| /* |
| * Copyright (c) 2017-2018 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_MISC_SHAPE_CALCULATOR_H__ |
| #define __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| |
| namespace arm_compute |
| { |
| namespace misc |
| { |
| namespace shape_calculator |
| { |
| inline TensorShape compute_permutation_output_shape(const ITensorInfo &input, const PermutationVector &perm) |
| { |
| TensorShape output_shape = input.tensor_shape(); |
| permute(output_shape, perm); |
| return output_shape; |
| } |
| inline TensorShape compute_interleaved_shape(const ITensorInfo &a, int mult_interleave4x4_height = 1) |
| { |
| // The interleaved output matrix will have the following shape: [ a_height * W, ceil(a_width / W) ] where W = 4 * mult_interleave4x4_height |
| ARM_COMPUTE_ERROR_ON(mult_interleave4x4_height < 1); |
| const int interleave_width = 4 * mult_interleave4x4_height; |
| TensorShape shape_interleaved_a{ a.tensor_shape() }; |
| shape_interleaved_a.set(0, a.dimension(0) * interleave_width); |
| shape_interleaved_a.set(1, std::ceil(a.dimension(1) / static_cast<float>(interleave_width))); |
| |
| return shape_interleaved_a; |
| } |
| inline TensorShape compute_transpose1xW_shape(const ITensorInfo &b) |
| { |
| // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] |
| TensorShape shape_transposed1xW_b{ b.tensor_shape() }; |
| shape_transposed1xW_b.set(0, b.dimension(1) * 16); |
| shape_transposed1xW_b.set(1, std::ceil(b.dimension(0) / 16.f)); |
| |
| return shape_transposed1xW_b; |
| } |
| inline TensorShape compute_transpose1xW_with_element_size_shape(const ITensorInfo &b, int mult_transpose1xW_width = 1) |
| { |
| // Note: mult_transpose1xW_width expresses the number of chunks with size 1x(W) we want to store on the same row |
| // The transpose1xW output matrix will have the following shape: |
| // [ b_height * W, ceil(b_width / W) ] where W = (16 / element size of the tensor) * mult_transpose1xW_width |
| ARM_COMPUTE_ERROR_ON(mult_transpose1xW_width < 1); |
| TensorShape shape_transposed1xW_b{ b.tensor_shape() }; |
| const size_t transpose_width = (16 / b.element_size()) * mult_transpose1xW_width; |
| shape_transposed1xW_b.set(0, b.dimension(1) * transpose_width); |
| shape_transposed1xW_b.set(1, static_cast<size_t>(std::ceil(b.dimension(0) / static_cast<float>(transpose_width)))); |
| |
| return shape_transposed1xW_b; |
| } |
| inline TensorShape compute_reductionA_shape(const ITensorInfo &b) |
| { |
| TensorShape shape_vector_sum_col{ b.tensor_shape() }; |
| if(shape_vector_sum_col.num_dimensions() > 1) |
| { |
| shape_vector_sum_col.remove_dimension(1); |
| } |
| |
| return shape_vector_sum_col; |
| } |
| inline TensorShape compute_reductionB_shape(const ITensorInfo &a) |
| { |
| TensorShape shape_vector_sum_row{ a.tensor_shape() }; |
| shape_vector_sum_row.set(Window::DimX, a.dimension(1)); |
| if(a.num_dimensions() > 1) |
| { |
| shape_vector_sum_row.remove_dimension(1); |
| } |
| |
| return shape_vector_sum_row; |
| } |
| inline TensorShape compute_im2col_shape(const ITensorInfo &input) |
| { |
| TensorShape shape_im2col{ input.tensor_shape() }; |
| shape_im2col.collapse(3); |
| |
| return shape_im2col; |
| } |
| inline TensorShape compute_transposed_shape(const ITensorInfo &input) |
| { |
| TensorShape shape_transposed{ input.tensor_shape() }; |
| |
| shape_transposed.set(0, input.dimension(1)); |
| shape_transposed.set(1, input.dimension(0)); |
| |
| return shape_transposed; |
| } |
| inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info) |
| { |
| const TensorShape input_shape{ input.tensor_shape() }; |
| const TensorShape weights_shape{ weights.tensor_shape() }; |
| |
| unsigned int output_width = 0; |
| unsigned int output_height = 0; |
| std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), |
| weights_shape.x(), weights_shape.y(), |
| conv_info); |
| |
| TensorShape output_shape{ input_shape }; |
| output_shape.set(0, output_width); |
| output_shape.set(1, output_height); |
| |
| return output_shape; |
| } |
| inline TensorShape compute_deconvolution_shape(const ITensorInfo &input, unsigned int sx, unsigned int sy, unsigned int inner_border_right, unsigned int inner_border_top, const PadStrideInfo &info) |
| { |
| TensorShape scale_out_shape(input.tensor_shape()); |
| const unsigned int out_x = input.dimension(0) + (input.dimension(0) - 1) * (sx - 1) + inner_border_right + 2 * info.pad().first; |
| const unsigned int out_y = input.dimension(1) + (input.dimension(1) - 1) * (sy - 1) + inner_border_top + 2 * info.pad().second; |
| scale_out_shape.set(0, out_x); |
| scale_out_shape.set(1, out_y); |
| |
| return scale_out_shape; |
| } |
| } // namespace shape_calculator |
| } // namespace misc |
| } // namespace arm_compute |
| #endif /* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */ |