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Diffstat (limited to 'src/graph/nodes/ConvolutionLayer.cpp')
-rw-r--r-- | src/graph/nodes/ConvolutionLayer.cpp | 117 |
1 files changed, 117 insertions, 0 deletions
diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp new file mode 100644 index 0000000000..b80bf93eff --- /dev/null +++ b/src/graph/nodes/ConvolutionLayer.cpp @@ -0,0 +1,117 @@ +/* + * 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/ConvolutionLayer.h" + +#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" +#include "support/ToolchainSupport.h" +#include "utils/TypePrinter.h" + +using namespace arm_compute::graph; + +namespace +{ +template <typename ConvolutionType, typename TensorType, Hint hint> +std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +{ + bool weights_are_loaded = weights.tensor() != nullptr; + bool biases_are_loaded = biases.tensor() != nullptr; + + auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); + conv->configure( + dynamic_cast<TensorType *>(input), + dynamic_cast<TensorType *>(weights.set_target(hint)), + dynamic_cast<TensorType *>(biases.set_target(hint)), + dynamic_cast<TensorType *>(output), + conv_info, weights_info); + 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, const PadStrideInfo &conv_info, const WeightsInfo &weights_info); + +template <> +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +{ + return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info); +} + +template <> +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +{ + return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info); +} +} // namespace + +std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +{ + if(_weights.tensor() == nullptr) + { + _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, input->info()->dimension(2), _ofm), input->info()->num_channels(), input->info()->data_type(), + input->info()->fixed_point_position())); + } + if(_biases.tensor() == nullptr) + { + _biases.set_info(TensorInfo(TensorShape(_ofm), 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, _conv_info, _weights_info); + } + else + { + func = instantiate<Hint::NEON>(input, _weights, _biases, output, _conv_info, _weights_info); + } + + return func; +} + +void ConvolutionLayer::print_info() +{ + if(_hint == Hint::OPENCL) + { + std::cout << "Instantiating CLConvolutionLayer"; + } + else + { + std::cout << "Instantiating NEConvolutionLayer"; + } + 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() << " PadStrideInfo: " << _conv_info << "WeightsInfo: " << _weights_info << std::endl; +} |