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author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2018-06-13 14:05:54 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:53:57 +0000 |
commit | f1c2bf0971dd1c996da149faf3dd669d566074c7 (patch) | |
tree | 802b3ce5198c3209d77fc6b603c209023fe45650 /arm_compute/core/utils | |
parent | 89a2b571cfc0ea87c26ba8b1ed1ab87d13244f0e (diff) | |
download | ComputeLibrary-f1c2bf0971dd1c996da149faf3dd669d566074c7.tar.gz |
COMPMID-1201 - Implementing Winograd Convolution Layer 1x3 and 3x1 kernels on OpenCL
Change-Id: I39667bab49daa4da009694163274a59fd3574c73
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137595
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'arm_compute/core/utils')
-rw-r--r-- | arm_compute/core/utils/misc/ShapeCalculator.h | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 115cbe688d..221387649f 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -255,12 +255,14 @@ inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &inp const size_t idx_h = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT); const size_t idx_c = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL); - // Compute height - const unsigned int num_tiles_x = std::ceil((input.tensor_shape()[idx_w] - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width)); - const unsigned int num_tiles_y = std::ceil((input.tensor_shape()[idx_h] - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height)); + // Compute the number of output tiles along the x and y direction of size "output_tile_size" + const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input.tensor_shape()[idx_w], input.tensor_shape()[idx_h]), + kernel_size, + output_tile_size, + conv_info); const unsigned int width = input.tensor_shape()[idx_c]; - const unsigned int height = num_tiles_x * num_tiles_y; + const unsigned int height = num_tiles.area(); const unsigned int depth = input_tile_size.area(); TensorShape output_shape{ input.tensor_shape() }; |