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/*
* Copyright (c) 2017-2018 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_NEWINOGRADCONVOLUTIONLAYER_H__
#define __ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H__
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CPP/functions/CPPPermute.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
class ITensor;
/** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
* -# @ref NEWinogradLayerTransformWeightsKernel (executed only once in the first call to the run() method )
* -# @ref NEWinogradLayerTransformInputKernel
* -# @ref NEWinogradLayerTransformOutputKernel
* -# @ref NEGEMMAssemblyDispatch
* -# @ref CPPPermute (three times: weights, input and output)
*
* @note Some Winograd configurations (i.e. F(2x2, 5x5), F(4x4, 5x5)) are supported only with enable_fast_math = true
*/
class NEWinogradConvolutionLayer : public IFunction
{
public:
/** Constructor */
NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Set the input and output tensors.
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F32.
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
* Currently only 3x3 and 5x5 kernels are supported.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false);
// Inherited methods overridden:
void run() override;
void prepare() override;
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F32.
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
* Currently only 3x3 and 5x5 kernels are supported.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
* @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradConvolutionLayer(const NEWinogradConvolutionLayer &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradConvolutionLayer &operator=(const NEWinogradConvolutionLayer &) = delete;
private:
MemoryGroup _memory_group;
NEGEMM _gemm_function;
std::unique_ptr<INEKernel> _transform_input_kernel;
std::unique_ptr<INEKernel> _transform_output_kernel;
std::unique_ptr<INEKernel> _transform_weights_kernel;
NEActivationLayer _activationlayer_function;
CPPPermute _permute_input;
CPPPermute _permute_weights;
CPPPermute _permute_output;
Tensor _input_workspace;
Tensor _output_workspace;
Tensor _kernel_storage;
Tensor _input_nhwc;
Tensor _output_nhwc;
Tensor _weights_hwio;
const ITensor *_input;
const ITensor *_weights;
ITensor *_output;
bool _is_prepared;
bool _is_activationlayer_enabled;
};
}
#endif /* __ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H__ */
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