src/runtime/NEON/functions/NEFullyConnectedLayer.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017 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.
  */
 #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"

 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"

 #include <algorithm>
 #include <cmath>

 using namespace arm_compute;

 NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights()
     : _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
 {
 }

 void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON(output == nullptr);
     ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2);
     ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false));

     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     _transpose_weights   = transpose_weights;
     _is_batched_fc_layer = is_batched_fc_layer;

     // Check if we need to transpose the weights
     if(_transpose_weights)
     {
         if(_is_batched_fc_layer)
         {
             // Initialize the output tensor for transpose
             TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
             _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, dt, fixed_point_position));
             _transpose_kernel.configure(input, &_transpose_output);

             // Configure transpose 1xW kernel
             _transpose1xW_kernel.configure(&_transpose_output, output);

             // Allocate temporary tensor used for transposing the weights
             _transpose_output.allocator()->allocate();
         }
         else
         {
             _transpose_kernel.configure(input, output);
         }
     }
     else
     {
         if(_is_batched_fc_layer)
         {
             // Configure transpose 1xW kernel
             _transpose1xW_kernel.configure(input, output);
         }
         else
         {
             ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
         }
     }
 }

 void NEFullyConnectedLayerReshapeWeights::run()
 {
     if(_transpose_weights)
     {
         NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
     }
     if(_is_batched_fc_layer)
     {
         NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
     }
 }

 NEFullyConnectedLayer::NEFullyConnectedLayer()
     : _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(),
       _are_weights_reshaped(false), _is_fc_after_conv(false), _is_batched_fc_layer(false), _accumulate_biases(false)
 {
 }

 void NEFullyConnectedLayer::configure_conv_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output)
 {
     ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2) * (16 / weights->info()->element_size())));

     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized

     // Initialize output tensor for im2col
     TensorShape shape_im2col;
     shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
     shape_im2col.set(1, input->info()->dimension(3));
     shape_im2col.set(2, input->info()->dimension(4));
     shape_im2col.set(3, input->info()->dimension(5));
     _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));

     // Initialize output tensor for interleave 4x4
     TensorShape shape_interleaved = _im2col_output.info()->tensor_shape();
     shape_interleaved.set(0, shape_interleaved.x() * 4);
     shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
     _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));

     // Configure im2col kernel
     _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);

     // Configure interleave4x4 kernel
     _interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output);

     // Configure matrix multiply kernel
     _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);

     // Allocate the tensors once all the configure methods have been called
     _im2col_output.allocator()->allocate();
     _interleave4x4_output.allocator()->allocate();
 }

 void NEFullyConnectedLayer::configure_fc_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output)
 {
     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     // Initialize output tensor for interleave 4x4
     TensorShape shape_interleaved = input->info()->tensor_shape();
     shape_interleaved.set(0, shape_interleaved.x() * 4);
     shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
     _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));

     // Configure interleave4x4 kernel
     _interleave4x4_kernel.configure(input, &_interleave4x4_output);

     // Configure matrix multiply kernel
     _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);

     // Allocate the tensors once all the configure methods have been called
     _interleave4x4_output.allocator()->allocate();
 }

 void NEFullyConnectedLayer::configure_conv_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output)
 {
     ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));

     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized

     // Initialize output tensor for im2col
     TensorShape shape_im2col;
     shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
     shape_im2col.set(1, 1);
     _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));

     // Configure im2col kernel
     _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);

     // Configure matrix multiply kernel
     _mm_kernel.configure(&_im2col_output, weights, output, 1.0f);

     // Allocate the output tensor for im2col once all the configure methods have been called
     _im2col_output.allocator()->allocate();
 }

 void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output)
 {
     ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

     // Configure matrix multiply kernel
     _mm_kernel.configure(input, weights, output, 1.0f);
 }

 void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2);

     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     _are_weights_reshaped = are_weights_reshaped;
     _is_fc_after_conv     = true;
     _is_batched_fc_layer  = false;
     _accumulate_biases    = false;

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);

         _accumulate_biases = true;

         // Configure accumulate biases kernel
         _accumulate_biases_kernel.configure(output, biases);
     }

     // With the Fully Connected layer we can have 4 different cases:
     //  1) Convolution layer -> Fully Connected layer without batches
     //  2) Fully Connected layer -> Fully Connected layer without batches
     //  3) Convolution layer -> Fully Connected layer with batches
     //  4) Fully Connected layer -> Fully Connected layer with batches

     // Check if we have a fully connected layer with batches
     _is_batched_fc_layer = (output->info()->dimension(1) > 1);

     const ITensor *weights_to_use = weights;

     if(!are_weights_reshaped)
     {
         if((transpose_weights || _is_batched_fc_layer))
         {
             weights_to_use = &_reshape_weights_output;

             if(transpose_weights)
             {
                 if(_is_batched_fc_layer)
                 {
                     const float transpose_width = 16.0f / input->info()->element_size();
                     TensorShape shape_wt(weights->info()->dimension(0) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(1) / transpose_width)));
                     TensorInfo  info_wt(shape_wt, 1, dt, fixed_point_position);
                     _reshape_weights_output.allocator()->init(info_wt);
                 }
                 else
                 {
                     TensorShape shape_wt(weights->info()->dimension(1), weights->info()->dimension(0));
                     TensorInfo  info_wt(shape_wt, 1, dt, fixed_point_position);
                     _reshape_weights_output.allocator()->init(info_wt);
                 }
             }
             else
             {
                 ARM_COMPUTE_ERROR_ON(!_is_batched_fc_layer);

                 const float transpose_width = 16.0f / input->info()->element_size();
                 TensorShape shape_wt(weights->info()->dimension(1) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(0) / transpose_width)));
                 TensorInfo  info_wt(shape_wt, 1, dt, fixed_point_position);
                 _reshape_weights_output.allocator()->init(info_wt);
             }

             // Reshape the weights
             _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer);
         }
     }

     if(_is_batched_fc_layer)
     {
         _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
                                                                                   input->info()->tensor_shape().cend(),
                                                                                   output->info()->tensor_shape().cbegin() + 1));

         if(_is_fc_after_conv)
         {
             // Fully Connected layer after a Convolution Layer with batches
             configure_conv_fc_wb(input, weights_to_use, output);
         }
         else
         {
             // Fully Connected layer after a Fully Connected Layer with batches
             configure_fc_fc_wb(input, weights_to_use, output);
         }
     }
     else
     {
         // In case of not batched fully connected layer, the weights will not be reshaped using transposed1xW
         _is_fc_after_conv = ((weights_to_use->info()->dimension(1)) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)));

         if(_is_fc_after_conv)
         {
             // Fully Connected layer after a Convolution Layer without batches
             configure_conv_fc_nb(input, weights_to_use, output);
         }
         else
         {
             // Fully Connected layer after a Fully Connected Layer without batches
             configure_fc_fc_nb(input, weights_to_use, output);
         }
     }

     // Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
     if(!are_weights_reshaped)
     {
         if(transpose_weights || _is_batched_fc_layer)
         {
             // Allocate the tensor for the weights reshaped
             _reshape_weights_output.allocator()->allocate();
         }
     }
 }

 void NEFullyConnectedLayer::run()
 {
     // Reshape of the weights (happens only once)
     if(!_are_weights_reshaped)
     {
         _are_weights_reshaped = true;
         _reshape_weights_kernel.run();
     }

     // Linearize input if comes from a convolutional layer
     if(_is_fc_after_conv)
     {
         NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
     }

     // Interleave input
     if(_is_batched_fc_layer)
     {
         NEScheduler::get().schedule(&_interleave4x4_kernel, Window::DimY);
     }

     // Run matrix multiply
     NEScheduler::get().schedule(&_mm_kernel, _is_batched_fc_layer ? Window::DimY : Window::DimX);

     // Accumulate biases if provided
     if(_accumulate_biases)
     {
         NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
     }
 }
	/*
	* Copyright (c) 2017 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.
	*/
	#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"

	#include "arm_compute/core/Validate.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"

	#include <algorithm>
	#include <cmath>

	using namespace arm_compute;

	NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights()
	: _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
	{
	}

	void NEFullyConnectedLayerReshapeWeights::configure(const ITensor input, ITensor output, bool transpose_weights, bool is_batched_fc_layer)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON(output == nullptr);
	ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2);
	ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false));

	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	_transpose_weights = transpose_weights;
	_is_batched_fc_layer = is_batched_fc_layer;

	// Check if we need to transpose the weights
	if(_transpose_weights)
	{
	if(_is_batched_fc_layer)
	{
	// Initialize the output tensor for transpose
	TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
	_transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, dt, fixed_point_position));
	_transpose_kernel.configure(input, &_transpose_output);

	// Configure transpose 1xW kernel
	_transpose1xW_kernel.configure(&_transpose_output, output);

	// Allocate temporary tensor used for transposing the weights
	_transpose_output.allocator()->allocate();
	}
	else
	{
	_transpose_kernel.configure(input, output);
	}
	}
	else
	{
	if(_is_batched_fc_layer)
	{
	// Configure transpose 1xW kernel
	_transpose1xW_kernel.configure(input, output);
	}
	else
	{
	ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
	}
	}
	}

	void NEFullyConnectedLayerReshapeWeights::run()
	{
	if(_transpose_weights)
	{
	NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
	}
	if(_is_batched_fc_layer)
	{
	NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
	}
	}

	NEFullyConnectedLayer::NEFullyConnectedLayer()
	: _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(),
	_are_weights_reshaped(false), _is_fc_after_conv(false), _is_batched_fc_layer(false), _accumulate_biases(false)
	{
	}

	void NEFullyConnectedLayer::configure_conv_fc_wb(const ITensor input, const ITensor weights, ITensor *output)
	{
	ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2) * (16 / weights->info()->element_size())));

	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	// If the fully connected layer is called after a convolution layer, the input tensor must be linearized

	// Initialize output tensor for im2col
	TensorShape shape_im2col;
	shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
	shape_im2col.set(1, input->info()->dimension(3));
	shape_im2col.set(2, input->info()->dimension(4));
	shape_im2col.set(3, input->info()->dimension(5));
	_im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));

	// Initialize output tensor for interleave 4x4
	TensorShape shape_interleaved = _im2col_output.info()->tensor_shape();
	shape_interleaved.set(0, shape_interleaved.x() * 4);
	shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
	_interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));

	// Configure im2col kernel
	_im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);

	// Configure interleave4x4 kernel
	_interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output);

	// Configure matrix multiply kernel
	_mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);

	// Allocate the tensors once all the configure methods have been called
	_im2col_output.allocator()->allocate();
	_interleave4x4_output.allocator()->allocate();
	}

	void NEFullyConnectedLayer::configure_fc_fc_wb(const ITensor input, const ITensor weights, ITensor *output)
	{
	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	// Initialize output tensor for interleave 4x4
	TensorShape shape_interleaved = input->info()->tensor_shape();
	shape_interleaved.set(0, shape_interleaved.x() * 4);
	shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
	_interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));

	// Configure interleave4x4 kernel
	_interleave4x4_kernel.configure(input, &_interleave4x4_output);

	// Configure matrix multiply kernel
	_mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f);

	// Allocate the tensors once all the configure methods have been called
	_interleave4x4_output.allocator()->allocate();
	}

	void NEFullyConnectedLayer::configure_conv_fc_nb(const ITensor input, const ITensor weights, ITensor *output)
	{
	ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));

	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	// If the fully connected layer is called after a convolution layer, the input tensor must be linearized

	// Initialize output tensor for im2col
	TensorShape shape_im2col;
	shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
	shape_im2col.set(1, 1);
	_im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));

	// Configure im2col kernel
	_im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);

	// Configure matrix multiply kernel
	_mm_kernel.configure(&_im2col_output, weights, output, 1.0f);

	// Allocate the output tensor for im2col once all the configure methods have been called
	_im2col_output.allocator()->allocate();
	}

	void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor input, const ITensor weights, ITensor *output)
	{
	ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

	// Configure matrix multiply kernel
	_mm_kernel.configure(input, weights, output, 1.0f);
	}

	void NEFullyConnectedLayer::configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, bool transpose_weights, bool are_weights_reshaped)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2);

	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	_are_weights_reshaped = are_weights_reshaped;
	_is_fc_after_conv = true;
	_is_batched_fc_layer = false;
	_accumulate_biases = false;

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);

	_accumulate_biases = true;

	// Configure accumulate biases kernel
	_accumulate_biases_kernel.configure(output, biases);
	}

	// With the Fully Connected layer we can have 4 different cases:
	// 1) Convolution layer -> Fully Connected layer without batches
	// 2) Fully Connected layer -> Fully Connected layer without batches
	// 3) Convolution layer -> Fully Connected layer with batches
	// 4) Fully Connected layer -> Fully Connected layer with batches

	// Check if we have a fully connected layer with batches
	_is_batched_fc_layer = (output->info()->dimension(1) > 1);

	const ITensor *weights_to_use = weights;

	if(!are_weights_reshaped)
	{
	if((transpose_weights \|\| _is_batched_fc_layer))
	{
	weights_to_use = &_reshape_weights_output;

	if(transpose_weights)
	{
	if(_is_batched_fc_layer)
	{
	const float transpose_width = 16.0f / input->info()->element_size();
	TensorShape shape_wt(weights->info()->dimension(0) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(1) / transpose_width)));
	TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
	_reshape_weights_output.allocator()->init(info_wt);
	}
	else
	{
	TensorShape shape_wt(weights->info()->dimension(1), weights->info()->dimension(0));
	TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
	_reshape_weights_output.allocator()->init(info_wt);
	}
	}
	else
	{
	ARM_COMPUTE_ERROR_ON(!_is_batched_fc_layer);

	const float transpose_width = 16.0f / input->info()->element_size();
	TensorShape shape_wt(weights->info()->dimension(1) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(0) / transpose_width)));
	TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
	_reshape_weights_output.allocator()->init(info_wt);
	}

	// Reshape the weights
	_reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer);
	}
	}

	if(_is_batched_fc_layer)
	{
	_is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
	input->info()->tensor_shape().cend(),
	output->info()->tensor_shape().cbegin() + 1));

	if(_is_fc_after_conv)
	{
	// Fully Connected layer after a Convolution Layer with batches
	configure_conv_fc_wb(input, weights_to_use, output);
	}
	else
	{
	// Fully Connected layer after a Fully Connected Layer with batches
	configure_fc_fc_wb(input, weights_to_use, output);
	}
	}
	else
	{
	// In case of not batched fully connected layer, the weights will not be reshaped using transposed1xW
	_is_fc_after_conv = ((weights_to_use->info()->dimension(1)) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)));

	if(_is_fc_after_conv)
	{
	// Fully Connected layer after a Convolution Layer without batches
	configure_conv_fc_nb(input, weights_to_use, output);
	}
	else
	{
	// Fully Connected layer after a Fully Connected Layer without batches
	configure_fc_fc_nb(input, weights_to_use, output);
	}
	}

	// Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
	if(!are_weights_reshaped)
	{
	if(transpose_weights \|\| _is_batched_fc_layer)
	{
	// Allocate the tensor for the weights reshaped
	_reshape_weights_output.allocator()->allocate();
	}
	}
	}

	void NEFullyConnectedLayer::run()
	{
	// Reshape of the weights (happens only once)
	if(!_are_weights_reshaped)
	{
	_are_weights_reshaped = true;
	_reshape_weights_kernel.run();
	}

	// Linearize input if comes from a convolutional layer
	if(_is_fc_after_conv)
	{
	NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
	}

	// Interleave input
	if(_is_batched_fc_layer)
	{
	NEScheduler::get().schedule(&_interleave4x4_kernel, Window::DimY);
	}

	// Run matrix multiply
	NEScheduler::get().schedule(&_mm_kernel, _is_batched_fc_layer ? Window::DimY : Window::DimX);

	// Accumulate biases if provided
	if(_accumulate_biases)
	{
	NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
	}
	}