src/runtime/NEON/functions/NEReduceMean.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2020 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/NEReduceMean.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "src/core/CPP/Validate.h"
 #include "src/core/NEON/kernels/NEReductionOperationKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"

 namespace arm_compute
 {
 namespace
 {
 Status validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
 {
     ARM_COMPUTE_UNUSED(keep_dims);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1);
     ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());

     const unsigned int reduction_ops = reduction_axis.num_dimensions();
     const int          input_dims    = input->num_dimensions();
     Coordinates        axis_local    = reduction_axis;

     for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i)
     {
         //axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)).
         ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast<int>(input->num_dimensions())));
         ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast<int>(input->num_dimensions()));
     }

     if(output->tensor_shape().total_size() != 0)
     {
         // Only validate if not using auto_init for the output tensor
         TensorShape out_shape = input->tensor_shape();
         // Validate output_shape only if not using auto_init
         convert_negative_axis(axis_local, input_dims);
         std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
         for(unsigned int i = 0; i < reduction_ops; ++i)
         {
             ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
             ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1);
             if(output->total_size() > 0 && keep_dims)
             {
                 ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
             }
             if(keep_dims)
             {
                 out_shape.set(axis_local[i], 1);
             }
             else
             {
                 ARM_COMPUTE_RETURN_ERROR_ON(i > static_cast<unsigned int>(axis_local[i]));
                 const unsigned int remove_index = axis_local[i] - i;
                 ARM_COMPUTE_RETURN_ERROR_ON(remove_index >= out_shape.num_dimensions());
                 out_shape.remove_dimension(remove_index);
             }
         }
         const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
         const bool requant = is_data_type_quantized(input->data_type()) && input->quantization_info() != output->quantization_info();
         if(requant)
         {
             TensorInfo input_no_quant(input->clone()->set_data_type(DataType::F32));
             NEDequantizationLayer::validate(input, &input_no_quant);
             TensorInfo output_no_quant(output->clone()->set_data_type(DataType::F32));
             NEQuantizationLayer::validate(&output_no_quant, output);
         }
     }
     return Status{};
 }
 } // namespace

 NEReduceMean::~NEReduceMean() = default;

 NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _dequant(), _requant(), _reduction_ops(), _keep_dims(), _do_requant(), _input_no_quant(),
       _output_no_quant()
 {
 }

 Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
 {
     return validate_config(input, reduction_axis, keep_dims, output);
 }

 void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info()));
     // Output auto inizialitation if not yet initialized
     const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input->info(), reduction_axis, keep_dims);
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));

     _do_requant    = is_data_type_quantized(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info();
     _reduction_ops = reduction_axis.num_dimensions();
     _reduction_kernels.resize(_reduction_ops);
     _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
     _keep_dims = keep_dims;

     ITensor *tmp_input  = input;
     ITensor *tmp_output = output;
     if(_do_requant)
     {
         _memory_group.manage(&_input_no_quant);
         _memory_group.manage(&_output_no_quant);
         TensorInfo output_no_quant_info = input->info()->clone()->set_tensor_shape(output_shape);
         output_no_quant_info.set_data_type(DataType::F32);
         auto_init_if_empty(*_output_no_quant.info(), output_no_quant_info);
         auto_init_if_empty(*_input_no_quant.info(), input->info()->clone()->set_data_type(DataType::F32));
         _dequant.configure(input, &_input_no_quant);
         tmp_input  = &_input_no_quant;
         tmp_output = &_output_no_quant;
     }

     Coordinates axis_local = reduction_axis;
     const int   input_dims = tmp_input->info()->num_dimensions();

     convert_negative_axis(axis_local, input_dims);

     // Perform reduction for every axis
     for(int i = 0; i < _reduction_ops; ++i)
     {
         TensorShape out_shape = i == 0 ? tmp_input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
         out_shape.set(axis_local[i], 1);
         auto in = (i == 0) ? tmp_input : (&_reduced_outs[i - 1]);

         if(i == _reduction_ops - 1 && keep_dims)
         {
             _reduction_kernels[i].configure(in, tmp_output, axis_local[i], ReductionOperation::MEAN_SUM);
         }
         else
         {
             _reduced_outs[i].allocator()->init(TensorInfo(out_shape, tmp_input->info()->num_channels(), tmp_input->info()->data_type(), tmp_input->info()->quantization_info()));
             _memory_group.manage(&_reduced_outs[i]);
             _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], ReductionOperation::MEAN_SUM);
         }
     }

     // Allocate intermediate tensors
     for(int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
     {
         _reduced_outs[i].allocator()->allocate();
     }

     // Configure reshape layer if we want to drop the dimensions
     if(!keep_dims)
     {
         TensorShape out_shape = tmp_input->info()->tensor_shape();
         // We have to sort the reduction axis vectors in order for remove_dimension
         // to work properly
         std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
         for(int i = 0; i < _reduction_ops; ++i)
         {
             out_shape.remove_dimension(axis_local[i] - i);
         }
         auto_init_if_empty(*tmp_output->info(), tmp_input->info()->clone()->set_tensor_shape(out_shape));
         _reshape.configure(&_reduced_outs[_reduction_ops - 1], tmp_output);
     }
     if(_do_requant)
     {
         _requant.configure(&_output_no_quant, output);
         _input_no_quant.allocator()->allocate();
         _output_no_quant.allocator()->allocate();
     }
 }

 void NEReduceMean::run()
 {
     MemoryGroupResourceScope scope_mg(_memory_group);
     if(_do_requant)
     {
         _dequant.run();
     }
     for(auto &kernel : _reduction_kernels)
     {
         kernel.run();
     }
     if(!_keep_dims)
     {
         _reshape.run();
     }
     if(_do_requant)
     {
         _requant.run();
     }
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2020 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/NEReduceMean.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"
	#include "src/core/CPP/Validate.h"
	#include "src/core/NEON/kernels/NEReductionOperationKernel.h"
	#include "src/core/helpers/AutoConfiguration.h"

	namespace arm_compute
	{
	namespace
	{
	Status validate_config(const ITensorInfo input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo output)
	{
	ARM_COMPUTE_UNUSED(keep_dims);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1);
	ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());

	const unsigned int reduction_ops = reduction_axis.num_dimensions();
	const int input_dims = input->num_dimensions();
	Coordinates axis_local = reduction_axis;

	for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i)
	{
	//axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)).
	ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast<int>(input->num_dimensions())));
	ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast<int>(input->num_dimensions()));
	}

	if(output->tensor_shape().total_size() != 0)
	{
	// Only validate if not using auto_init for the output tensor
	TensorShape out_shape = input->tensor_shape();
	// Validate output_shape only if not using auto_init
	convert_negative_axis(axis_local, input_dims);
	std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
	for(unsigned int i = 0; i < reduction_ops; ++i)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
	ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1);
	if(output->total_size() > 0 && keep_dims)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
	}
	if(keep_dims)
	{
	out_shape.set(axis_local[i], 1);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON(i > static_cast<unsigned int>(axis_local[i]));
	const unsigned int remove_index = axis_local[i] - i;
	ARM_COMPUTE_RETURN_ERROR_ON(remove_index >= out_shape.num_dimensions());
	out_shape.remove_dimension(remove_index);
	}
	}
	const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
	const bool requant = is_data_type_quantized(input->data_type()) && input->quantization_info() != output->quantization_info();
	if(requant)
	{
	TensorInfo input_no_quant(input->clone()->set_data_type(DataType::F32));
	NEDequantizationLayer::validate(input, &input_no_quant);
	TensorInfo output_no_quant(output->clone()->set_data_type(DataType::F32));
	NEQuantizationLayer::validate(&output_no_quant, output);
	}
	}
	return Status{};
	}
	} // namespace

	NEReduceMean::~NEReduceMean() = default;

	NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _dequant(), _requant(), _reduction_ops(), _keep_dims(), _do_requant(), _input_no_quant(),
	_output_no_quant()
	{
	}

	Status NEReduceMean::validate(const ITensorInfo input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo output)
	{
	return validate_config(input, reduction_axis, keep_dims, output);
	}

	void NEReduceMean::configure(ITensor input, const Coordinates &reduction_axis, bool keep_dims, ITensor output)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info()));
	// Output auto inizialitation if not yet initialized
	const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input->info(), reduction_axis, keep_dims);
	auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));

	_do_requant = is_data_type_quantized(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info();
	_reduction_ops = reduction_axis.num_dimensions();
	_reduction_kernels.resize(_reduction_ops);
	_reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
	_keep_dims = keep_dims;

	ITensor *tmp_input = input;
	ITensor *tmp_output = output;
	if(_do_requant)
	{
	_memory_group.manage(&_input_no_quant);
	_memory_group.manage(&_output_no_quant);
	TensorInfo output_no_quant_info = input->info()->clone()->set_tensor_shape(output_shape);
	output_no_quant_info.set_data_type(DataType::F32);
	auto_init_if_empty(*_output_no_quant.info(), output_no_quant_info);
	auto_init_if_empty(*_input_no_quant.info(), input->info()->clone()->set_data_type(DataType::F32));
	_dequant.configure(input, &_input_no_quant);
	tmp_input = &_input_no_quant;
	tmp_output = &_output_no_quant;
	}

	Coordinates axis_local = reduction_axis;
	const int input_dims = tmp_input->info()->num_dimensions();

	convert_negative_axis(axis_local, input_dims);

	// Perform reduction for every axis
	for(int i = 0; i < _reduction_ops; ++i)
	{
	TensorShape out_shape = i == 0 ? tmp_input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
	out_shape.set(axis_local[i], 1);
	auto in = (i == 0) ? tmp_input : (&_reduced_outs[i - 1]);

	if(i == _reduction_ops - 1 && keep_dims)
	{
	_reduction_kernels[i].configure(in, tmp_output, axis_local[i], ReductionOperation::MEAN_SUM);
	}
	else
	{
	_reduced_outs[i].allocator()->init(TensorInfo(out_shape, tmp_input->info()->num_channels(), tmp_input->info()->data_type(), tmp_input->info()->quantization_info()));
	_memory_group.manage(&_reduced_outs[i]);
	_reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], ReductionOperation::MEAN_SUM);
	}
	}

	// Allocate intermediate tensors
	for(int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
	{
	_reduced_outs[i].allocator()->allocate();
	}

	// Configure reshape layer if we want to drop the dimensions
	if(!keep_dims)
	{
	TensorShape out_shape = tmp_input->info()->tensor_shape();
	// We have to sort the reduction axis vectors in order for remove_dimension
	// to work properly
	std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
	for(int i = 0; i < _reduction_ops; ++i)
	{
	out_shape.remove_dimension(axis_local[i] - i);
	}
	auto_init_if_empty(*tmp_output->info(), tmp_input->info()->clone()->set_tensor_shape(out_shape));
	_reshape.configure(&_reduced_outs[_reduction_ops - 1], tmp_output);
	}
	if(_do_requant)
	{
	_requant.configure(&_output_no_quant, output);
	_input_no_quant.allocator()->allocate();
	_output_no_quant.allocator()->allocate();
	}
	}

	void NEReduceMean::run()
	{
	MemoryGroupResourceScope scope_mg(_memory_group);
	if(_do_requant)
	{
	_dequant.run();
	}
	for(auto &kernel : _reduction_kernels)
	{
	kernel.run();
	}
	if(!_keep_dims)
	{
	_reshape.run();
	}
	if(_do_requant)
	{
	_requant.run();
	}
	}
	} // namespace arm_compute