diff options
author | Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-27 17:46:17 +0100 |
---|---|---|
committer | felixjohnny.thomasmathibalan <felixjohnny.thomasmathibalan@arm.com> | 2023-09-28 12:08:05 +0000 |
commit | afd38f0c617d6f89b2b4532c6c44f116617e2b6f (patch) | |
tree | 03bc7d5a762099989b16a656fa8d397b490ed70e /arm_compute/core/Types.h | |
parent | bdcb4c148ee2fdeaaddf4cf1e57bbb0de02bb894 (diff) | |
download | ComputeLibrary-afd38f0c617d6f89b2b4532c6c44f116617e2b6f.tar.gz |
Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Diffstat (limited to 'arm_compute/core/Types.h')
-rw-r--r-- | arm_compute/core/Types.h | 294 |
1 files changed, 198 insertions, 96 deletions
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index 9264cefe3e..6b51af17d4 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -59,13 +59,13 @@ /** The following symbols have been moved to: * MatMulInfo */ -#include "arm_compute/function_info/MatMulInfo.h" - #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Size3D.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/utils/misc/Macros.h" +#include "arm_compute/function_info/MatMulInfo.h" + #include "support/Bfloat16.h" #include <cmath> @@ -143,8 +143,7 @@ enum class ComparisonOperation struct ValidRegion { /** Default constructor */ - ValidRegion() - : anchor{}, shape{} + ValidRegion() : anchor{}, shape{} { } @@ -165,8 +164,7 @@ struct ValidRegion * @param[in] a_shape Shape of the valid region. * */ - ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape) - : anchor{ an_anchor }, shape{ a_shape } + ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape) : anchor{an_anchor}, shape{a_shape} { anchor.set_num_dimensions(std::max(anchor.num_dimensions(), shape.num_dimensions())); } @@ -179,7 +177,7 @@ struct ValidRegion * */ ValidRegion(const Coordinates &an_anchor, const TensorShape &a_shape, size_t num_dimensions) - : anchor{ an_anchor }, shape{ a_shape } + : anchor{an_anchor}, shape{a_shape} { ARM_COMPUTE_ERROR_ON(num_dimensions < std::max(anchor.num_dimensions(), shape.num_dimensions())); anchor.set_num_dimensions(num_dimensions); @@ -241,32 +239,24 @@ enum class BorderMode struct BorderSize { /** Empty border, i.e. no border */ - constexpr BorderSize() noexcept - : top{ 0 }, - right{ 0 }, - bottom{ 0 }, - left{ 0 } + constexpr BorderSize() noexcept : top{0}, right{0}, bottom{0}, left{0} { } /** Border with equal size around the 2D plane */ - explicit constexpr BorderSize(unsigned int size) noexcept - : top{ size }, - right{ size }, - bottom{ size }, - left{ size } + explicit constexpr BorderSize(unsigned int size) noexcept : top{size}, right{size}, bottom{size}, left{size} { } /** Border with same size for top/bottom and left/right */ constexpr BorderSize(unsigned int top_bottom, unsigned int left_right) - : top{ top_bottom }, right{ left_right }, bottom{ top_bottom }, left{ left_right } + : top{top_bottom}, right{left_right}, bottom{top_bottom}, left{left_right} { } /** Border with different sizes */ constexpr BorderSize(unsigned int top, unsigned int right, unsigned int bottom, unsigned int left) - : top{ top }, right{ right }, bottom{ bottom }, left{ left } + : top{top}, right{right}, bottom{bottom}, left{left} { } @@ -371,7 +361,7 @@ enum class InterpolationPolicy { NEAREST_NEIGHBOR, /**< Output values are defined to match the source pixel whose center is nearest to the sample position */ BILINEAR, /**< Output values are defined by bilinear interpolation between the pixels */ - AREA, /**< Output values are determined by averaging the source pixels whose areas fall under the area of the destination pixel, projected onto the source image */ + AREA, /**< Output values are determined by averaging the source pixels whose areas fall under the area of the destination pixel, projected onto the source image */ }; /** Bilinear Interpolation method used by LKTracker */ @@ -478,12 +468,12 @@ enum class NormType */ struct DetectionWindow { - uint16_t x{ 0 }; /**< Top-left x coordinate */ - uint16_t y{ 0 }; /**< Top-left y coordinate */ - uint16_t width{ 0 }; /**< Width of the detection window */ - uint16_t height{ 0 }; /**< Height of the detection window */ - uint16_t idx_class{ 0 }; /**< Index of the class */ - float score{ 0.f }; /**< Confidence value for the detection window */ + uint16_t x{0}; /**< Top-left x coordinate */ + uint16_t y{0}; /**< Top-left y coordinate */ + uint16_t width{0}; /**< Width of the detection window */ + uint16_t height{0}; /**< Height of the detection window */ + uint16_t idx_class{0}; /**< Index of the class */ + float score{0.f}; /**< Confidence value for the detection window */ }; /** Available pooling types */ @@ -520,12 +510,28 @@ public: * @param[in] im_width (Optional) Boxes whose centers (on the x axis) is beyond im_width will be filtered. Defaults to 1 * @param[in] im_height (Optional) Boxes whose centers (on the y axis) is beyond im_height will be filtered. Defaults to 1 */ - BoxNMSLimitInfo(float score_thresh = 0.05f, float nms = 0.3f, - int detections = 100, bool soft_nms_enabled = false, - NMSType soft_nms_method = NMSType::LINEAR, - float soft_nms_sigma = 0.5f, float soft_nms_min_score_thres = 0.001f, bool suppress_size = false, float min_size = 1.0f, float im_width = 1.0f, float im_height = 1.0f) - : _score_thresh(score_thresh), _nms(nms), _detections_per_im(detections), _soft_nms_enabled(soft_nms_enabled), _soft_nms_method(soft_nms_method), _soft_nms_sigma(soft_nms_sigma), - _soft_nms_min_score_thres(soft_nms_min_score_thres), _suppress_size(suppress_size), _min_size(min_size), _im_width(im_width), _im_height(im_height) + BoxNMSLimitInfo(float score_thresh = 0.05f, + float nms = 0.3f, + int detections = 100, + bool soft_nms_enabled = false, + NMSType soft_nms_method = NMSType::LINEAR, + float soft_nms_sigma = 0.5f, + float soft_nms_min_score_thres = 0.001f, + bool suppress_size = false, + float min_size = 1.0f, + float im_width = 1.0f, + float im_height = 1.0f) + : _score_thresh(score_thresh), + _nms(nms), + _detections_per_im(detections), + _soft_nms_enabled(soft_nms_enabled), + _soft_nms_method(soft_nms_method), + _soft_nms_sigma(soft_nms_sigma), + _soft_nms_min_score_thres(soft_nms_min_score_thres), + _suppress_size(suppress_size), + _min_size(min_size), + _im_width(im_width), + _im_height(im_height) { } /** Get the score threshold */ @@ -603,14 +609,13 @@ private: struct Padding2D { Padding2D() = default; - Padding2D(size_t left, size_t right, size_t top, size_t bottom) - : left(left), right(right), top(top), bottom(bottom) + Padding2D(size_t left, size_t right, size_t top, size_t bottom) : left(left), right(right), top(top), bottom(bottom) { } - size_t left = { 0 }; /**< Padding across the width dimension on the left, in elements. */ - size_t right = { 0 }; /**< Padding across the width dimension on the right, in elements. */ - size_t top = { 0 }; /**< Padding across the height dimension on the top, in elements. */ - size_t bottom = { 0 }; /**< Padding across the height dimension on the bottom, in elements. */ + size_t left = {0}; /**< Padding across the width dimension on the left, in elements. */ + size_t right = {0}; /**< Padding across the width dimension on the right, in elements. */ + size_t top = {0}; /**< Padding across the height dimension on the top, in elements. */ + size_t bottom = {0}; /**< Padding across the height dimension on the bottom, in elements. */ }; /** Padding information for 3D operations like Conv3d */ @@ -630,12 +635,12 @@ struct Padding3D { } - size_t left = { 0 }; /**< Padding across the width dimenstion on the left, in elements. */ - size_t right = { 0 }; /**< Padding across the width dimenstion on the right, in elements. */ - size_t top = { 0 }; /**< Padding across the height dimenstion on the top, in elements. */ - size_t bottom = { 0 }; /**< Padding across the height dimenstion on the bottom, in elements. */ - size_t front = { 0 }; /**< Padding across the depth dimenstion on the front, in elements. */ - size_t back = { 0 }; /**< Padding across the depth dimenstion on the back, in elements. */ + size_t left = {0}; /**< Padding across the width dimenstion on the left, in elements. */ + size_t right = {0}; /**< Padding across the width dimenstion on the right, in elements. */ + size_t top = {0}; /**< Padding across the height dimenstion on the top, in elements. */ + size_t bottom = {0}; /**< Padding across the height dimenstion on the bottom, in elements. */ + size_t front = {0}; /**< Padding across the depth dimenstion on the front, in elements. */ + size_t back = {0}; /**< Padding across the depth dimenstion on the back, in elements. */ }; /** PriorBox layer info */ @@ -667,9 +672,15 @@ public: * @param[in] img_size (Optional) Image size. * @param[in] steps (Optional) Step values. */ - PriorBoxLayerInfo(const std::vector<float> &min_sizes, const std::vector<float> &variances, float offset, bool flip = true, bool clip = false, - const std::vector<float> &max_sizes = {}, const std::vector<float> &aspect_ratios = {}, - const Coordinates2D &img_size = Coordinates2D{ 0, 0 }, const std::array<float, 2> &steps = { { 0.f, 0.f } }) + PriorBoxLayerInfo(const std::vector<float> &min_sizes, + const std::vector<float> &variances, + float offset, + bool flip = true, + bool clip = false, + const std::vector<float> &max_sizes = {}, + const std::vector<float> &aspect_ratios = {}, + const Coordinates2D &img_size = Coordinates2D{0, 0}, + const std::array<float, 2> &steps = {{0.f, 0.f}}) : _min_sizes(min_sizes), _variances(variances), _offset(offset), @@ -681,22 +692,22 @@ public: _steps(steps) { _aspect_ratios.push_back(1.); - for(unsigned int i = 0; i < aspect_ratios.size(); ++i) + for (unsigned int i = 0; i < aspect_ratios.size(); ++i) { float ar = aspect_ratios[i]; bool already_exist = false; - for(auto ar_new : _aspect_ratios) + for (auto ar_new : _aspect_ratios) { - if(fabs(ar - ar_new) < 1e-6) + if (fabs(ar - ar_new) < 1e-6) { already_exist = true; break; } } - if(!already_exist) + if (!already_exist) { _aspect_ratios.push_back(ar); - if(flip) + if (flip) { _aspect_ratios.push_back(1.f / ar); } @@ -808,8 +819,16 @@ public: * @param[in] variance_encoded_in_target (Optional) If true, variance is encoded in target. Otherwise we need to adjust the predicted offset accordingly.Default set to false. * @param[in] eta (Optional) Eta. */ - DetectionOutputLayerInfo(int num_classes, bool share_location, DetectionOutputLayerCodeType code_type, int keep_top_k, float nms_threshold, int top_k = -1, int background_label_id = -1, - float confidence_threshold = std::numeric_limits<float>::lowest(), bool variance_encoded_in_target = false, float eta = 1) + DetectionOutputLayerInfo(int num_classes, + bool share_location, + DetectionOutputLayerCodeType code_type, + int keep_top_k, + float nms_threshold, + int top_k = -1, + int background_label_id = -1, + float confidence_threshold = std::numeric_limits<float>::lowest(), + bool variance_encoded_in_target = false, + float eta = 1) : _num_classes(num_classes), _share_location(share_location), _code_type(code_type), @@ -923,8 +942,15 @@ public: * @param[in] detection_per_class (Optional) Number of detection per class. Used in the Regular Non-Max-Suppression. Defaults to 100. * @param[in] dequantize_scores (Optional) If the scores need to be dequantized. Defaults to true. */ - DetectionPostProcessLayerInfo(unsigned int max_detections, unsigned int max_classes_per_detection, float nms_score_threshold, float iou_threshold, unsigned int num_classes, - std::array<float, 4> scales_values, bool use_regular_nms = false, unsigned int detection_per_class = 100, bool dequantize_scores = true) + DetectionPostProcessLayerInfo(unsigned int max_detections, + unsigned int max_classes_per_detection, + float nms_score_threshold, + float iou_threshold, + unsigned int num_classes, + std::array<float, 4> scales_values, + bool use_regular_nms = false, + unsigned int detection_per_class = 100, + bool dequantize_scores = true) : _max_detections(max_detections), _max_classes_per_detection(max_classes_per_detection), _nms_score_threshold(nms_score_threshold), @@ -1240,8 +1266,14 @@ public: * @param[in] spatial_scale Spatial scale to be applied to the ROI coordinates and dimensions. * @param[in] sampling_ratio Number of samples to include in each pooling region (if set to zero, a ceil(roi_dims/pooling_dims)) */ - ROIPoolingLayerInfo(unsigned int pooled_width, unsigned int pooled_height, float spatial_scale, unsigned int sampling_ratio = 0) - : _pooled_width(pooled_width), _pooled_height(pooled_height), _spatial_scale(spatial_scale), _sampling_ratio(sampling_ratio) + ROIPoolingLayerInfo(unsigned int pooled_width, + unsigned int pooled_height, + float spatial_scale, + unsigned int sampling_ratio = 0) + : _pooled_width(pooled_width), + _pooled_height(pooled_height), + _spatial_scale(spatial_scale), + _sampling_ratio(sampling_ratio) { } /** Get the pooled width of the layer */ @@ -1288,10 +1320,24 @@ public: * @param[in] min_size (Optional)Size used to validate the anchors produced. Defaults to 16. * @param[in] values_per_roi (Optional)Values used to represent a ROI(Region of interest). Defaults to 4. */ - GenerateProposalsInfo(float im_width, float im_height, float im_scale, float spatial_scale = 1.0, int pre_nms_topN = 6000, int post_nms_topN = 300, float nms_thres = 0.7, float min_size = 16.0, + GenerateProposalsInfo(float im_width, + float im_height, + float im_scale, + float spatial_scale = 1.0, + int pre_nms_topN = 6000, + int post_nms_topN = 300, + float nms_thres = 0.7, + float min_size = 16.0, size_t values_per_roi = 4) - : _im_height(im_height), _im_width(im_width), _im_scale(im_scale), _spatial_scale(spatial_scale), _pre_nms_topN(pre_nms_topN), _post_nms_topN(post_nms_topN), _nms_thres(nms_thres), - _min_size(min_size), _values_per_roi(values_per_roi) + : _im_height(im_height), + _im_width(im_width), + _im_scale(im_scale), + _spatial_scale(spatial_scale), + _pre_nms_topN(pre_nms_topN), + _post_nms_topN(post_nms_topN), + _nms_thres(nms_thres), + _min_size(min_size), + _values_per_roi(values_per_roi) { } @@ -1417,11 +1463,20 @@ public: * @param[in] correct_transform_coords (Optional)Correct bounding box transform coordinates. Defaults to false * @param[in] bbox_xform_clip (Optional)Minimum bounding box width and height after bounding box transformation in log-space. Defaults to log(1000/16) */ - BoundingBoxTransformInfo(float img_width, float img_height, float scale, bool apply_scale = false, const std::array<float, 4> weights = { { 1.f, 1.f, 1.f, 1.f } }, bool correct_transform_coords = - false, - float bbox_xform_clip = - 4.135166556742356f) - : _img_width(img_width), _img_height(img_height), _scale(scale), _apply_scale(apply_scale), _correct_transform_coords(correct_transform_coords), _weights(weights), _bbox_xform_clip(bbox_xform_clip) + BoundingBoxTransformInfo(float img_width, + float img_height, + float scale, + bool apply_scale = false, + const std::array<float, 4> weights = {{1.f, 1.f, 1.f, 1.f}}, + bool correct_transform_coords = false, + float bbox_xform_clip = 4.135166556742356f) + : _img_width(img_width), + _img_height(img_height), + _scale(scale), + _apply_scale(apply_scale), + _correct_transform_coords(correct_transform_coords), + _weights(weights), + _bbox_xform_clip(bbox_xform_clip) { } @@ -1484,7 +1539,12 @@ public: * @param[in] is_scaled (Optional) Boolean that specifies if alpha will be scaled by the normalization size or not. * Should be false to follow [Krichevksy 2012]. */ - NormalizationLayerInfo(NormType type, uint32_t norm_size = 5, float alpha = 0.0001f, float beta = 0.5f, float kappa = 1.f, bool is_scaled = true) + NormalizationLayerInfo(NormType type, + uint32_t norm_size = 5, + float alpha = 0.0001f, + float beta = 0.5f, + float kappa = 1.f, + bool is_scaled = true) : _type(type), _norm_size(norm_size), _alpha(alpha), _beta(beta), _kappa(kappa), _is_scaled(is_scaled) { } @@ -1612,7 +1672,12 @@ class WeightsInfo public: /** Default constructor */ WeightsInfo() - : _are_reshaped(false), _kernel_width(0), _kernel_height(0), _num_kernels(0), _retain_internal_weights(false), _weight_format(arm_compute::WeightFormat::UNSPECIFIED) + : _are_reshaped(false), + _kernel_width(0), + _kernel_height(0), + _num_kernels(0), + _retain_internal_weights(false), + _weight_format(arm_compute::WeightFormat::UNSPECIFIED) { } /** Constructor @@ -1624,9 +1689,18 @@ public: * @param[in] retain_internal_weights (Optional) True if internal reshaped weights must be retained. Used for reconfiguration purposes. Default is false. * @param[in] weight_format (Optional) arm_gemm:WeightFormat enumeration requested by the user. Default is arm_compute::WeightFormat::UNSPECIFIED. */ - WeightsInfo(bool are_reshaped, unsigned int kernel_width, unsigned int kernel_height, unsigned int num_kernels, bool retain_internal_weights = false, - arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED) - : _are_reshaped(are_reshaped), _kernel_width(kernel_width), _kernel_height(kernel_height), _num_kernels(num_kernels), _retain_internal_weights(retain_internal_weights), _weight_format(weight_format) + WeightsInfo(bool are_reshaped, + unsigned int kernel_width, + unsigned int kernel_height, + unsigned int num_kernels, + bool retain_internal_weights = false, + arm_compute::WeightFormat weight_format = arm_compute::WeightFormat::UNSPECIFIED) + : _are_reshaped(are_reshaped), + _kernel_width(kernel_width), + _kernel_height(kernel_height), + _num_kernels(num_kernels), + _retain_internal_weights(retain_internal_weights), + _weight_format(weight_format) { } /** Flag which specifies if the weights tensor has been reshaped. @@ -1698,7 +1772,14 @@ class GEMMReshapeInfo final public: /** Default constructor */ GEMMReshapeInfo() - : _m(1), _n(1), _k(1), _mult_transpose1xW_width(1), _mult_interleave4x4_height(1), _depth_output_gemm3d(0), _reinterpret_input_as_3d(false), _broadcast_bias(false) + : _m(1), + _n(1), + _k(1), + _mult_transpose1xW_width(1), + _mult_interleave4x4_height(1), + _depth_output_gemm3d(0), + _reinterpret_input_as_3d(false), + _broadcast_bias(false) { } /** Constructor @@ -1714,9 +1795,22 @@ public: * to perform 1x1 convolutions with the NHWC data layout) * @param[in] broadcast_bias (Optional) Broadcast the shape of the bias tensor from a vector to a matrix. */ - GEMMReshapeInfo(int m, int n, int k, int mult_transpose1xW_width = 1, int mult_interleave4x4_height = 1, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool broadcast_bias = false) - : _m(m), _n(n), _k(k), _mult_transpose1xW_width(mult_transpose1xW_width), _mult_interleave4x4_height(mult_interleave4x4_height), _depth_output_gemm3d(depth_output_gemm3d), - _reinterpret_input_as_3d(reinterpret_input_as_3d), _broadcast_bias(broadcast_bias) + GEMMReshapeInfo(int m, + int n, + int k, + int mult_transpose1xW_width = 1, + int mult_interleave4x4_height = 1, + int depth_output_gemm3d = 0, + bool reinterpret_input_as_3d = false, + bool broadcast_bias = false) + : _m(m), + _n(n), + _k(k), + _mult_transpose1xW_width(mult_transpose1xW_width), + _mult_interleave4x4_height(mult_interleave4x4_height), + _depth_output_gemm3d(depth_output_gemm3d), + _reinterpret_input_as_3d(reinterpret_input_as_3d), + _broadcast_bias(broadcast_bias) { } /** Number of matrix A rows @@ -1806,11 +1900,11 @@ struct GEMMLHSMatrixInfo : m0(m), k0(k), v0(v), transpose(trans), interleave(inter) { } - unsigned int m0{ 1 }; /**< Number of rows processed by the matrix multiplication */ - unsigned int k0{ 1 }; /**< Number of partial accumulations performed by the matrix multiplication */ - unsigned int v0{ 1 }; /**< Number of vertical blocks of size (m0xk0) stored on the same output row */ - bool transpose{ true }; /**< True if the (m0xk0) block has to be transposed before been stored */ - bool interleave{ true }; /**< True if the v0 (m0xk0) blocks have to be interleaved in the output row */ + unsigned int m0{1}; /**< Number of rows processed by the matrix multiplication */ + unsigned int k0{1}; /**< Number of partial accumulations performed by the matrix multiplication */ + unsigned int v0{1}; /**< Number of vertical blocks of size (m0xk0) stored on the same output row */ + bool transpose{true}; /**< True if the (m0xk0) block has to be transposed before been stored */ + bool interleave{true}; /**< True if the v0 (m0xk0) blocks have to be interleaved in the output row */ }; /** GEMM RHS (Right Hand Side) matrix information */ @@ -1821,12 +1915,13 @@ struct GEMMRHSMatrixInfo : n0(n), k0(k), h0(h), transpose(trans), interleave(inter), export_to_cl_image(export_to_cl_img) { } - unsigned int n0{ 1 }; /**< Number of columns processed by the matrix multiplication */ - unsigned int k0{ 1 }; /**< Number of partial accumulations performed by the matrix multiplication */ - unsigned int h0{ 1 }; /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */ - bool transpose{ true }; /**< True if the (k0xn0) block has to be transposed before been stored */ - bool interleave{ true }; /**< True if the h0 (k0xn0) blocks have to be interleaved in the output row */ - bool export_to_cl_image{ false }; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */ + unsigned int n0{1}; /**< Number of columns processed by the matrix multiplication */ + unsigned int k0{1}; /**< Number of partial accumulations performed by the matrix multiplication */ + unsigned int h0{1}; /**< Number of horizontal blocks of size (k0xn0) stored on the same output row */ + bool transpose{true}; /**< True if the (k0xn0) block has to be transposed before been stored */ + bool interleave{true}; /**< True if the h0 (k0xn0) blocks have to be interleaved in the output row */ + bool export_to_cl_image{ + false}; /**< True if the reshaped rhs has to be exported to cl_image. n0 must be equal to 4 */ }; class ITensorInfo; @@ -1842,16 +1937,23 @@ struct WinogradInfo * @param[in] conv_info Convolution info (Pads, strides) * @param[in] data_layout Data layout to use for the output tensor once the convolution has been applied */ - WinogradInfo(Size2D output_tile_sz, Size2D kernel_sz, Size2D input_dims, PadStrideInfo conv_info, DataLayout data_layout) - : output_tile_size(output_tile_sz), kernel_size(kernel_sz), input_dimensions(input_dims), convolution_info(conv_info), output_data_layout(data_layout) - { - } - - Size2D output_tile_size{}; /**< Width and height of the output tile */ - Size2D kernel_size{}; /**< Width and height of the kernel*/ - Size2D input_dimensions{}; /**< Width and height of the input tensor before the convolution is applied */ - PadStrideInfo convolution_info{}; /**< Convolution info (Pads, strides,...) */ - DataLayout output_data_layout{ DataLayout::NCHW }; /**< Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) */ + WinogradInfo( + Size2D output_tile_sz, Size2D kernel_sz, Size2D input_dims, PadStrideInfo conv_info, DataLayout data_layout) + : output_tile_size(output_tile_sz), + kernel_size(kernel_sz), + input_dimensions(input_dims), + convolution_info(conv_info), + output_data_layout(data_layout) + { + } + + Size2D output_tile_size{}; /**< Width and height of the output tile */ + Size2D kernel_size{}; /**< Width and height of the kernel*/ + Size2D input_dimensions{}; /**< Width and height of the input tensor before the convolution is applied */ + PadStrideInfo convolution_info{}; /**< Convolution info (Pads, strides,...) */ + DataLayout output_data_layout{ + DataLayout:: + NCHW}; /**< Data layout to use for the output tensor once the convolution has been applied (NCHW or NHWC) */ }; /** IO formatting information class*/ |