184 lines
14 KiB
C
184 lines
14 KiB
C
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/*
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* Copyright (c) 2017-2020 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_CLCONVOLUTIONLAYER_H
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#define ARM_COMPUTE_CLCONVOLUTIONLAYER_H
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#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
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#include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h"
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#include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h"
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#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/runtime/IMemoryManager.h"
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#include <memory>
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namespace arm_compute
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{
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/** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions:
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*
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* -# @ref CLGEMMConvolutionLayer
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* -# @ref CLWinogradConvolutionLayer
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* -# @ref CLDirectConvolutionLayer
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* -# @ref CLFFTConvolutionLayer
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*
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* The function selects one of the algorithms mentioned above based on:
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* - The size of the kernel
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* - Number of input/output feature maps
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* - Amount of memory needed
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*
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* Generally GEMM-based convolution is executed when neither Winograd nor FFT nor Direct convolution can be performed.
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*
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* FP32 Algorithm| Filter Size | Input/Output feature maps |
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* --------------|-------------------------------------------------------------|-------------------------------------------|
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* Winograd | 3x3 1x3 3x1 5x1 1x5 5x5(fast maths) 7x1 1x7 | Input channels is greater than 3 |
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* FFT | Squared kernels and greater than 9x9 | Input feature maps > Output feature maps |
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* DirectConv | 9x9 | |
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* GEMM | Any size | |
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*
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* Winograd 5x5 requires fast maths enabled.
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*
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* FP16 Algorithm| Filter Size | Input/Output feature maps |
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* --------------|----------------------------|-------------------------------------------|
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* Winograd | 3x3 1x3 3x1 5x1 1x5 5x5 | Input channels is greater than 3 |
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* FFT | Not supported | |
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* DirectConv | 9x9 | |
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* GEMM | Any size | |
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*
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* Winograd FP16 requires fast maths enabled.
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*
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*/
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class CLConvolutionLayer : public IFunction
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{
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public:
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/** Default constructor */
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CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
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/** Default Destructor */
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~CLConvolutionLayer();
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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CLConvolutionLayer(const CLConvolutionLayer &) = delete;
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/** Default move constructor */
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CLConvolutionLayer(CLConvolutionLayer &&) = default;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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CLConvolutionLayer &operator=(const CLConvolutionLayer &) = delete;
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/** Default move assignment operator */
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CLConvolutionLayer &operator=(CLConvolutionLayer &&) = default;
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/** Set the input and output tensors.
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*
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
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* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
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*/
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void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
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const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1);
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/** Set the input and output tensors.
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*
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* @param[in] compile_context The compile context to be used.
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
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* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
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*/
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void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
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const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
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unsigned int num_groups = 1);
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/** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer
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*
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
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* @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
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const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
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unsigned int num_groups = 1);
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/** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer
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*
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
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* @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] gpu_target Specifies the @p GPUTarget.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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*
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* @return a status
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*/
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static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
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const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U), bool enable_fast_math = false);
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// Inherited methods overridden:
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void run() override;
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void prepare() override;
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private:
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std::shared_ptr<IMemoryManager> _memory_manager;
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std::unique_ptr<IFunction> _function;
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};
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}
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#endif /* ARM_COMPUTE_CLCONVOLUTIONLAYER_H */
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