| /* |
| * 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_CLCONVOLUTIONLAYER_H__ |
| #define __ARM_COMPUTE_CLCONVOLUTIONLAYER_H__ |
| |
| #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" |
| #include "arm_compute/runtime/IFunction.h" |
| #include "arm_compute/runtime/IMemoryManager.h" |
| |
| #include <memory> |
| |
| namespace arm_compute |
| { |
| /** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions: |
| * |
| * -# @ref CLGEMMConvolutionLayer |
| * -# @ref CLWinogradConvolutionLayer |
| * -# @ref CLDirectConvolutionLayer |
| * -# @ref CLFFTConvolutionLayer |
| * |
| * The function selects one of the algorithms mentioned above based on: |
| * - The size of the kernel |
| * - Number of input/output feature maps |
| * - Amount of memory needed |
| * |
| * Generally GEMM-based convolution is executed when neither Winograd nor FFT nor Direct convolution can be performed. |
| * |
| * FP32 Algorithm| Filter Size | Input/Output feature maps | |
| * --------------|-------------------------------------------------------------|-------------------------------------------| |
| * Winograd | 3x3 1x3 3x1 5x1 1x5 5x5(fast maths) 7x1 1x7 | Input channels is greater than 3 | |
| * FFT | Squared kernels and greater than 9x9 | Input feature maps > Output feature maps | |
| * DirectConv | 9x9 | | |
| * GEMM | Any size | | |
| * |
| * Winograd 5x5 requires fast maths enabled. |
| * |
| * FP16 Algorithm| Filter Size | Input/Output feature maps | |
| * --------------|----------------------------|-------------------------------------------| |
| * Winograd | 3x3 1x3 3x1 5x1 1x5 5x5 | Input channels is greater than 3 | |
| * FFT | Not supported | | |
| * DirectConv | 9x9 | | |
| * GEMM | Any size | | |
| * |
| * Winograd FP16 requires fast maths enabled. |
| * |
| */ |
| class CLConvolutionLayer : public IFunction |
| { |
| public: |
| /** Default constructor */ |
| CLConvolutionLayer(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: QASYMM8/F16/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. |
| * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. |
| * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type. |
| * @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. |
| * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input. |
| * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). |
| * @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 |
| * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout |
| */ |
| void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), |
| const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1); |
| /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer |
| * |
| * @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: QASYMM8/F16/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. |
| * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input. |
| * @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. |
| * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. |
| * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). |
| * @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 |
| * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout |
| * |
| * @return a status |
| */ |
| static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, |
| unsigned int num_groups = 1); |
| /** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer |
| * |
| * @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: QASYMM8/F16/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. |
| * @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. |
| * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. |
| * @param[in] act_info (Optional) Activation layer information in case of a fused activation. |
| * @param[in] gpu_target Specifies the @p GPUTarget. |
| * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). |
| * @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 ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U), bool enable_fast_math = false); |
| // Inherited methods overridden: |
| void run() override; |
| void prepare() override; |
| |
| private: |
| std::shared_ptr<IMemoryManager> _memory_manager; |
| std::unique_ptr<IFunction> _function; |
| }; |
| } |
| #endif /* __ARM_COMPUTE_CLCONVOLUTIONLAYER_H__ */ |
