/* * Copyright (c) 2018-2019 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/NEConcatenateLayer.h" #include "arm_compute/runtime/NEON/functions/NEDepthConcatenateLayer.h" #include "arm_compute/runtime/NEON/functions/NEWidthConcatenateLayer.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "support/ToolchainSupport.h" namespace arm_compute { NEConcatenateLayer::NEConcatenateLayer() : _concat_kernels(), _num_inputs(0), _axis(Window::DimX) { } void NEConcatenateLayer::configure(const std::vector &inputs_vector, ITensor *output, size_t axis) { ARM_COMPUTE_ERROR_ON(output == nullptr); _axis = axis; _num_inputs = inputs_vector.size(); std::vector inputs_vector_info; inputs_vector_info.reserve(_num_inputs); for(unsigned int i = 0; i < _num_inputs; ++i) { ARM_COMPUTE_ERROR_ON_NULLPTR(inputs_vector.at(i)); inputs_vector_info.emplace_back(inputs_vector.at(i)->info()); } TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, _axis); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), output_shape, 1, inputs_vector[0]->info()->data_type()); ARM_COMPUTE_ERROR_THROW_ON(NEConcatenateLayer::validate(inputs_vector_info, output->info(), axis)); unsigned int offset = 0; for(unsigned int i = 0; i < _num_inputs; ++i) { switch(_axis) { case Window::DimX: { auto kernel = support::cpp14::make_unique(); kernel->configure(inputs_vector.at(i), offset, output); _concat_kernels.emplace_back(std::move(kernel)); break; } case Window::DimY: { auto kernel = support::cpp14::make_unique(); kernel->configure(inputs_vector.at(i), offset, output); _concat_kernels.emplace_back(std::move(kernel)); break; } case Window::DimZ: { auto kernel = support::cpp14::make_unique(); kernel->configure(inputs_vector.at(i), offset, output); _concat_kernels.emplace_back(std::move(kernel)); break; } default: ARM_COMPUTE_ERROR("Axis not supported"); } offset += inputs_vector.at(i)->info()->dimension(_axis); } } Status NEConcatenateLayer::validate(const std::vector &inputs_vector, const ITensorInfo *output, size_t axis) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); ARM_COMPUTE_RETURN_ERROR_ON(inputs_vector.size() < 2); unsigned int offset = 0; for(const auto &input : inputs_vector) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); switch(axis) { case Window::DimX: { ARM_COMPUTE_RETURN_ON_ERROR(NEWidthConcatenateLayerKernel::validate(input, offset, output)); break; } case Window::DimY: { ARM_COMPUTE_RETURN_ON_ERROR(NEHeightConcatenateLayerKernel::validate(input, offset, output)); break; } case Window::DimZ: { ARM_COMPUTE_RETURN_ON_ERROR(NEDepthConcatenateLayerKernel::validate(input, offset, output)); break; } default: ARM_COMPUTE_ERROR("Axis not supported"); } offset += input->dimension(axis); } if(output->total_size() != 0) { TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, axis); ARM_COMPUTE_RETURN_ERROR_ON(output_shape.total_size() != output->tensor_shape().total_size()); } return Status{}; } void NEConcatenateLayer::run() { for(auto &kernel : _concat_kernels) { NEScheduler::get().schedule(kernel.get(), _axis); } } } // namespace arm_compute