* @param[in] sub_stream1 First graph branch
|
|
* @param[in] sub_stream2 Second graph branch
|
|
* @param[in] rest_sub_streams Rest sub-graph branches
|
|
*/
|
|
template <typename... Ts>
|
|
StackLayer(SubStream &&sub_stream1, SubStream &&sub_stream2, Ts &&... rest_sub_streams)
|
|
: _sub_streams(), _axis(0)
|
|
{
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream1)));
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream2)));
|
|
|
|
utility::for_each([&](SubStream && sub_stream)
|
|
{
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
|
|
},
|
|
std::move(rest_sub_streams)...);
|
|
}
|
|
/** Construct a concatenation layer
|
|
*
|
|
* @param[in] axis Stack layer axis along which to stack the inputs
|
|
* @param[in] sub_stream1 First graph branch
|
|
* @param[in] sub_stream2 Second graph branch
|
|
* @param[in] rest_sub_streams Rest sub-graph branches
|
|
*/
|
|
template <typename... Ts>
|
|
StackLayer(int axis, SubStream &&sub_stream1, SubStream &&sub_stream2, Ts &&... rest_sub_streams)
|
|
: _sub_streams(), _axis(axis)
|
|
{
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream1)));
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream2)));
|
|
|
|
utility::for_each([&](SubStream && sub_stream)
|
|
{
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
|
|
},
|
|
std::move(rest_sub_streams)...);
|
|
}
|
|
/** Construct a concat layer
|
|
*
|
|
* @param[in] sub_stream Sub-stream
|
|
*/
|
|
template <typename... Ts>
|
|
StackLayer(SubStream &&sub_stream)
|
|
: _sub_streams(), _axis(0)
|
|
{
|
|
_sub_streams.push_back(arm_compute::support::cpp14::make_unique<SubStream>(std::move(sub_stream)));
|
|
}
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeID nid = EmptyNodeID;
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
if(_sub_streams.size() == 1 && _sub_streams.at(0) != nullptr)
|
|
{
|
|
nid = _sub_streams[0]->tail_node();
|
|
}
|
|
else
|
|
{
|
|
// Collect tail nodes and stack
|
|
std::vector<NodeIdxPair> nodes;
|
|
for(auto &ss : _sub_streams)
|
|
{
|
|
if(ss && (ss->tail_node() != EmptyNodeID))
|
|
{
|
|
const auto tail_node = s.graph().node(ss->tail_node());
|
|
if(tail_node != nullptr && tail_node->type() != NodeType::Output)
|
|
{
|
|
nodes.push_back({ ss->tail_node(), 0 });
|
|
}
|
|
}
|
|
}
|
|
nid = GraphBuilder::add_stack_node(s.graph(), common_params, nodes, _axis);
|
|
}
|
|
return nid;
|
|
}
|
|
|
|
private:
|
|
std::vector<std::unique_ptr<SubStream>> _sub_streams;
|
|
int _axis;
|
|
};
|
|
|
|
/** StridedSlice Layer */
|
|
class StridedSliceLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a strided slice layer.
|
|
*
|
|
* @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input).
|
|
* @param[in] ends The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input).
|
|
* @param[in] strides The strides of the dimensions of the input tensor to be sliced. The length must be of rank(input).
|
|
* @param[in] strided_slice_info Contains masks for the starts, ends and strides
|
|
*/
|
|
StridedSliceLayer(Coordinates &starts, Coordinates &ends, BiStrides &strides, StridedSliceLayerInfo strided_slice_info)
|
|
: _starts(starts), _ends(ends), _strides(strides), _info(strided_slice_info)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_strided_slice_node(s.graph(), common_params, input, _starts, _ends, _strides, _info);
|
|
}
|
|
|
|
private:
|
|
Coordinates _starts;
|
|
Coordinates _ends;
|
|
BiStrides _strides;
|
|
StridedSliceLayerInfo _info;
|
|
};
|
|
|
|
/** Upsample Layer */
|
|
class UpsampleLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a Upsample layer.
|
|
*
|
|
* @param[in] info Stride info
|
|
* @param[in] upsampling_policy Upsampling policy
|
|
*/
|
|
UpsampleLayer(Size2D info, InterpolationPolicy upsampling_policy)
|
|
: _info(info), _upsampling_policy(upsampling_policy)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_upsample_node(s.graph(), common_params, input, _info, _upsampling_policy);
|
|
}
|
|
|
|
private:
|
|
Size2D _info;
|
|
InterpolationPolicy _upsampling_policy;
|
|
};
|
|
|
|
/** YOLO Layer */
|
|
class YOLOLayer final : public ILayer
|
|
{
|
|
public:
|
|
/** Construct a YOLO layer.
|
|
*
|
|
* @param[in] act_info Activation info
|
|
* @param[in] num_classes Number of classes to activate
|
|
*/
|
|
YOLOLayer(ActivationLayerInfo act_info, int32_t num_classes)
|
|
: _act_info(act_info), _num_classes(num_classes)
|
|
{
|
|
}
|
|
|
|
NodeID create_layer(IStream &s) override
|
|
{
|
|
NodeParams common_params = { name(), s.hints().target_hint };
|
|
NodeIdxPair input = { s.tail_node(), 0 };
|
|
return GraphBuilder::add_yolo_node(s.graph(), common_params, input, _act_info, _num_classes);
|
|
}
|
|
|
|
private:
|
|
ActivationLayerInfo _act_info;
|
|
int32_t _num_classes;
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};
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} // namespace frontend
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} // namespace graph
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_GRAPH_LAYERS_H */
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