| /* |
| * 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/NEON/functions/NEReduceMean.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| } // namespace |
| |
| NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims() |
| { |
| } |
| |
| Status validate_config(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, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1); |
| ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions()); |
| |
| const unsigned int reduction_ops = reduction_axis.num_dimensions(); |
| const int input_dims = input->num_dimensions(); |
| Coordinates axis_local = reduction_axis; |
| |
| for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i) |
| { |
| //axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)). |
| ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast<int>(input->num_dimensions()))); |
| ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast<int>(input->num_dimensions())); |
| } |
| |
| if(output->tensor_shape().total_size() != 0) |
| { |
| // Only validate if not using auto_init for the output tensor |
| TensorShape out_shape = input->tensor_shape(); |
| // Validate output_shape only if not using auto_init |
| convert_negative_axis(axis_local, input_dims); |
| std::sort(axis_local.begin(), axis_local.begin() + reduction_ops); |
| for(unsigned int i = 0; i < reduction_ops; ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3); |
| ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1); |
| if(output->total_size() > 0 && keep_dims) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1); |
| } |
| if(keep_dims) |
| { |
| out_shape.set(axis_local[i], 1); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(i > static_cast<unsigned int>(axis_local[i])); |
| const unsigned int remove_index = axis_local[i] - i; |
| ARM_COMPUTE_RETURN_ERROR_ON(remove_index >= out_shape.num_dimensions()); |
| out_shape.remove_dimension(remove_index); |
| } |
| } |
| const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); |
| } |
| return Status{}; |
| } |
| |
| Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output) |
| { |
| return validate_config(input, reduction_axis, keep_dims, output); |
| } |
| |
| void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output) |
| { |
| // Perform validate step |
| ARM_COMPUTE_ERROR_THROW_ON(NEReduceMean::validate(input->info(), reduction_axis, keep_dims, output->info())); |
| // Output auto inizialitation if not yet initialized |
| const TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_reduce_mean_shape(input, reduction_axis, keep_dims); |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); |
| |
| _reduction_ops = reduction_axis.num_dimensions(); |
| _reduction_kernels.resize(_reduction_ops); |
| _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0)); |
| _keep_dims = keep_dims; |
| |
| Coordinates axis_local = reduction_axis; |
| const int input_dims = input->info()->num_dimensions(); |
| |
| convert_negative_axis(axis_local, input_dims); |
| |
| // Perform reduction for every axis |
| for(int i = 0; i < _reduction_ops; ++i) |
| { |
| TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape(); |
| out_shape.set(axis_local[i], 1); |
| auto in = (i == 0) ? input : (&_reduced_outs[i - 1]); |
| |
| if(i == _reduction_ops - 1 && keep_dims) |
| { |
| _reduction_kernels[i].configure(in, output, axis_local[i], ReductionOperation::MEAN_SUM); |
| } |
| else |
| { |
| _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type(), input->info()->quantization_info())); |
| _memory_group.manage(&_reduced_outs[i]); |
| _reduction_kernels[i].configure(in, &_reduced_outs[i], axis_local[i], ReductionOperation::MEAN_SUM); |
| } |
| } |
| |
| // Allocate intermediate tensors |
| for(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(); |
| // We have to sort the reduction axis vectors in order for remove_dimension |
| // to work properly |
| std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops); |
| for(int i = 0; i < _reduction_ops; ++i) |
| { |
| out_shape.remove_dimension(axis_local[i] - i); |
| } |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape)); |
| _reshape.configure(&_reduced_outs[_reduction_ops - 1], output); |
| } |
| } |
| |
| void NEReduceMean::run() |
| { |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| for(auto &kernel : _reduction_kernels) |
| { |
| kernel.run(); |
| } |
| |
| if(!_keep_dims) |
| { |
| _reshape.run(); |
| } |
| } |
| } // namespace arm_compute |