| /* |
| * Copyright (c) 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, INNEUDING 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 NEAIM, 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/Helpers.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| using namespace arm_compute; |
| |
| NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims() |
| { |
| } |
| |
| Status NEReduceMean::validate(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); |
| ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions()); |
| |
| for(unsigned int i = 0; i < reduction_axis.num_dimensions(); ++i) |
| { |
| if(output->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(reduction_axis[i]) != 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(reduction_axis[i]) > input->num_dimensions() - 1); |
| } |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, reduction_axis[i], ReductionOperation::MEAN_SUM)); |
| } |
| |
| return Status{}; |
| } |
| |
| void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| |
| _reduction_ops = reduction_axis.num_dimensions(); |
| _reduction_kernels = arm_compute::support::cpp14::make_unique<NEReductionOperation[]>(_reduction_ops); |
| _reduced_outs = arm_compute::support::cpp14::make_unique<Tensor[]>(_reduction_ops - (keep_dims ? 1 : 0)); |
| _keep_dims = keep_dims; |
| |
| // Perform reduction for every axis |
| for(unsigned int i = 0; i < _reduction_ops; ++i) |
| { |
| TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (_reduced_outs.get() + i - 1)->info()->tensor_shape(); |
| out_shape.set(reduction_axis[i], 1); |
| auto in = (i == 0) ? input : (_reduced_outs.get() + i - 1); |
| |
| if(i == _reduction_ops - 1 && keep_dims) |
| { |
| _reduction_kernels[i].configure(in, output, reduction_axis[i], ReductionOperation::MEAN_SUM); |
| } |
| else |
| { |
| _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type())); |
| _memory_group.manage(_reduced_outs.get() + i); |
| _reduction_kernels[i].configure(in, _reduced_outs.get() + i, reduction_axis[i], ReductionOperation::MEAN_SUM); |
| } |
| } |
| |
| // Allocate intermediate tensors |
| for(unsigned 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(); |
| for(unsigned int i = 0; i < _reduction_ops; ++i) |
| { |
| out_shape.remove_dimension(reduction_axis[i]); |
| } |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape)); |
| _reshape.configure(_reduced_outs.get() + _reduction_ops - 1, output); |
| } |
| } |
| |
| void NEReduceMean::run() |
| { |
| _memory_group.acquire(); |
| |
| for(unsigned int i = 0; i < _reduction_ops; ++i) |
| { |
| _reduction_kernels[i].run(); |
| } |
| |
| if(!_keep_dims) |
| { |
| _reshape.run(); |
| } |
| _memory_group.release(); |
| } |