| /* |
| * 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/CLConcatenateLayer.h" |
| |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "src/core/CL/kernels/CLDepthConcatenateLayerKernel.h" |
| #include "src/core/CL/kernels/CLHeightConcatenateLayerKernel.h" |
| #include "src/core/CL/kernels/CLWidthConcatenate2TensorsKernel.h" |
| #include "src/core/CL/kernels/CLWidthConcatenate4TensorsKernel.h" |
| #include "src/core/CL/kernels/CLWidthConcatenateLayerKernel.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 "src/core/CL/kernels/CLBatchConcatenateLayerKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| |
| namespace arm_compute |
| { |
| namespace experimental |
| { |
| CLConcatenation::CLConcatenation() |
| : _concat_kernels(), |
| _num_inputs(0), |
| _axis(Window::DimX) |
| { |
| } |
| |
| void CLConcatenation::configure(const CLCompileContext &compile_context, const std::vector<ITensorInfo *> &inputs_vector, ITensorInfo *output, size_t axis) |
| { |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| _axis = axis; |
| _num_inputs = inputs_vector.size(); |
| |
| TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, _axis); |
| std::vector<const ITensorInfo *> const_inputs_vector(inputs_vector.size()); |
| std::transform(inputs_vector.begin(), inputs_vector.end(), const_inputs_vector.begin(), [](ITensorInfo * t) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(t); |
| return t; |
| }); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, output_shape, 1, inputs_vector[0]->data_type()); |
| ARM_COMPUTE_ERROR_THROW_ON(CLConcatenateLayer::validate(const_inputs_vector, output, axis)); |
| |
| unsigned int offset = 0; |
| switch(_axis) |
| { |
| case Window::DimX: |
| { |
| switch(_num_inputs) |
| { |
| case 2: |
| { |
| // Configure WidthConcatenate2Tensors kernel |
| auto kernel = std::make_unique<CLWidthConcatenate2TensorsKernel>(); |
| kernel->configure(compile_context, inputs_vector.at(0), inputs_vector.at(1), output); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| break; |
| } |
| case 4: |
| { |
| // Configure WidthConcatenate4Tensors kernel |
| auto kernel = std::make_unique<CLWidthConcatenate4TensorsKernel>(); |
| kernel->configure(compile_context, 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 = std::make_unique<CLWidthConcatenateLayerKernel>(); |
| kernel->configure(compile_context, inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->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 = std::make_unique<CLHeightConcatenateLayerKernel>(); |
| kernel->configure(compile_context, inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| case Window::DimZ: |
| { |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = std::make_unique<CLDepthConcatenateLayerKernel>(); |
| kernel->configure(compile_context, inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| case 3: |
| { |
| for(unsigned int i = 0; i < _num_inputs; ++i) |
| { |
| auto kernel = std::make_unique<CLBatchConcatenateLayerKernel>(); |
| kernel->configure(compile_context, inputs_vector.at(i), offset, output); |
| offset += inputs_vector.at(i)->dimension(_axis); |
| _concat_kernels.emplace_back(std::move(kernel)); |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Axis not supported"); |
| } |
| } |
| |
| Status CLConcatenation::validate(const std::vector<const ITensorInfo *> &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 CLConcatenation::run(ITensorPack &tensors) |
| { |
| if(tensors.empty()) |
| { |
| ARM_COMPUTE_ERROR("No inputs provided"); |
| } |
| |
| if(static_cast<int>(tensors.size()) - 1 != static_cast<int>(_num_inputs)) |
| { |
| ARM_COMPUTE_ERROR("Configured with different number of inputs"); |
| } |
| |
| if(_axis == Window::DimX && (_num_inputs == 2 || _num_inputs == 4)) |
| { |
| ARM_COMPUTE_ERROR_ON(_concat_kernels.empty()); |
| CLScheduler::get().enqueue_op(*_concat_kernels.at(0), tensors, true); |
| } |
| else |
| { |
| int i = 0; |
| for(auto &k : _concat_kernels) |
| { |
| ITensorPack pack; |
| pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC_VEC + i)); |
| pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_DST)); |
| CLScheduler::get().enqueue_op(*k, pack, true); |
| ++i; |
| } |
| } |
| } |
| } // namespace experimental |
| |
| struct CLConcatenateLayer::Impl |
| { |
| std::vector<const ICLTensor *> srcs{}; |
| ICLTensor *dst{ nullptr }; |
| unsigned int num_inputs{ 0 }; |
| unsigned int axis{ 0 }; |
| std::unique_ptr<experimental::CLConcatenation> op{ nullptr }; |
| }; |
| |
| CLConcatenateLayer::CLConcatenateLayer() |
| : _impl(std::make_unique<Impl>()) |
| { |
| } |
| |
| CLConcatenateLayer::CLConcatenateLayer(CLConcatenateLayer &&) = default; |
| |
| CLConcatenateLayer &CLConcatenateLayer::operator=(CLConcatenateLayer &&) = default; |
| |
| CLConcatenateLayer::~CLConcatenateLayer() = default; |
| |
| void CLConcatenateLayer::configure(std::vector<const ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), inputs_vector, output, axis); |
| } |
| |
| void CLConcatenateLayer::configure(const CLCompileContext &compile_context, std::vector<const ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis) |
| { |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| _impl->srcs = inputs_vector; |
| _impl->dst = output; |
| _impl->axis = axis; |
| _impl->num_inputs = inputs_vector.size(); |
| _impl->op = std::make_unique<experimental::CLConcatenation>(); |
| |
| std::vector<ITensorInfo *> inputs_vector_info; |
| for(unsigned int i = 0; i < inputs_vector.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(inputs_vector.at(i)); |
| inputs_vector_info.emplace_back(inputs_vector.at(i)->info()); |
| } |
| _impl->op->configure(compile_context, inputs_vector_info, _impl->dst->info(), axis); |
| } |
| |
| Status CLConcatenateLayer::validate(const std::vector<const ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis) |
| { |
| return experimental::CLConcatenation::validate(inputs_vector, output, axis); |
| } |
| |
| void CLConcatenateLayer::run() |
| { |
| ITensorPack pack; |
| for(unsigned i = 0; i < _impl->num_inputs; ++i) |
| { |
| pack.add_tensor(TensorType::ACL_SRC_VEC + i, _impl->srcs.at(i)); |
| } |
| pack.add_tensor(TensorType::ACL_DST, _impl->dst); |
| |
| _impl->op->run(pack); |
| } |
| } // namespace arm_compute |