| /* |
| * 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/CL/functions/CLConcatenateLayer.h" |
| |
| #include "arm_compute/core/CL/kernels/CLBatchConcatenateLayerKernel.h" |
| #include "arm_compute/core/CL/kernels/CLDepthConcatenateLayerKernel.h" |
| #include "arm_compute/core/CL/kernels/CLHeightConcatenateLayerKernel.h" |
| #include "arm_compute/core/CL/kernels/CLWidthConcatenate2TensorsKernel.h" |
| #include "arm_compute/core/CL/kernels/CLWidthConcatenate4TensorsKernel.h" |
| #include "arm_compute/core/CL/kernels/CLWidthConcatenateLayerKernel.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| CLConcatenateLayer::CLConcatenateLayer() |
| : _concat_kernels(), |
| _num_inputs(0), |
| _axis(Window::DimX) |
| { |
| } |
| |
| void CLConcatenateLayer::configure(std::vector<ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis) |
| { |
| configure_internal(std::move(inputs_vector), output, axis); |
| } |
| |
| void CLConcatenateLayer::configure(std::vector<const ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis) |
| { |
| configure_internal(std::move(inputs_vector), output, axis); |
| } |
| |
| Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis) |
| { |
| return validate_internal(inputs_vector, output, axis); |
| } |
| |
| Status CLConcatenateLayer::validate(const std::vector<const ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis) |
| { |
| return validate_internal(inputs_vector, output, axis); |
| } |
| |
| template <typename TensorType> |
| void CLConcatenateLayer::configure_internal(std::vector<TensorType *> &&inputs_vector, ICLTensor *output, size_t axis) |
| { |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| _axis = axis; |
| _num_inputs = inputs_vector.size(); |
| |
| std::vector<ITensorInfo *> inputs_vector_info(inputs_vector.size()); |
| std::transform(inputs_vector.begin(), inputs_vector.end(), inputs_vector_info.begin(), [](TensorType * t) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(t); |
| return t->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(CLConcatenateLayer::validate(inputs_vector_info, output->info(), axis)); |
| |
| unsigned int offset = 0; |
| switch(_axis) |
| { |
| case Window::DimX: |
| { |
| switch(_num_inputs) |
| { |
| case 2: |
| { |
| // Configure WidthConcatenate2Tensors kernel |
| auto kernel = support::cpp14::make_unique<CLWidthConcatenate2TensorsKernel>(); |
| kernel->configure(inputs_vector.at(0), inputs_vector.at(1), output); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| break; |
| } |
| case 4: |
| { |
| // Configure WidthConcatenate4Tensors kernel |
| auto kernel = support::cpp14::make_unique<CLWidthConcatenate4TensorsKernel>(); |
| kernel->configure(inputs_vector.at(0), inputs_vector.at(1), inputs_vector.at(2), inputs_vector.at(3), output); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| break; |
| } |
| default: |
| { |
| // Configure generic case WidthConcatenate kernels |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = support::cpp14::make_unique<CLWidthConcatenateLayerKernel>(); |
| kernel->configure(inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->info()->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| } |
| break; |
| } |
| case Window::DimY: |
| { |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = support::cpp14::make_unique<CLHeightConcatenateLayerKernel>(); |
| kernel->configure(inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->info()->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| case Window::DimZ: |
| { |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = support::cpp14::make_unique<CLDepthConcatenateLayerKernel>(); |
| kernel->configure(inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->info()->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| case 3: |
| { |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = support::cpp14::make_unique<CLBatchConcatenateLayerKernel>(); |
| kernel->configure(inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->info()->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Axis not supported"); |
| } |
| } |
| |
| template <typename TensorInfoType> |
| Status CLConcatenateLayer::validate_internal(const std::vector<TensorInfoType *> &inputs_vector, const ITensorInfo *output, size_t axis) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(output == nullptr); |
| const unsigned int num_inputs = inputs_vector.size(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_RETURN_ERROR_ON(num_inputs < 2); |
| |
| unsigned int offset = 0; |
| switch(axis) |
| { |
| case Window::DimX: |
| { |
| switch(num_inputs) |
| { |
| case 2: |
| // Validate WidthConcatenate2Tensors kernels if there are 2 inputs |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(inputs_vector[0], inputs_vector[1]); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate2TensorsKernel::validate(inputs_vector[0], inputs_vector[1], output)); |
| break; |
| case 4: |
| // Validate WidthConcatenate4Tensors kernels if there are 4 inputs |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(inputs_vector[0], inputs_vector[1], inputs_vector[2], inputs_vector[3]); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenate4TensorsKernel::validate(inputs_vector[0], inputs_vector[1], inputs_vector[2], inputs_vector[3], output)); |
| break; |
| default: |
| // Validate generic case of WidthConcatenate kernel |
| for(const auto &input : inputs_vector) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayerKernel::validate(input, offset, output)); |
| offset += input->dimension(axis); |
| } |
| break; |
| } |
| break; |
| } |
| case Window::DimY: |
| { |
| for(const auto &input : inputs_vector) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLHeightConcatenateLayerKernel::validate(input, offset, output)); |
| offset += input->dimension(axis); |
| } |
| break; |
| } |
| case Window::DimZ: |
| { |
| for(const auto &input : inputs_vector) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLDepthConcatenateLayerKernel::validate(input, offset, output)); |
| offset += input->dimension(axis); |
| } |
| break; |
| } |
| case 3: |
| { |
| for(const auto &input : inputs_vector) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLBatchConcatenateLayerKernel::validate(input, offset, output)); |
| offset += input->dimension(axis); |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Axis not supported"); |
| } |
| |
| 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 CLConcatenateLayer::run() |
| { |
| for(auto &kernel : _concat_kernels) |
| { |
| CLScheduler::get().enqueue(*kernel, true); |
| } |
| } |
| } // namespace arm_compute |