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/*
* 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/CL/functions/CLReduceMean.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/kernels/CLReductionOperationKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.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_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, 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);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
}
return Status{};
}
}
CLReduceMean::CLReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims()
{
}
void CLReduceMean::configure(ICLTensor *input, const Coordinates &reduction_axis, bool keep_dims, ICLTensor *output)
{
configure(CLKernelLibrary::get().get_compile_context(), input, reduction_axis, keep_dims, output);
}
void CLReduceMean::configure(const CLCompileContext &compile_context, ICLTensor *input, const Coordinates &reduction_axis, bool keep_dims, ICLTensor *output)
{
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(CLReduceMean::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, reduction_axis, keep_dims);
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
_reduction_ops = reduction_axis.num_dimensions();
_reduction_kernels.resize(_reduction_ops);
_reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0));
_keep_dims = keep_dims;
Coordinates axis_local = reduction_axis;
const int input_dims = 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 ? input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape();
out_shape.set(axis_local[i], 1);
auto in = (i == 0) ? input : (&_reduced_outs[i - 1]);
if(i == _reduction_ops - 1 && keep_dims)
{
_reduction_kernels[i].configure(compile_context, in, output, axis_local[i], ReductionOperation::MEAN_SUM);
}
else
{
_reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type(), input->info()->quantization_info()));
_memory_group.manage(&_reduced_outs[i]);
_reduction_kernels[i].configure(compile_context, 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 = 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(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
_reshape.configure(compile_context, &_reduced_outs[_reduction_ops - 1], output);
}
}
Status CLReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output)
{
return validate_config(input, reduction_axis, keep_dims, output);
}
void CLReduceMean::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
for(auto &kernel : _reduction_kernels)
{
kernel.run();
}
if(!_keep_dims)
{
_reshape.run();
}
}
} // namespace arm_compute
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