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/*
* Copyright (c) 2017-2019 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_ICLDEPTHWISECONVOLUTIONKERNEL3x3_H__
#define __ARM_COMPUTE_ICLDEPTHWISECONVOLUTIONKERNEL3x3_H__
#include "arm_compute/core/CL/ICLKernel.h"
namespace arm_compute
{
class ICLTensor;
/** Interface for the kernel to run a 3x3 depthwise convolution on a tensor.
*/
class ICLDepthwiseConvolutionLayer3x3Kernel : public ICLKernel
{
public:
/** Default constructor */
ICLDepthwiseConvolutionLayer3x3Kernel()
: _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_y(1), _output_multipliers(), _output_shifts(), _is_quantized(false)
{
}
/** Prevent instances of this class from being copied (As this class contains pointers) */
ICLDepthwiseConvolutionLayer3x3Kernel(const ICLDepthwiseConvolutionLayer3x3Kernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
ICLDepthwiseConvolutionLayer3x3Kernel &operator=(const ICLDepthwiseConvolutionLayer3x3Kernel &) = delete;
/** Default Move Constructor. */
ICLDepthwiseConvolutionLayer3x3Kernel(ICLDepthwiseConvolutionLayer3x3Kernel &&) = default;
/** Default move assignment operator */
ICLDepthwiseConvolutionLayer3x3Kernel &operator=(ICLDepthwiseConvolutionLayer3x3Kernel &&) = default;
/** Initialize the function's source, destination, conv and border_size.
*
* @param[in] input Source tensor. DataType supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM].
* Data type supported: Same as @p input, QASYMM8/QSYMM8_PER_CHANNEL when input is QASYMM8.
* @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed.
* Data type supported: Same as @p input, S32 when input is QASYMM8.
* @param[out] output Destination tensor. Data type supported: Same as @p input.
* @param[in] conv_info Padding and stride information to use for the convolution.
* @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported for QASYMM8.
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization,
* the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32
* @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization,
* the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32
*/
virtual void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U),
const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr) = 0;
protected:
BorderSize _border_size;
const ICLTensor *_input;
ICLTensor *_output;
const ICLTensor *_weights;
const ICLTensor *_biases;
unsigned int _conv_stride_y;
const ICLTensor *_output_multipliers;
const ICLTensor *_output_shifts;
bool _is_quantized;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_ICLDEPTHWISECONVOLUTIONKERNEL3x3_H__ */
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