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Diffstat (limited to 'src/runtime/NEON/functions/NEReduceMean.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEReduceMean.cpp | 117 |
1 files changed, 117 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp new file mode 100644 index 0000000000..0b022df729 --- /dev/null +++ b/src/runtime/NEON/functions/NEReduceMean.cpp @@ -0,0 +1,117 @@ +/* + * 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(); +} |