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/*
* 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.
*/
#ifndef __ARM_COMPUTE_GRAPH_TYPES_H__
#define __ARM_COMPUTE_GRAPH_TYPES_H__
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/CLTunerTypes.h"
#include <limits>
#include <string>
namespace arm_compute
{
namespace graph
{
using arm_compute::CLTunerMode;
using arm_compute::Status;
using arm_compute::Coordinates;
using arm_compute::DataType;
using arm_compute::DataLayout;
using arm_compute::DataLayoutDimension;
using arm_compute::TensorShape;
using arm_compute::Size2D;
using arm_compute::PermutationVector;
using arm_compute::ActivationLayerInfo;
using arm_compute::DetectionOutputLayerInfo;
using arm_compute::DetectionPostProcessLayerInfo;
using arm_compute::NormType;
using arm_compute::NormalizationLayerInfo;
using arm_compute::FullyConnectedLayerInfo;
using arm_compute::PadStrideInfo;
using arm_compute::PoolingLayerInfo;
using arm_compute::PoolingType;
using arm_compute::PriorBoxLayerInfo;
using arm_compute::DimensionRoundingType;
using arm_compute::InterpolationPolicy;
using GraphID = unsigned int;
using TensorID = unsigned int;
using NodeID = unsigned int;
using EdgeID = unsigned int;
using Activation = arm_compute::ActivationLayerInfo::ActivationFunction;
/**< Constant TensorID specifying an equivalent of null tensor */
constexpr TensorID NullTensorID = std::numeric_limits<TensorID>::max();
/**< Constant NodeID specifying an equivalent of null node */
constexpr NodeID EmptyNodeID = std::numeric_limits<NodeID>::max();
/**< Constant EdgeID specifying an equivalent of null edge */
constexpr EdgeID EmptyEdgeID = std::numeric_limits<EdgeID>::max();
// Forward declarations
struct TensorDescriptor;
/** Graph configuration structure */
struct GraphConfig
{
bool use_function_memory_manager{ true }; /**< Use a memory manager to manage per-funcion auxilary memory */
bool use_function_weights_manager{ true }; /**< Use a weights manager to manage transformed weights */
bool use_transition_memory_manager{ true }; /**< Use a memory manager to manager transition buffer memory */
bool use_tuner{ false }; /**< Use a tuner in tunable backends */
CLTunerMode tuner_mode{ CLTunerMode::EXHAUSTIVE }; /**< Tuner mode to be used by the CL tuner */
int num_threads{ -1 }; /**< Number of threads to use (thread capable backends), if 0 the backend will auto-initialize, if -1 the backend will stay as it is. */
std::string tuner_file{ "acl_tuner.csv" }; /**< File to load/store tuning values from */
};
/**< Device target types */
enum class Target
{
UNSPECIFIED, /**< Unspecified Target */
NEON, /**< NEON capable target device */
CL, /**< OpenCL capable target device */
GC, /**< GLES compute capable target device */
};
/** Supported Element-wise operations */
enum class EltwiseOperation
{
Add, /**< Arithmetic addition */
Sub, /**< Arithmetic subtraction */
Mul /**< Arithmetic multiplication */
};
/** Supported Convolution layer methods */
enum class ConvolutionMethod
{
Default, /**< Default approach using internal heuristics */
GEMM, /**< GEMM based convolution */
Direct, /**< Deep direct convolution */
Winograd /**< Winograd based convolution */
};
/** Supported Depthwise Convolution layer methods */
enum class DepthwiseConvolutionMethod
{
Default, /**< Default approach using internal heuristics */
GEMV, /**< Generic GEMV based depthwise convolution */
Optimized3x3, /**< Optimized 3x3 direct depthwise convolution */
};
/** Enable or disable fast math for Convolution layer */
enum class FastMathHint
{
Enabled, /**< Fast math enabled for Convolution layer */
Disabled, /**< Fast math disabled for Convolution layer */
};
/** Supported nodes */
enum class NodeType
{
ActivationLayer,
BatchNormalizationLayer,
BoundingBoxTransformLayer,
ChannelShuffleLayer,
ConcatenateLayer,
ConvolutionLayer,
DeconvolutionLayer,
DepthwiseConvolutionLayer,
DetectionOutputLayer,
DetectionPostProcessLayer,
EltwiseLayer,
FlattenLayer,
FullyConnectedLayer,
FusedConvolutionBatchNormalizationLayer,
FusedDepthwiseConvolutionBatchNormalizationLayer,
GenerateProposalsLayer,
NormalizationLayer,
NormalizePlanarYUVLayer,
PadLayer,
PermuteLayer,
PoolingLayer,
PriorBoxLayer,
QuantizationLayer,
ReorgLayer,
ReshapeLayer,
ResizeLayer,
ROIAlignLayer,
SoftmaxLayer,
SliceLayer,
SplitLayer,
StackLayer,
UpsampleLayer,
YOLOLayer,
Input,
Output,
Const,
Dummy
};
/** Backend Memory Manager affinity **/
enum class MemoryManagerAffinity
{
Buffer, /**< Affinity at buffer level */
Offset /**< Affinity at offset level */
};
/** NodeID-index struct
*
* Used to describe connections
*/
struct NodeIdxPair
{
NodeID node_id; /**< Node ID */
size_t index; /**< Index */
};
/** Common node parameters */
struct NodeParams
{
std::string name; /**< Node name */
Target target; /**< Node target */
};
} // namespace graph
} // namespace arm_compute
#endif /* __ARM_COMPUTE_GRAPH_TYPES_H__ */
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