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_im2col_shape(const ITensorInfo &input)
 {
     TensorShape shape_im2col{ input.tensor_shape() };
     shape_im2col.collapse(3);

     return shape_im2col;
 }
 inline TensorShape compute_col2im_shape(const ITensorInfo &input, std::pair<unsigned int, unsigned int> convolved_dims)
 {
     TensorShape col2im_shape{ input.tensor_shape() };
     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)
 {
     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;
 }
 inline TensorShape compute_im2col_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_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 Size2D &output_tile)
 {
     TensorShape tensor_shape{ input.tensor_shape() };

     tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH));
     tensor_shape.set(Window::DimY, input.dimension(2));
     tensor_shape.set(Window::DimZ, (output_tile.width == 2) ? 16 : 36);

     if(input.data_layout() == DataLayout::NCHW)
     {
         tensor_shape.set(Window::DimX, input.dimension(3));
     }

     return tensor_shape;
 }

 inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const PadStrideInfo &conv_info, const Size2D &kernel_size)
 {
     // Compute height
     const unsigned int num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f);
     const unsigned int num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f);

     const unsigned int width  = input.tensor_shape()[get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)];
     const unsigned int height = num_tiles_x * num_tiles_y;
     const unsigned int depth  = 16; // COMPMID-990

     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 Size2D &output_convolved_dims, DataLayout data_layout)
 {
     TensorShape tensor_shape{ input.tensor_shape() };

     // Output dimension
     const unsigned int out_w = output_convolved_dims.width;
     const unsigned int out_h = output_convolved_dims.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_channel = weights_shape[idx_channel];
     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_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_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;
 }

 } // 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_im2col_shape(const ITensorInfo &input)
	{
	TensorShape shape_im2col{ input.tensor_shape() };
	shape_im2col.collapse(3);

	return shape_im2col;
	}
	inline TensorShape compute_col2im_shape(const ITensorInfo &input, std::pair<unsigned int, unsigned int> convolved_dims)
	{
	TensorShape col2im_shape{ input.tensor_shape() };
	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)
	{
	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;
	}
	inline TensorShape compute_im2col_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_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 Size2D &output_tile)
	{
	TensorShape tensor_shape{ input.tensor_shape() };

	tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH));
	tensor_shape.set(Window::DimY, input.dimension(2));
	tensor_shape.set(Window::DimZ, (output_tile.width == 2) ? 16 : 36);

	if(input.data_layout() == DataLayout::NCHW)
	{
	tensor_shape.set(Window::DimX, input.dimension(3));
	}

	return tensor_shape;
	}

	inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const PadStrideInfo &conv_info, const Size2D &kernel_size)
	{
	// Compute height
	const unsigned int num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f);
	const unsigned int num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f);

	const unsigned int width = input.tensor_shape()[get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)];
	const unsigned int height = num_tiles_x * num_tiles_y;
	const unsigned int depth = 16; // COMPMID-990

	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 Size2D &output_convolved_dims, DataLayout data_layout)
	{
	TensorShape tensor_shape{ input.tensor_shape() };

	// Output dimension
	const unsigned int out_w = output_convolved_dims.width;
	const unsigned int out_h = output_convolved_dims.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_channel = weights_shape[idx_channel];
	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_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_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;
	}

	} // namespace shape_calculator
	} // namespace misc
	} // namespace arm_compute
	#endif /* __ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H__ */