From 4a8ec803747780c97a444ca3df4bdeaa8c10190b Mon Sep 17 00:00:00 2001 From: giuros01 Date: Mon, 18 Mar 2019 13:25:05 +0000 Subject: Optimize CL DeconvolutionLayer-Part II: Add CLDirectDeconvolution function to be used by CLDeconvolution. This is only a code refactoring (no optimizations have been added) Change-Id: I78488f4aecfe1cce93c31dba31489dcee4c85c67 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/895 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Gian Marco Iodice --- arm_compute/runtime/CL/CLFunctions.h | 1 + .../runtime/CL/functions/CLDeconvolutionLayer.h | 78 ++------ .../CL/functions/CLDirectDeconvolutionLayer.h | 131 ++++++++++++++ src/runtime/CL/functions/CLDeconvolutionLayer.cpp | 172 +++--------------- .../CL/functions/CLDirectDeconvolutionLayer.cpp | 198 +++++++++++++++++++++ tests/validation/CL/DeconvolutionLayer.cpp | 2 +- 6 files changed, 370 insertions(+), 212 deletions(-) create mode 100644 arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h create mode 100644 src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h index 46e43dc0a9..f1021843a0 100644 --- a/arm_compute/runtime/CL/CLFunctions.h +++ b/arm_compute/runtime/CL/CLFunctions.h @@ -61,6 +61,7 @@ #include "arm_compute/runtime/CL/functions/CLDerivative.h" #include "arm_compute/runtime/CL/functions/CLDilate.h" #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h" #include "arm_compute/runtime/CL/functions/CLElementWiseUnaryLayer.h" #include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h" #include "arm_compute/runtime/CL/functions/CLEqualizeHistogram.h" diff --git a/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h index 9c115f8b3d..b613708c50 100644 --- a/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h @@ -24,13 +24,7 @@ #ifndef __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ #define __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ -#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" -#include "arm_compute/runtime/CL/functions/CLDeconvolutionLayerUpsample.h" - -#include "arm_compute/core/CPP/kernels/CPPFlipWeightsKernel.h" - -#include "arm_compute/runtime/CL/CLMemoryGroup.h" -#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" @@ -38,51 +32,16 @@ namespace arm_compute { -class ICLTensor; -/** Function to run the deconvolution layer. - * - * Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input depending on the stride and pad info and then perform a 1x1 - * convolution pass. Input stride defines how many zeroes we should put between each element of the input, pad is the amount of padding and finally a is a user - * specified value where a < stride - 1, that increases the padding top and right of the input image. - * - * The relation between input to output is as follows: - * \f[ - * width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x - * \f] - * \f[ - * height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y - * \f] - * - * where: - * width_input is the size of the first input dimension. - * height_input is the size of the second input dimension. - * width_output is the size of the first output dimension. - * height_output is the size of the second output dimension. - * kernel_x and kernel_y are the convolution sizes in x and y. - * stride_x and stride_y is the input stride of the first and second dimension. - * - * The weights used by Deconvolution are supposed to be the same as the ones used for Convolution. Therefore, it will be necessary to use the weights in the - * reverse order to perform an actual convolution. This is achieved by using the @ref CPPFlipWeightsKernel. - * - * This function calls the following OpenCL kernels/functions: - * - * -# @ref CLDeconvolutionLayerUpsample - * -# @ref CLConvolutionLayer +/** Basic function to compute the deconvolution layer. This function calls the following OpenCL kernels/functions: * + * -# @ref CLDirectDeconvolutionLayer */ class CLDeconvolutionLayer : public IFunction { public: - /** Constructor */ + /** Default constructor */ CLDeconvolutionLayer(std::shared_ptr memory_manager = nullptr); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDeconvolutionLayer(const CLDeconvolutionLayer &) = delete; - /** Default move constructor */ - CLDeconvolutionLayer(CLDeconvolutionLayer &&) = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDeconvolutionLayer &operator=(const CLDeconvolutionLayer &) = delete; - /** Default move assignment operator */ - CLDeconvolutionLayer &operator=(CLDeconvolutionLayer &&) = default; + /** Set the input, weights, biases and output tensors. * * @deprecated This method is deprecated and will be removed in release 19.05 @@ -91,13 +50,13 @@ public: * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. * @param[out] output Output tensor. The output has the same number of dimensions as the @p input. - * @param[in] info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. + * @param[in] deconv_info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. * @param[in] inner_border_right The number of zeros added to right edge of the input. * @param[in] inner_border_top The number of zeros added to top edge of the input. * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. * */ - void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, + void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo()); /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer * @@ -107,14 +66,14 @@ public: * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. * @param[in] output Output tensor info. The output has the same number of dimensions as the @p input. - * @param[in] info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. + * @param[in] deconv_info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. * @param[in] inner_border_right The number of zeros added to right edge of the input. * @param[in] inner_border_top The number of zeros added to top edge of the input. * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info = WeightsInfo()); /** Set the input, weights, biases and output tensors. @@ -123,37 +82,32 @@ public: * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. * @param[out] output Output tensor. The output has the same number of dimensions as the @p input. - * @param[in] info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. + * @param[in] deconv_info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. * */ - void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo()); + void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, const WeightsInfo &weights_info = WeightsInfo()); /** Static function to check if given info will lead to a valid configuration of @ref CLDeconvolutionLayer * * @param[in] input Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32. * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. * @param[in] output Output tensor info. The output has the same number of dimensions as the @p input. - * @param[in] info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. + * @param[in] deconv_info Contains padding and policies to be used in the deconvolution, this is described in @ref PadStrideInfo. * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo()); + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, + const WeightsInfo &weights_info = WeightsInfo()); // Inherited methods overridden: void run() override; void prepare() override; private: - CLMemoryGroup _memory_group; - CLDeconvolutionLayerUpsample _scale_f; - CLConvolutionLayer _conv_f; - CPPFlipWeightsKernel _flip_weights; - CLTensor _scaled_output; - ICLTensor *_original_weights; - CLTensor _weights_flipped; - bool _is_prepared; + std::shared_ptr _memory_manager; + std::unique_ptr _function; }; } #endif /* __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h new file mode 100644 index 0000000000..936263d635 --- /dev/null +++ b/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h @@ -0,0 +1,131 @@ +/* + * Copyright (c) 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_CLDIRECTDECONVOLUTIONLAYER_H__ +#define __ARM_COMPUTE_CLDIRECTDECONVOLUTIONLAYER_H__ + +#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLDeconvolutionLayerUpsample.h" +#include "arm_compute/runtime/CL/functions/CLTranspose.h" + +#include "arm_compute/core/CPP/kernels/CPPFlipWeightsKernel.h" + +#include "arm_compute/runtime/CL/CLMemoryGroup.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/IFunction.h" +#include "arm_compute/runtime/IMemoryManager.h" + +#include + +namespace arm_compute +{ +class ICLTensor; +/** Function to run the deconvolution layer. + * + * Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input depending on the stride and pad info and then perform a 1x1 + * convolution pass. Input stride defines how many zeroes we should put between each element of the input and pad is the amount of padding. + * + * The relation between input to output is as follows: + * \f[ + * width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x + * \f] + * \f[ + * height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y + * \f] + * + * where: + * width_input is the size of the first input dimension. + * height_input is the size of the second input dimension. + * width_output is the size of the first output dimension. + * height_output is the size of the second output dimension. + * kernel_x and kernel_y are the convolution sizes in x and y. + * stride_x and stride_y is the input stride of the first and second dimension. + * + * The weights used by Deconvolution are supposed to be the same as the ones used for Convolution. Therefore, it will be necessary to use the weights in the + * reverse order to perform an actual convolution. This is achieved by using the @ref CPPFlipWeightsKernel. + * + * This function calls the following OpenCL kernels/functions: + * + * -# @ref CLDeconvolutionLayerUpsample + * -# @ref CLConvolutionLayer + * + * And the following CPP kernels: + * -# @ref CPPFlipWeightsKernel + * + */ +class CLDirectDeconvolutionLayer : public IFunction +{ +public: + /** Constructor */ + CLDirectDeconvolutionLayer(std::shared_ptr memory_manager = nullptr); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLDirectDeconvolutionLayer(const CLDirectDeconvolutionLayer &) = delete; + /** Default move constructor */ + CLDirectDeconvolutionLayer(CLDirectDeconvolutionLayer &&) = default; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLDirectDeconvolutionLayer &operator=(const CLDirectDeconvolutionLayer &) = delete; + /** Default move assignment operator */ + CLDirectDeconvolutionLayer &operator=(CLDirectDeconvolutionLayer &&) = default; + /** Set the input, weights, biases and output tensors. + * + * @param[in,out] input Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32. + * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. + * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. + * @param[out] output Output tensor. The output has the same number of dimensions as the @p input. + * @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo. + * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. + * + */ + void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo()); + /** Static function to check if given info will lead to a valid configuration of @ref CLDirectDeconvolutionLayer + * + * @param[in] input Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32. + * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input. + * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input. + * @param[in] output Output tensor info. The output has the same number of dimensions as the @p input. + * @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo. + * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, + const WeightsInfo &weights_info = WeightsInfo()); + + // Inherited methods overridden: + void run() override; + void prepare() override; + +private: + CLMemoryGroup _memory_group; + CLDeconvolutionLayerUpsample _scale_f; + CLConvolutionLayer _conv_f; + CPPFlipWeightsKernel _flip_weights; + + CLTensor _scaled_output; + ICLTensor *_original_weights; + CLTensor _weights_flipped; + + bool _is_prepared; +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ */ diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp index 9da02c10ad..2c17473fc7 100644 --- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp @@ -23,188 +23,62 @@ */ #include "arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h" -#include "arm_compute/core/Helpers.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include #include #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; -CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr memory_manager) // NOLINT - : _memory_group(std::move(memory_manager)), - _scale_f(), - _conv_f(), - _flip_weights(), - _scaled_output(), - _original_weights(nullptr), - _weights_flipped(), - _is_prepared(false) +CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr memory_manager) + : _memory_manager(std::move(memory_manager)), _function() { } -Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, - unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); - - const DataLayout data_layout = input->data_layout(); - - const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1); - ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric()); - - const unsigned int stride_x = info.stride().first; - const unsigned int stride_y = info.stride().second; - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y"); - - auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), - info.pad().first, info.pad().second, stride_x, stride_y); - - const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); - - if(bias != nullptr) - { - if(is_data_type_quantized_asymmetric(input->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); - } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias); - } - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid."); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid."); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "Output's depth is invalid."); - - unsigned int padx = 0; - unsigned int pady = 0; - const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady); - TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout)); - const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); - - ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); - - return Status{}; -} - -void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, +void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info) { + ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top); ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + auto f = arm_compute::support::cpp14::make_unique(); + f->configure(input, weights, bias, output, deconv_info, weights_info); + _function = std::move(f); +} - const unsigned int stride_x = info.stride().first; - const unsigned int stride_y = info.stride().second; - - const DataLayout data_layout = input->info()->data_layout(); - - const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - - _original_weights = weights; - _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout)); - _flip_weights.configure(weights, &_weights_flipped); - - auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), - info.pad().first, info.pad().second, stride_x, stride_y); - - const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info()); - - // Output auto initialization if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout)); - - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top)); - - _is_prepared = weights_info.retain_internal_weights(); - - _memory_group.manage(&_scaled_output); - - // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape - unsigned int padx = 0; - unsigned int pady = 0; - const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady); - - TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info()); - scale_out_info.set_data_layout(data_layout); - _scaled_output.allocator()->init(scale_out_info); - - // configure scale function - const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2); - _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), upsample_info); - - // setup the function to convolve the upscaled output - const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); - _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info, weights_info); - _scaled_output.allocator()->allocate(); +Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, + unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top); + ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info)); + return Status{}; } -void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, +void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info, const WeightsInfo &weights_info) { - configure(input, weights, bias, output, info, 0, 0, weights_info); + configure(input, weights, bias, output, deconv_info, 0, 0, weights_info); } -Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, +Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info, const WeightsInfo &weights_info) { - return CLDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0, weights_info); + return CLDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, 0, 0, weights_info); } void CLDeconvolutionLayer::run() { prepare(); - - _memory_group.acquire(); - - _scale_f.run(); - _conv_f.run(); - - _memory_group.release(); + _function->run(); } void CLDeconvolutionLayer::prepare() { - if(!_is_prepared) - { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - - // Run weights flipping and mark original weights tensor as unused - _weights_flipped.allocator()->allocate(); - _weights_flipped.map(true); - _original_weights->map(CLScheduler::get().queue(), true); - CPPScheduler::get().schedule(&_flip_weights, Window::DimZ); - _weights_flipped.unmap(); - _original_weights->unmap(CLScheduler::get().queue()); - _original_weights->mark_as_unused(); - - // Prepare convolution - _conv_f.prepare(); - - if(!_weights_flipped.is_used()) - { - _weights_flipped.allocator()->free(); - } - - _is_prepared = true; - } + _function->prepare(); } diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp new file mode 100644 index 0000000000..c01588a164 --- /dev/null +++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp @@ -0,0 +1,198 @@ +/* + * Copyright (c) 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. + */ +#include "arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include "utils/TypePrinter.h" + +#include +#include + +namespace arm_compute +{ +using namespace arm_compute::misc::shape_calculator; + +CLDirectDeconvolutionLayer::CLDirectDeconvolutionLayer(std::shared_ptr memory_manager) // NOLINT + : _memory_group(std::move(memory_manager)), + _scale_f(), + _conv_f(), + _flip_weights(), + _scaled_output(), + _original_weights(nullptr), + _weights_flipped(), + _is_prepared(false) +{ +} + +Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, + const WeightsInfo &weights_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); + + const DataLayout data_layout = input->data_layout(); + + const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1); + ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric()); + + const unsigned int stride_x = info.stride().first; + const unsigned int stride_y = info.stride().second; + + auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), + info.pad().first, info.pad().second, stride_x, stride_y); + + const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); + + if(bias != nullptr) + { + if(is_data_type_quantized_asymmetric(input->data_type())) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + } + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid."); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "Output's depth is invalid."); + + unsigned int padx = 0; + unsigned int pady = 0; + const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, 0, 0, out_dims, padx, pady); + TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout)); + const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); + + ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(), info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); + + return Status{}; +} + +void CLDirectDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, + const WeightsInfo &weights_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + + const unsigned int stride_x = info.stride().first; + const unsigned int stride_y = info.stride().second; + + const DataLayout data_layout = input->info()->data_layout(); + + const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + _original_weights = weights; + _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout)); + _flip_weights.configure(weights, &_weights_flipped); + + auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), + info.pad().first, info.pad().second, stride_x, stride_y); + + const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info()); + + // Output auto initialization if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout)); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(CLDirectDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info)); + + _is_prepared = weights_info.retain_internal_weights(); + + _memory_group.manage(&_scaled_output); + + // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape + unsigned int padx = 0; + unsigned int pady = 0; + const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, 0, 0, out_dims, padx, pady); + + TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info()); + scale_out_info.set_data_layout(data_layout); + _scaled_output.allocator()->init(scale_out_info); + + // configure scale function + const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2); + _scale_f.configure(input, &_scaled_output, BorderSize(), upsample_info); + + // setup the function to convolve the upscaled output + const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); + _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info, weights_info); + _scaled_output.allocator()->allocate(); +} + +void CLDirectDeconvolutionLayer::run() +{ + prepare(); + + _memory_group.acquire(); + + _scale_f.run(); + _conv_f.run(); + + _memory_group.release(); +} + +void CLDirectDeconvolutionLayer::prepare() +{ + if(!_is_prepared) + { + ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); + + // Run weights flipping and mark original weights tensor as unused + _weights_flipped.allocator()->allocate(); + _weights_flipped.map(true); + _original_weights->map(CLScheduler::get().queue(), true); + CPPScheduler::get().schedule(&_flip_weights, Window::DimZ); + _weights_flipped.unmap(); + _original_weights->unmap(CLScheduler::get().queue()); + _original_weights->mark_as_unused(); + + // Prepare convolution + _conv_f.prepare(); + + if(!_weights_flipped.is_used()) + { + _weights_flipped.allocator()->free(); + } + + _is_prepared = true; + } +} +} // namespace arm_compute diff --git a/tests/validation/CL/DeconvolutionLayer.cpp b/tests/validation/CL/DeconvolutionLayer.cpp index 31852c8eb6..958a0e438a 100644 --- a/tests/validation/CL/DeconvolutionLayer.cpp +++ b/tests/validation/CL/DeconvolutionLayer.cpp @@ -119,7 +119,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi 1U, 0U, })), - framework::dataset::make("Expected", { false, false, false, false, false, true })), + framework::dataset::make("Expected", { false, false, false, false, true, true })), input_info, weights_info, bias_info, output_info, pad_info, ax, ay, expected) { bool is_valid = bool(CLDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pad_info, ax, ay)); 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