| /* |
| * Copyright (c) 2017 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/graph/nodes/FullyConnectedLayer.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Logger.h" |
| #include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h" |
| #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" |
| #include "support/ToolchainSupport.h" |
| #include "utils/TypePrinter.h" |
| |
| using namespace arm_compute::graph; |
| |
| namespace |
| { |
| TensorShape calculate_fullyconnected_layer_output_shape(const TensorShape &input_shape, unsigned int output_neurons) |
| { |
| // Note: Only 1D batch space is supported at the moment |
| unsigned int batches = input_shape[1]; |
| if(input_shape.num_dimensions() > 2) |
| { |
| batches = input_shape[3]; |
| } |
| return TensorShape(output_neurons, batches); |
| } |
| template <typename FullyConnectedType, typename TensorType, TargetHint target_hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) |
| { |
| bool weights_are_loaded = weights.tensor() != nullptr; |
| bool biases_are_loaded = biases.tensor() != nullptr; |
| |
| auto conv = arm_compute::support::cpp14::make_unique<FullyConnectedType>(); |
| conv->configure( |
| dynamic_cast<TensorType *>(input), |
| dynamic_cast<TensorType *>(weights.set_target(target_hint)), |
| dynamic_cast<TensorType *>(biases.set_target(target_hint)), |
| dynamic_cast<TensorType *>(output)); |
| if(!weights_are_loaded) |
| { |
| weights.allocate_and_fill_if_needed(); |
| } |
| if(!biases_are_loaded) |
| { |
| biases.allocate_and_fill_if_needed(); |
| } |
| |
| return std::move(conv); |
| } |
| |
| template <TargetHint target_hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output); |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) |
| { |
| return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, weights, biases, output); |
| } |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) |
| { |
| return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, TargetHint::NEON>(input, weights, biases, output); |
| } |
| } // namespace |
| |
| std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) |
| { |
| if(_weights.tensor() == nullptr) |
| { |
| unsigned int num_weights = 1; |
| unsigned int num_dimensions = input->info()->num_dimensions(); |
| // Ignore the batch dimension if there is one: |
| if(num_dimensions == 2 || num_dimensions == 4) |
| { |
| num_dimensions--; |
| } |
| for(unsigned int i = 0; i < num_dimensions; i++) |
| { |
| num_weights *= input->info()->dimension(i); |
| } |
| _weights.set_info(TensorInfo(TensorShape(num_weights, _num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); |
| } |
| if(_biases.tensor() == nullptr) |
| { |
| _biases.set_info(TensorInfo(TensorShape(_num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); |
| } |
| |
| // Auto configure output |
| arm_compute::auto_init_if_empty(*output->info(), |
| calculate_fullyconnected_layer_output_shape(input->info()->tensor_shape(), _num_neurons), |
| input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()); |
| |
| std::unique_ptr<arm_compute::IFunction> func; |
| _target_hint = ctx.hints().target_hint(); |
| |
| if(_target_hint == TargetHint::OPENCL) |
| { |
| func = instantiate<TargetHint::OPENCL>(input, _weights, _biases, output); |
| ARM_COMPUTE_LOG("Instantiating CLFullyConnectedLayer"); |
| } |
| else |
| { |
| func = instantiate<TargetHint::NEON>(input, _weights, _biases, output); |
| ARM_COMPUTE_LOG("Instantiating NEFullyConnectedLayer"); |
| } |
| |
| ARM_COMPUTE_LOG(" Type: " << input->info()->data_type() |
| << " Input Shape: " << input->info()->tensor_shape() |
| << " Weights shape: " << _weights.info().tensor_shape() |
| << " Biases Shape: " << _biases.info().tensor_shape() |
| << " Output Shape: " << output->info()->tensor_shape() |
| << std::endl); |
| |
| return func; |
| } |