arm_compute/core/utils/misc/ShapeCalculator.h - ml/ComputeLibrary - Gitiles

 /*
  * 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"

 #include <cmath>

 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_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias = false)
 {
     // Calculate output shape
     TensorShape weights_reshaped{ weights.tensor_shape() };
     weights_reshaped.collapse(3);
     const size_t tmp_dim = weights_reshaped[0];
     weights_reshaped.set(0, weights_reshaped[1]);
     weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));

     return weights_reshaped;
 }
 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_col2im_shape(const ITensorInfo &input, std::pair<unsigned int, unsigned int> convolved_dims)
 {
     TensorShape col2im_shape{ input.tensor_shape() };
     col2im_shape.shift_right(1);
     col2im_shape.set(0, convolved_dims.first);
     col2im_shape.set(1, convolved_dims.second);
     col2im_shape.set(2, input.tensor_shape()[0]);

     return col2im_shape;
 }
 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, unsigned int depth_multiplier)
 {
     const TensorShape input_shape{ input.tensor_shape() };
     const TensorShape weights_shape{ weights.tensor_shape() };

     const DataLayout data_layout = input.data_layout();
     const int        width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
     const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

     unsigned int output_width  = 0;
     unsigned int output_height = 0;
     std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx],
                                                               weights_shape[width_idx], weights_shape[height_idx],
                                                               conv_info);

     TensorShape output_shape{ input_shape };
     output_shape.set(width_idx, output_width);
     output_shape.set(height_idx, output_height);
     output_shape.set(channel_idx, input_shape[channel_idx] * depth_multiplier);

     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;
 }
 inline TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
 {
     // The output shape will be the 2D shape used as input for GEMM [ out_channels * kernel_area, num_elems_per_out_channel ]

     TensorShape output_shape{ input->tensor_shape() };

     const DataLayout data_layout = input->data_layout();
     const int        width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
     const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

     std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(output_shape[width_idx], output_shape[height_idx], kernel_dims.width, kernel_dims.height, conv_info, dilation);
     output_shape.set(0, (output_shape[channel_idx] * kernel_dims.area() + (has_bias ? 1 : 0)));
     output_shape.set(1, (out_dims.first * out_dims.second));
     output_shape.set(2, 1);

     return output_shape;
 }
 inline TensorShape compute_im2col_fc_shape(const ITensorInfo *input, const int num_input_dimensions = 3)
 {
     TensorShape output_shape{ input->tensor_shape() };

     output_shape.collapse(num_input_dimensions);

     return output_shape;
 }
 inline TensorShape compute_im2col_flatten_shape(const ITensorInfo *input)
 {
     // The output shape will be the flatten version of the input (i.e. [ width * height * channels, 1, 1, ... ] ). Used for FlattenLayer.

     ARM_COMPUTE_ERROR_ON(input->num_dimensions() < 3);

     TensorShape output_shape{ input->tensor_shape() };

     const size_t flatten_shape = input->dimension(0) * input->dimension(1) * input->dimension(2);
     output_shape.set(0, flatten_shape);
     output_shape.remove_dimension(1);
     output_shape.remove_dimension(1);

     return output_shape;
 }
 inline TensorShape compute_interleave_custom_shape(const TensorShape &input, const int x_interleave, const int y_interleave)
 {
     TensorShape output_shape{ input };

     output_shape.set(0, output_shape.x() * x_interleave);
     output_shape.set(1, std::ceil(output_shape.y() / static_cast<float>(y_interleave)));

     return output_shape;
 }
 inline TensorShape compute_fully_connected_reshaped_weights_shape(const ITensorInfo *input, bool transpose_weights, bool is_batched_fc_layer, const int interleave)
 {
     TensorShape output_shape{ input->tensor_shape() };

     // Transpose weights if the user hasn't done it
     if(transpose_weights)
     {
         output_shape = compute_transposed_shape(*input);
     }

     // If we run multiple batches we need 1xW transpose, too.
     if(is_batched_fc_layer)
     {
         output_shape = compute_transposed_shape(input->clone()->set_tensor_shape(output_shape));
         output_shape = compute_interleave_custom_shape(output_shape, interleave, interleave);
     }

     return output_shape;
 }

 inline TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
 {
     TensorShape tensor_shape{ input.tensor_shape() };

     const Size2D kernel_size      = winograd_info.kernel_size;
     const Size2D output_tile_size = winograd_info.output_tile_size;
     const Size2D input_tile_size  = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);

     tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH));
     tensor_shape.set(Window::DimX, input.dimension(3));
     tensor_shape.set(Window::DimY, input.dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)));
     tensor_shape.set(Window::DimZ, input_tile_size.area());

     return tensor_shape;
 }
 inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
 {
     const PadStrideInfo conv_info        = winograd_info.convolution_info;
     const Size2D        kernel_size      = winograd_info.kernel_size;
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const Size2D        input_tile_size  = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);

     const size_t idx_w = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
     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));

     const unsigned int width  = input.tensor_shape()[idx_c];
     const unsigned int height = num_tiles_x * num_tiles_y;
     const unsigned int depth  = input_tile_size.area();

     TensorShape output_shape{ input.tensor_shape() };
     output_shape.set(0, width);
     output_shape.set(1, height);
     output_shape.set(2, depth);

     return output_shape;
 }
 inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
 {
     const PadStrideInfo conv_info        = winograd_info.convolution_info;
     const Size2D        kernel_size      = winograd_info.kernel_size;
     const Size2D        input_dimensions = winograd_info.input_dimensions;
     const DataLayout    data_layout      = winograd_info.output_data_layout;

     // Compute output shape
     unsigned int output_width  = 0;
     unsigned int output_height = 0;
     std::tie(output_width, output_height) = scaled_dimensions(input_dimensions.width, input_dimensions.height,
                                                               kernel_size.width, kernel_size.height, conv_info);

     TensorShape tensor_shape{ input.tensor_shape() };

     // Output dimension
     const unsigned int out_w = output_width;
     const unsigned int out_h = output_height;
     const unsigned int out_c = input.dimension(0);

     tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH), out_w);
     tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT), out_h);
     tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL), out_c);

     return tensor_shape;
 }
 inline TensorShape compute_deep_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() };

     const size_t idx_width   = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_height  = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
     const size_t idx_channel = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL);

     const unsigned int input_width         = input_shape[idx_width];
     const unsigned int input_height        = input_shape[idx_height];
     const unsigned int weights_width       = weights_shape[idx_width];
     const unsigned int weights_height      = weights_shape[idx_height];
     const unsigned int weights_out_channel = weights_shape[3];
     unsigned int       output_width        = 0;
     unsigned int       output_height       = 0;
     std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, weights_width, weights_height, conv_info);

     TensorShape output_shape{ input_shape };
     output_shape.set(idx_width, output_width);
     output_shape.set(idx_height, output_height);
     output_shape.set(idx_channel, weights_out_channel);

     return output_shape;
 }
 inline TensorShape compute_min_max_shape(const ITensorInfo *input)
 {
     TensorShape output_shape{ input->tensor_shape() };
     output_shape.set(Window::DimX, 2);
     output_shape.remove_dimension(1);
     output_shape.remove_dimension(1);

     return output_shape;
 }

 inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo pool_info)
 {
     unsigned int pooled_w = 0;
     unsigned int pooled_h = 0;

     TensorShape output_shape{ input.tensor_shape() };

     const bool         is_global_pooling = pool_info.is_global_pooling();
     const unsigned int idx_width         = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
     const unsigned int idx_height        = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
     const unsigned int pool_size_x       = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size().width;
     const unsigned int pool_size_y       = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size().height;

     std::tie(pooled_w, pooled_h) = scaled_dimensions(output_shape[idx_width],
                                                      output_shape[idx_height],
                                                      pool_size_x,
                                                      pool_size_y,
                                                      pool_info.pad_stride_info());

     output_shape.set(idx_width, pooled_w);
     output_shape.set(idx_height, pooled_h);

     return output_shape;
 }

 inline TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size)
 {
     TensorShape output_shape{ input->tensor_shape() };
     output_shape.set(1, batch_size);

     return output_shape;
 }
 inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
 {
     ARM_COMPUTE_ERROR_ON_MSG(input0.num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");

     const bool is_gemm3d = reshape_info.depth_output_gemm3d() != 1;

     // If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third
     // dimension of the output tensor
     const int dim0 = is_interleaved_transposed ? reshape_info.n() : input1.dimension(0);
     const int dim1 = is_interleaved_transposed ? reshape_info.m() / reshape_info.depth_output_gemm3d() : input0.dimension(1) / reshape_info.depth_output_gemm3d();
     const int dim2 = input0.tensor_shape()[2];
     const int dim3 = input0.tensor_shape()[3];

     TensorShape output_shape{ input0.tensor_shape() };

     output_shape.set(0, dim0);
     output_shape.set(1, dim1);
     output_shape.set(2, is_gemm3d ? reshape_info.depth_output_gemm3d() : dim2);
     output_shape.set(3, is_gemm3d ? dim2 : dim3);
     output_shape.set(4, is_gemm3d ? dim3 : 1);

     return output_shape;
 }

 template <typename T>
 inline TensorShape get_shape_from_info(T *info)
 {
     return info->info()->tensor_shape();
 }

 inline TensorShape get_shape_from_info(ITensorInfo *info)
 {
     return info->tensor_shape();
 }

 template <typename T>
 inline TensorShape calculate_width_concatenate_shape(const std::vector<T *> &inputs_vector)
 {
     TensorShape out_shape = get_shape_from_info(inputs_vector[0]);

     size_t width = 0;
     for(const auto &tensor : inputs_vector)
     {
         ARM_COMPUTE_ERROR_ON(tensor == nullptr);
         const TensorShape shape = get_shape_from_info(tensor);
         width += shape.x();
     }

     out_shape.set(0, width);

     return out_shape;
 }
 } // namespace shape_calculator
 } // namespace misc
 } // namespace arm_compute
 #endif /* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */
	/*
	* 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"

	#include <cmath>

	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_weights_reshaped_shape(const ITensorInfo &weights, bool has_bias = false)
	{
	// Calculate output shape
	TensorShape weights_reshaped{ weights.tensor_shape() };
	weights_reshaped.collapse(3);
	const size_t tmp_dim = weights_reshaped[0];
	weights_reshaped.set(0, weights_reshaped[1]);
	weights_reshaped.set(1, tmp_dim + (has_bias ? 1 : 0));

	return weights_reshaped;
	}
	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_col2im_shape(const ITensorInfo &input, std::pair<unsigned int, unsigned int> convolved_dims)
	{
	TensorShape col2im_shape{ input.tensor_shape() };
	col2im_shape.shift_right(1);
	col2im_shape.set(0, convolved_dims.first);
	col2im_shape.set(1, convolved_dims.second);
	col2im_shape.set(2, input.tensor_shape()[0]);

	return col2im_shape;
	}
	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, unsigned int depth_multiplier)
	{
	const TensorShape input_shape{ input.tensor_shape() };
	const TensorShape weights_shape{ weights.tensor_shape() };

	const DataLayout data_layout = input.data_layout();
	const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
	const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

	unsigned int output_width = 0;
	unsigned int output_height = 0;
	std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx],
	weights_shape[width_idx], weights_shape[height_idx],
	conv_info);

	TensorShape output_shape{ input_shape };
	output_shape.set(width_idx, output_width);
	output_shape.set(height_idx, output_height);
	output_shape.set(channel_idx, input_shape[channel_idx] * depth_multiplier);

	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;
	}
	inline TensorShape compute_im2col_conv_shape(const ITensorInfo *input, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
	{
	// The output shape will be the 2D shape used as input for GEMM [ out_channels * kernel_area, num_elems_per_out_channel ]

	TensorShape output_shape{ input->tensor_shape() };

	const DataLayout data_layout = input->data_layout();
	const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
	const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);

	std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(output_shape[width_idx], output_shape[height_idx], kernel_dims.width, kernel_dims.height, conv_info, dilation);
	output_shape.set(0, (output_shape[channel_idx] * kernel_dims.area() + (has_bias ? 1 : 0)));
	output_shape.set(1, (out_dims.first * out_dims.second));
	output_shape.set(2, 1);

	return output_shape;
	}
	inline TensorShape compute_im2col_fc_shape(const ITensorInfo *input, const int num_input_dimensions = 3)
	{
	TensorShape output_shape{ input->tensor_shape() };

	output_shape.collapse(num_input_dimensions);

	return output_shape;
	}
	inline TensorShape compute_im2col_flatten_shape(const ITensorInfo *input)
	{
	// The output shape will be the flatten version of the input (i.e. [ width * height * channels, 1, 1, ... ] ). Used for FlattenLayer.

	ARM_COMPUTE_ERROR_ON(input->num_dimensions() < 3);

	TensorShape output_shape{ input->tensor_shape() };

	const size_t flatten_shape = input->dimension(0) * input->dimension(1) * input->dimension(2);
	output_shape.set(0, flatten_shape);
	output_shape.remove_dimension(1);
	output_shape.remove_dimension(1);

	return output_shape;
	}
	inline TensorShape compute_interleave_custom_shape(const TensorShape &input, const int x_interleave, const int y_interleave)
	{
	TensorShape output_shape{ input };

	output_shape.set(0, output_shape.x() * x_interleave);
	output_shape.set(1, std::ceil(output_shape.y() / static_cast<float>(y_interleave)));

	return output_shape;
	}
	inline TensorShape compute_fully_connected_reshaped_weights_shape(const ITensorInfo *input, bool transpose_weights, bool is_batched_fc_layer, const int interleave)
	{
	TensorShape output_shape{ input->tensor_shape() };

	// Transpose weights if the user hasn't done it
	if(transpose_weights)
	{
	output_shape = compute_transposed_shape(*input);
	}

	// If we run multiple batches we need 1xW transpose, too.
	if(is_batched_fc_layer)
	{
	output_shape = compute_transposed_shape(input->clone()->set_tensor_shape(output_shape));
	output_shape = compute_interleave_custom_shape(output_shape, interleave, interleave);
	}

	return output_shape;
	}

	inline TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
	{
	TensorShape tensor_shape{ input.tensor_shape() };

	const Size2D kernel_size = winograd_info.kernel_size;
	const Size2D output_tile_size = winograd_info.output_tile_size;
	const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);

	tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH));
	tensor_shape.set(Window::DimX, input.dimension(3));
	tensor_shape.set(Window::DimY, input.dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)));
	tensor_shape.set(Window::DimZ, input_tile_size.area());

	return tensor_shape;
	}
	inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
	{
	const PadStrideInfo conv_info = winograd_info.convolution_info;
	const Size2D kernel_size = winograd_info.kernel_size;
	const Size2D output_tile_size = winograd_info.output_tile_size;
	const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1);

	const size_t idx_w = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
	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));

	const unsigned int width = input.tensor_shape()[idx_c];
	const unsigned int height = num_tiles_x * num_tiles_y;
	const unsigned int depth = input_tile_size.area();

	TensorShape output_shape{ input.tensor_shape() };
	output_shape.set(0, width);
	output_shape.set(1, height);
	output_shape.set(2, depth);

	return output_shape;
	}
	inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info)
	{
	const PadStrideInfo conv_info = winograd_info.convolution_info;
	const Size2D kernel_size = winograd_info.kernel_size;
	const Size2D input_dimensions = winograd_info.input_dimensions;
	const DataLayout data_layout = winograd_info.output_data_layout;

	// Compute output shape
	unsigned int output_width = 0;
	unsigned int output_height = 0;
	std::tie(output_width, output_height) = scaled_dimensions(input_dimensions.width, input_dimensions.height,
	kernel_size.width, kernel_size.height, conv_info);

	TensorShape tensor_shape{ input.tensor_shape() };

	// Output dimension
	const unsigned int out_w = output_width;
	const unsigned int out_h = output_height;
	const unsigned int out_c = input.dimension(0);

	tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH), out_w);
	tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT), out_h);
	tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL), out_c);

	return tensor_shape;
	}
	inline TensorShape compute_deep_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() };

	const size_t idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
	const size_t idx_height = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
	const size_t idx_channel = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL);

	const unsigned int input_width = input_shape[idx_width];
	const unsigned int input_height = input_shape[idx_height];
	const unsigned int weights_width = weights_shape[idx_width];
	const unsigned int weights_height = weights_shape[idx_height];
	const unsigned int weights_out_channel = weights_shape[3];
	unsigned int output_width = 0;
	unsigned int output_height = 0;
	std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, weights_width, weights_height, conv_info);

	TensorShape output_shape{ input_shape };
	output_shape.set(idx_width, output_width);
	output_shape.set(idx_height, output_height);
	output_shape.set(idx_channel, weights_out_channel);

	return output_shape;
	}
	inline TensorShape compute_min_max_shape(const ITensorInfo *input)
	{
	TensorShape output_shape{ input->tensor_shape() };
	output_shape.set(Window::DimX, 2);
	output_shape.remove_dimension(1);
	output_shape.remove_dimension(1);

	return output_shape;
	}

	inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo pool_info)
	{
	unsigned int pooled_w = 0;
	unsigned int pooled_h = 0;

	TensorShape output_shape{ input.tensor_shape() };

	const bool is_global_pooling = pool_info.is_global_pooling();
	const unsigned int idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH);
	const unsigned int idx_height = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
	const unsigned int pool_size_x = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size().width;
	const unsigned int pool_size_y = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size().height;

	std::tie(pooled_w, pooled_h) = scaled_dimensions(output_shape[idx_width],
	output_shape[idx_height],
	pool_size_x,
	pool_size_y,
	pool_info.pad_stride_info());

	output_shape.set(idx_width, pooled_w);
	output_shape.set(idx_height, pooled_h);

	return output_shape;
	}

	inline TensorShape compute_rnn_shape(const ITensorInfo *input, const unsigned int batch_size)
	{
	TensorShape output_shape{ input->tensor_shape() };
	output_shape.set(1, batch_size);

	return output_shape;
	}
	inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo &input1, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info)
	{
	ARM_COMPUTE_ERROR_ON_MSG(input0.num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");

	const bool is_gemm3d = reshape_info.depth_output_gemm3d() != 1;

	// If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third
	// dimension of the output tensor
	const int dim0 = is_interleaved_transposed ? reshape_info.n() : input1.dimension(0);
	const int dim1 = is_interleaved_transposed ? reshape_info.m() / reshape_info.depth_output_gemm3d() : input0.dimension(1) / reshape_info.depth_output_gemm3d();
	const int dim2 = input0.tensor_shape()[2];
	const int dim3 = input0.tensor_shape()[3];

	TensorShape output_shape{ input0.tensor_shape() };

	output_shape.set(0, dim0);
	output_shape.set(1, dim1);
	output_shape.set(2, is_gemm3d ? reshape_info.depth_output_gemm3d() : dim2);
	output_shape.set(3, is_gemm3d ? dim2 : dim3);
	output_shape.set(4, is_gemm3d ? dim3 : 1);

	return output_shape;
	}

	template <typename T>
	inline TensorShape get_shape_from_info(T *info)
	{
	return info->info()->tensor_shape();
	}

	inline TensorShape get_shape_from_info(ITensorInfo *info)
	{
	return info->tensor_shape();
	}

	template <typename T>
	inline TensorShape calculate_width_concatenate_shape(const std::vector<T *> &inputs_vector)
	{
	TensorShape out_shape = get_shape_from_info(inputs_vector[0]);

	size_t width = 0;
	for(const auto &tensor : inputs_vector)
	{
	ARM_COMPUTE_ERROR_ON(tensor == nullptr);
	const TensorShape shape = get_shape_from_info(tensor);
	width += shape.x();
	}

	out_shape.set(0, width);

	return out_shape;
	}
	} // namespace shape_calculator
	} // namespace misc
	} // namespace arm_compute
	#endif /* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */