src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp - 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.
  */

 #include "arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h"

 #include "arm_compute/core/PixelValue.h"
 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"

 #include <cmath>
 #include <memory>
 #include <tuple>

 using namespace arm_compute;

 GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights()
     : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
 {
 }

 void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type()));
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
     }

     const bool       append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
     const unsigned   bias_element  = (append_biases) ? 1 : 0;
     const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;

     _transpose1xW = transpose1xW;

     if(transpose1xW)
     {
         // Create tensor to store the reshaped weights
         const unsigned int mat_weights_cols = weights->info()->dimension(3);
         const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
         TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
         const DataType     dt                   = weights->info()->data_type();
         const int          fixed_point_position = weights->info()->fixed_point_position();
         TensorInfo         info_wr(shape_wr, 1, dt, fixed_point_position);

         _weights_reshaped.allocator()->init(info_wr);
         _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
         _weights_transposed_kernel.configure(&_weights_reshaped, output);
         _weights_reshaped.allocator()->allocate();
     }
     else
     {
         _weights_reshape_kernel.configure(weights, biases_to_use, output);
     }
 }

 void GCConvolutionLayerReshapeWeights::run()
 {
     GCScheduler::get().dispatch(_weights_reshape_kernel);
     if(_transpose1xW)
     {
         GCScheduler::get().dispatch(_weights_transposed_kernel);
     }
 }

 GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(),
       _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
       _are_weights_reshaped(false), _is_activationlayer_enabled(false)
 {
 }

 void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed)
 {
     _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
 }

 void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
                                    const Size2D &dilation, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
     }

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

     _append_bias          = (biases != nullptr);
     _are_weights_reshaped = weights_info.are_reshaped();

     const unsigned   bias_element  = (_append_bias) ? 1 : 0;
     const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr;

     // Get parameters from conv_info
     unsigned int stride_x = 0;
     unsigned int stride_y = 0;
     std::tie(stride_x, stride_y) = conv_info.stride();

     // Get convolved dimensions
     unsigned int conv_w = 0;
     unsigned int conv_h = 0;

     const unsigned int kernel_width  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
     const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
     std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
                                                  conv_info, dilation);

     // Check if its a "fully connected" convolution
     _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
     const bool run_interleaved      = (!_is_fully_connected_convolution);

     unsigned int mat_weights_cols = weights->info()->dimension(3);
     unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;

     // Reshape weights if needed
     if(_are_weights_reshaped)
     {
         if(_is_fully_connected_convolution)
         {
             mat_weights_cols = weights->info()->dimension(0);
             mat_weights_rows = weights->info()->dimension(1);
         }
         else
         {
             mat_weights_cols                         = weights_info.num_kernels();
             const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
             mat_weights_rows                         = quarter_reshaped_cols + bias_element;
         }
     }
     else
     {
         if(_is_fully_connected_convolution)
         {
             // Create tensor to store the reshaped weights
             int num_elems_read_per_iteration_x = 1;
             if(dt == DataType::F16)
             {
                 num_elems_read_per_iteration_x = 2;
             }
             TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows);
             _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
             _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
         }
         else
         {
             // Create tensor to store transposed weights
             const float transpose_width = 16.0f / input->info()->element_size();
             TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
             _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
             _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
         }
         weights = &_weights_reshaped;
     }

     // Create tensor to store im2col reshaped inputs
     const unsigned int mat_input_cols = mat_weights_rows;
     const unsigned int mat_input_rows = conv_w * conv_h;
     TensorShape        shape_im2col   = input->info()->tensor_shape();
     shape_im2col.set(0, mat_input_cols);
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);

     // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
     TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
     _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
     _memory_group.manage(&_input_im2col_reshaped);

     // Create tensor (interleave) to prepare input tensor for GEMM
     if(run_interleaved)
     {
         TensorShape shape_interleaved = shape_im2col;
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));

         // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
         TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
         _input_interleaved_reshaped.allocator()->init(interleaved_info);
         _memory_group.manage(&_input_interleaved_reshaped);
     }

     // Create GEMM output tensor
     TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
     const DataType gemm_data_type = dt;

     // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
     TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
     _gemm_output.allocator()->init(info_gemm);
     _memory_group.manage(&_gemm_output);

     // Configure kernels
     if(dt == DataType::F16)
     {
         BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
         input->info()->extend_padding(border_size);
         _fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border
     }
     _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation);

     // Configure matrix multiply
     if(run_interleaved)
     {
         _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
         configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
         _input_interleaved_reshaped.allocator()->allocate();
     }
     else
     {
         configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
     }
     _input_im2col_reshaped.allocator()->allocate();

     // Configure Col2Im
     _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
     _gemm_output.allocator()->allocate();

     ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");

     // Allocate intermediate tensor
     if(!_are_weights_reshaped)
     {
         _weights_reshaped.allocator()->allocate();
     }

     //Configure Activation Layer
     _is_activationlayer_enabled = act_info.enabled();

     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
     }
 }

 void GCConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)
     if(!_are_weights_reshaped)
     {
         _are_weights_reshaped = true;
         _reshape_weights.run();
     }

     _memory_group.acquire();

     // Run im2col
     GCScheduler::get().dispatch(_fill_border);
     GCScheduler::get().memory_barrier();
     GCScheduler::get().dispatch(_input_im2col_kernel);

     if(!_is_fully_connected_convolution)
     {
         GCScheduler::get().memory_barrier();
         // Run interleave4x4
         GCScheduler::get().dispatch(_input_interleave_kernel);
     }

     GCScheduler::get().memory_barrier();
     // Runs matrix multiply on reshaped matrices
     GCScheduler::get().dispatch(_mm_kernel);

     GCScheduler::get().memory_barrier();
     // Reshape output matrix
     GCScheduler::get().dispatch(_output_col2im_kernel, false);

     _memory_group.release();

     GCScheduler::get().memory_barrier();
     // Run Activation Layer
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.run();
     }
 }
	/*
	* 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.
	*/

	#include "arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h"

	#include "arm_compute/core/PixelValue.h"
	#include "arm_compute/core/Size2D.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"

	#include <cmath>
	#include <memory>
	#include <tuple>

	using namespace arm_compute;

	GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights()
	: _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
	{
	}

	void GCConvolutionLayerReshapeWeights::configure(const IGCTensor weights, const IGCTensor biases, IGCTensor *output, bool transpose1xW)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type()));
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
	ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
	}

	const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
	const unsigned bias_element = (append_biases) ? 1 : 0;
	const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;

	_transpose1xW = transpose1xW;

	if(transpose1xW)
	{
	// Create tensor to store the reshaped weights
	const unsigned int mat_weights_cols = weights->info()->dimension(3);
	const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
	TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
	const DataType dt = weights->info()->data_type();
	const int fixed_point_position = weights->info()->fixed_point_position();
	TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position);

	_weights_reshaped.allocator()->init(info_wr);
	_weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
	_weights_transposed_kernel.configure(&_weights_reshaped, output);
	_weights_reshaped.allocator()->allocate();
	}
	else
	{
	_weights_reshape_kernel.configure(weights, biases_to_use, output);
	}
	}

	void GCConvolutionLayerReshapeWeights::run()
	{
	GCScheduler::get().dispatch(_weights_reshape_kernel);
	if(_transpose1xW)
	{
	GCScheduler::get().dispatch(_weights_transposed_kernel);
	}
	}

	GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(),
	_input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
	_are_weights_reshaped(false), _is_activationlayer_enabled(false)
	{
	}

	void GCConvolutionLayer::configure_mm(const IGCTensor input, const IGCTensor weights, IGCTensor *output, bool is_interleaved_transposed)
	{
	_mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
	}

	void GCConvolutionLayer::configure(const IGCTensor input, const IGCTensor weights, const IGCTensor biases, IGCTensor output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
	const Size2D &dilation, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
	ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
	}

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

	_append_bias = (biases != nullptr);
	_are_weights_reshaped = weights_info.are_reshaped();

	const unsigned bias_element = (_append_bias) ? 1 : 0;
	const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr;

	// Get parameters from conv_info
	unsigned int stride_x = 0;
	unsigned int stride_y = 0;
	std::tie(stride_x, stride_y) = conv_info.stride();

	// Get convolved dimensions
	unsigned int conv_w = 0;
	unsigned int conv_h = 0;

	const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
	const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
	std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
	conv_info, dilation);

	// Check if its a "fully connected" convolution
	_is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
	const bool run_interleaved = (!_is_fully_connected_convolution);

	unsigned int mat_weights_cols = weights->info()->dimension(3);
	unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;

	// Reshape weights if needed
	if(_are_weights_reshaped)
	{
	if(_is_fully_connected_convolution)
	{
	mat_weights_cols = weights->info()->dimension(0);
	mat_weights_rows = weights->info()->dimension(1);
	}
	else
	{
	mat_weights_cols = weights_info.num_kernels();
	const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
	mat_weights_rows = quarter_reshaped_cols + bias_element;
	}
	}
	else
	{
	if(_is_fully_connected_convolution)
	{
	// Create tensor to store the reshaped weights
	int num_elems_read_per_iteration_x = 1;
	if(dt == DataType::F16)
	{
	num_elems_read_per_iteration_x = 2;
	}
	TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows);
	_weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
	_reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
	}
	else
	{
	// Create tensor to store transposed weights
	const float transpose_width = 16.0f / input->info()->element_size();
	TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
	_weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
	_reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
	}
	weights = &_weights_reshaped;
	}

	// Create tensor to store im2col reshaped inputs
	const unsigned int mat_input_cols = mat_weights_rows;
	const unsigned int mat_input_rows = conv_w * conv_h;
	TensorShape shape_im2col = input->info()->tensor_shape();
	shape_im2col.set(0, mat_input_cols);
	shape_im2col.set(1, mat_input_rows);
	shape_im2col.set(2, 1);

	// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
	TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
	_input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
	_memory_group.manage(&_input_im2col_reshaped);

	// Create tensor (interleave) to prepare input tensor for GEMM
	if(run_interleaved)
	{
	TensorShape shape_interleaved = shape_im2col;
	shape_interleaved.set(0, shape_interleaved.x() * 4);
	shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));

	// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
	TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
	_input_interleaved_reshaped.allocator()->init(interleaved_info);
	_memory_group.manage(&_input_interleaved_reshaped);
	}

	// Create GEMM output tensor
	TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
	shape_gemm.set(0, mat_weights_cols);
	shape_gemm.set(1, mat_input_rows);
	const DataType gemm_data_type = dt;

	// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
	TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
	_gemm_output.allocator()->init(info_gemm);
	_memory_group.manage(&_gemm_output);

	// Configure kernels
	if(dt == DataType::F16)
	{
	BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
	input->info()->extend_padding(border_size);
	_fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border
	}
	_input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation);

	// Configure matrix multiply
	if(run_interleaved)
	{
	_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
	configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
	_input_interleaved_reshaped.allocator()->allocate();
	}
	else
	{
	configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
	}
	_input_im2col_reshaped.allocator()->allocate();

	// Configure Col2Im
	_output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
	_gemm_output.allocator()->allocate();

	ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) \|\| (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");

	// Allocate intermediate tensor
	if(!_are_weights_reshaped)
	{
	_weights_reshaped.allocator()->allocate();
	}

	//Configure Activation Layer
	_is_activationlayer_enabled = act_info.enabled();

	if(_is_activationlayer_enabled)
	{
	_activationlayer_function.configure(output, nullptr, act_info);
	}
	}

	void GCConvolutionLayer::run()
	{
	// Run weights reshaping (Runs once for every configure)
	if(!_are_weights_reshaped)
	{
	_are_weights_reshaped = true;
	_reshape_weights.run();
	}

	_memory_group.acquire();

	// Run im2col
	GCScheduler::get().dispatch(_fill_border);
	GCScheduler::get().memory_barrier();
	GCScheduler::get().dispatch(_input_im2col_kernel);

	if(!_is_fully_connected_convolution)
	{
	GCScheduler::get().memory_barrier();
	// Run interleave4x4
	GCScheduler::get().dispatch(_input_interleave_kernel);
	}

	GCScheduler::get().memory_barrier();
	// Runs matrix multiply on reshaped matrices
	GCScheduler::get().dispatch(_mm_kernel);

	GCScheduler::get().memory_barrier();
	// Reshape output matrix
	GCScheduler::get().dispatch(_output_col2im_kernel, false);

	_memory_group.release();

	GCScheduler::get().memory_barrier();
	// Run Activation Layer
	if(_is_activationlayer_enabled)
	{
	_activationlayer_function.run();
	}
	}