| /* |
| * 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/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 |
| { |
| template <typename FullyConnectedType, typename TensorType, Hint 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(hint)), |
| dynamic_cast<TensorType *>(biases.set_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 <Hint hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output); |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) |
| { |
| return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output); |
| } |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) |
| { |
| return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output); |
| } |
| } // namespace |
| |
| std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hint hint, 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())); |
| } |
| |
| arm_compute::auto_init_if_empty(*output->info(), TensorShape(_num_neurons, input->info()->dimension(1)), input->info()->num_channels(), input->info()->data_type(), |
| input->info()->fixed_point_position()); |
| |
| std::unique_ptr<arm_compute::IFunction> func; |
| _hint = hint; |
| _input = input; |
| _output = output; |
| |
| if(_hint == Hint::OPENCL) |
| { |
| func = instantiate<Hint::OPENCL>(input, _weights, _biases, output); |
| } |
| else |
| { |
| func = instantiate<Hint::NEON>(input, _weights, _biases, output); |
| } |
| |
| return func; |
| } |
| |
| void FullyConnectedLayer::print_info() |
| { |
| if(_hint == Hint::OPENCL) |
| { |
| std::cout << "Instantiating CLFullyConnectedLayer"; |
| } |
| else |
| { |
| std::cout << "Instantiating NEFullyConnectedLayer"; |
| } |
| std::cout << " 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; |
| } |