| /* |
| * 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/CLArgMinMaxLayer.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/CL/kernels/CLArgMinMaxLayerKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/runtime/Utils.h" |
| |
| namespace arm_compute |
| { |
| CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _results_vector(), _not_reshaped_output(), _reduction_kernels_vector(), _reshape(), _num_of_stages(), _reduction_axis() |
| { |
| } |
| |
| CLArgMinMaxLayer::~CLArgMinMaxLayer() = default; |
| |
| Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid reduction operation"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= static_cast<int>(TensorShape::num_max_dimensions), "Reduction axis greater than max number of dimensions"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); |
| const unsigned int num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->dimension(0), axis); |
| |
| DataType output_data_type = DataType::S32; |
| TensorInfo not_reshaped_output; |
| const auto input_num_channles = input->num_channels(); |
| const auto input_qinfo = input->quantization_info(); |
| |
| if(output->total_size() != 0) |
| { |
| output_data_type = output->data_type(); |
| const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, false)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); |
| } |
| |
| auto shape_before_reshape = input->tensor_shape(); |
| shape_before_reshape.set(axis, 1); |
| auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) |
| { |
| ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo); |
| }; |
| |
| initialize_tensorinfo(not_reshaped_output, shape_before_reshape, output_data_type, input_num_channles, input_qinfo); |
| |
| if(num_of_stages == 1) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, ¬_reshaped_output, axis, op)); |
| } |
| else |
| { |
| // Create temporary tensor infos |
| std::vector<TensorInfo> sums_vector(num_of_stages - 1); |
| |
| // Create intermediate tensor info |
| TensorShape shape{ input->tensor_shape() }; |
| |
| for(unsigned int i = 0; i < num_of_stages - 1; i++) |
| { |
| shape.set(0, ceil(shape.x() / 128.f)); |
| sums_vector[i].set_data_type(input->data_type()); |
| sums_vector[i].set_tensor_shape(shape); |
| sums_vector[i].set_num_channels(input->num_channels()); |
| } |
| |
| // Validate ReductionOperation only on first kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &sums_vector[0], axis, op)); |
| |
| // Validate ReductionOperation on intermediate stages |
| for(unsigned int i = 1; i < num_of_stages - 1; ++i) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[i - 1], &sums_vector[i], axis, op)); |
| } |
| |
| // Validate ReductionOperation on the last stage |
| const unsigned int last_stage = num_of_stages - 1; |
| ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[last_stage - 1], ¬_reshaped_output, axis, op)); |
| } |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(¬_reshaped_output, output)); |
| return Status{}; |
| } |
| |
| void CLArgMinMaxLayer::configure(const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, axis, output, op); |
| } |
| |
| void CLArgMinMaxLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| _num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis); |
| _reduction_axis = axis; |
| |
| const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); |
| DataType output_data_type = (output->info()->data_type() == DataType::UNKNOWN) ? DataType::S32 : output->info()->data_type(); |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); |
| |
| // Configure reduction operation kernels |
| _reduction_kernels_vector.reserve(_num_of_stages); |
| |
| auto add_reduction_kernel = [this, &compile_context, axis, op](const ICLTensor * input, const ICLTensor * prev_output, ICLTensor * output) |
| { |
| _reduction_kernels_vector.emplace_back(std::make_unique<CLArgMinMaxLayerKernel>()); |
| _reduction_kernels_vector.back()->configure(compile_context, input, prev_output, output, axis, op); |
| }; |
| |
| _memory_group.manage(&_not_reshaped_output); |
| // Create temporary tensors |
| if(_num_of_stages == 1) |
| { |
| add_reduction_kernel(input, nullptr, &_not_reshaped_output); |
| } |
| else |
| { |
| _results_vector.resize(_num_of_stages - 1); |
| TensorShape shape{ input->info()->tensor_shape() }; |
| for(unsigned int i = 0; i < _num_of_stages - 1; i++) |
| { |
| shape.set(0, ceil(shape.x() / 128.f)); |
| _results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape).set_data_type(output_data_type)); |
| } |
| |
| // Apply ReductionOperation only on first kernel |
| _memory_group.manage(&_results_vector[0]); |
| add_reduction_kernel(input, nullptr, &_results_vector[0]); |
| |
| // Apply ReductionOperation on intermediate stages |
| for(unsigned int i = 1; i < _num_of_stages - 1; ++i) |
| { |
| _memory_group.manage(&_results_vector[i]); |
| add_reduction_kernel(input, &_results_vector[i - 1], &_results_vector[i]); |
| _results_vector[i - 1].allocator()->allocate(); |
| } |
| |
| // Apply ReductionOperation on the last stage |
| const unsigned int last_stage = _num_of_stages - 1; |
| add_reduction_kernel(input, &_results_vector[last_stage - 1], &_not_reshaped_output); |
| _results_vector[last_stage - 1].allocator()->allocate(); |
| } |
| _reshape.configure(compile_context, &_not_reshaped_output, output); |
| _not_reshaped_output.allocator()->allocate(); |
| } |
| |
| void CLArgMinMaxLayer::run() |
| { |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| for(unsigned int i = 0; i < _num_of_stages; ++i) |
| { |
| CLScheduler::get().enqueue(*_reduction_kernels_vector[i], false); |
| } |
| _reshape.run(); |
| } |
| } // namespace arm_compute |