diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2017-09-26 12:32:57 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | 6f669f039fb74675b858bc3703295609a6a3e122 (patch) | |
tree | 704847bbebb2439f68309680bd4f4142b876c179 /src | |
parent | 1682430e220eb609752c650f85c0f96e375b6d6a (diff) | |
download | ComputeLibrary-6f669f039fb74675b858bc3703295609a6a3e122.tar.gz |
COMPMID-417: Add grouping in convolution layer
-Adds grouping support in convolution layer
-Adds Normalization layer node in graph
-Adds alexnet example
-Fixes FullyConnectedLayer output autoconfigure (works only for 1d batch
space)
Change-Id: I5bd75f9a8b08cfd68f7c34745150266c2bc4221f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/89518
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src')
-rw-r--r-- | src/graph/SubTensor.cpp | 105 | ||||
-rw-r--r-- | src/graph/nodes/ConvolutionLayer.cpp | 263 | ||||
-rw-r--r-- | src/graph/nodes/FullyConnectedLayer.cpp | 24 | ||||
-rw-r--r-- | src/graph/nodes/NormalizationLayer.cpp | 105 |
4 files changed, 468 insertions, 29 deletions
diff --git a/src/graph/SubTensor.cpp b/src/graph/SubTensor.cpp new file mode 100644 index 0000000000..a70f32927b --- /dev/null +++ b/src/graph/SubTensor.cpp @@ -0,0 +1,105 @@ +/* + * 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/SubTensor.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/CL/CLSubTensor.h" +#include "arm_compute/runtime/SubTensor.h" +#include "utils/TypePrinter.h" + +using namespace arm_compute::graph; + +namespace +{ +template <typename SubTensorType, typename ParentTensorType> +std::unique_ptr<ITensor> initialise_subtensor(ITensor *parent, TensorShape shape, Coordinates coords) +{ + auto ptensor = dynamic_cast<ParentTensorType *>(parent); + auto subtensor = arm_compute::support::cpp14::make_unique<SubTensorType>(ptensor, shape, coords); + return std::move(subtensor); +} +} // namespace + +SubTensor::SubTensor() + : _target(Hint::DONT_CARE), _coords(), _info(), _parent(nullptr), _subtensor(nullptr) +{ +} + +SubTensor::SubTensor(Tensor &parent, TensorShape tensor_shape, Coordinates coords) + : _target(Hint::DONT_CARE), _coords(coords), _info(), _parent(nullptr), _subtensor(nullptr) +{ + ARM_COMPUTE_ERROR_ON(parent.tensor() == nullptr); + _parent = parent.tensor(); + _info = SubTensorInfo(parent.tensor()->info(), tensor_shape, coords); + _target = parent.target(); + + instantiate_subtensor(); +} + +SubTensor::SubTensor(ITensor *parent, TensorShape tensor_shape, Coordinates coords, Hint target) + : _target(target), _coords(coords), _info(), _parent(parent), _subtensor(nullptr) +{ + ARM_COMPUTE_ERROR_ON(parent == nullptr); + _info = SubTensorInfo(parent->info(), tensor_shape, coords); + + instantiate_subtensor(); +} + +void SubTensor::set_info(SubTensorInfo &&info) +{ + _info = info; +} + +const SubTensorInfo &SubTensor::info() const +{ + return _info; +} + +ITensor *SubTensor::tensor() +{ + return _subtensor.get(); +} + +Hint SubTensor::target() const +{ + return _target; +} + +void SubTensor::instantiate_subtensor() +{ + switch(_target) + { + case Hint::OPENCL: + _subtensor = initialise_subtensor<arm_compute::CLSubTensor, arm_compute::ICLTensor>(_parent, _info.tensor_shape(), _coords); + break; + case Hint::NEON: + _subtensor = initialise_subtensor<arm_compute::SubTensor, arm_compute::ITensor>(_parent, _info.tensor_shape(), _coords); + break; + default: + ARM_COMPUTE_ERROR("Invalid Hint"); + } +} diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp index b80bf93eff..ce9f096719 100644 --- a/src/graph/nodes/ConvolutionLayer.cpp +++ b/src/graph/nodes/ConvolutionLayer.cpp @@ -24,60 +24,155 @@ #include "arm_compute/graph/nodes/ConvolutionLayer.h" #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" +#include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" +#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" #include "support/ToolchainSupport.h" +#include "utils/GraphTypePrinter.h" #include "utils/TypePrinter.h" +#include <tuple> +#include <vector> + 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) +/** Calculates the output shaped of the convolution layer + * + * @param[in] input_shape Input tensor shape + * @param[in] weights_shape Weights shape + * @param[in] conv_info Convolution information (padding, stride, etc.) + * + * @return The expected output tensor shape + */ +TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_shape, const TensorShape &weights_shape, const PadStrideInfo &conv_info) { - bool weights_are_loaded = weights.tensor() != nullptr; - bool biases_are_loaded = biases.tensor() != nullptr; + unsigned int output_width = 0; + unsigned int output_height = 0; + + // Get output width and height + std::tie(output_width, output_height) = arm_compute::scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info); + // Create output shape + TensorShape output_shape = input_shape; + output_shape.set(0, output_width); + output_shape.set(1, output_height); + output_shape.set(2, weights_shape[3]); + + return output_shape; +} + +// Instantiate GEMM based convolution layer +template <typename ConvolutionType, typename TensorType, Hint hint> +std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +{ 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 *>(weights), + dynamic_cast<TensorType *>(biases), 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); +} +// Instantiate direct convolution layer +template <typename ConvolutionType, typename TensorType, Hint hint> +std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) +{ + auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); + conv->configure( + dynamic_cast<TensorType *>(input), + dynamic_cast<TensorType *>(weights), + dynamic_cast<TensorType *>(biases), + dynamic_cast<TensorType *>(output), + conv_info); 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); +std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, + ConvolutionMethodHint conv_method); 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) +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, + ConvolutionMethodHint conv_method) { - return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info); + if(conv_method == ConvolutionMethodHint::GEMM) + { + return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info); + } + else + { + return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_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) +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, + ConvolutionMethodHint conv_method) { - return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info); + if(conv_method == ConvolutionMethodHint::GEMM) + { + return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info); + } + else + { + return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info); + } } } // namespace +/** Grouped Convolution function */ +class GroupedConvolutionFunction final : public arm_compute::IFunction +{ +public: + /** Default Constructor */ + GroupedConvolutionFunction() + : _convolutions() + { + } + /** Default Destructor */ + ~GroupedConvolutionFunction() final = default; + /** Prevent instances from being copy constructed */ + GroupedConvolutionFunction(const GroupedConvolutionFunction &) = delete; + /** Prevent instances from being copy assigned */ + GroupedConvolutionFunction &operator=(const GroupedConvolutionFunction &) = delete; + /** Allow instances to be move constructed */ + GroupedConvolutionFunction(GroupedConvolutionFunction &&) noexcept = default; + /** Allow instances to be move assigned */ + GroupedConvolutionFunction &operator=(GroupedConvolutionFunction &&) noexcept = default; + /** Adds a convolution + * + * @param convolution Convolution function to add + */ + void add_convolution_function(std::unique_ptr<IFunction> convolution) + { + _convolutions.emplace_back(std::move(convolution)); + } + + // Inherited methods overriden: + void run() override + { + for(auto &c : _convolutions) + { + c->run(); + } + } + +private: + std::vector<std::unique_ptr<IFunction>> _convolutions; +}; + std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) { + // Set weights and biases info 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(), + _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, input->info()->dimension(2) / _num_groups, _ofm), + input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); } if(_biases.tensor() == nullptr) @@ -90,13 +185,40 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint _input = input; _output = output; - if(_hint == Hint::OPENCL) + // Check if the weights and biases are loaded + bool weights_are_loaded = _weights.tensor() != nullptr; + bool biases_are_loaded = _weights.tensor() != nullptr; + + // Set bias and weights target + _weights.set_target(_hint); + _biases.set_target(_hint); + + // Calculate output shape + TensorShape output_shape = calculate_convolution_layer_output_shape(_input->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info); + + // Output auto inizialitation if not yet initialized + arm_compute::auto_init_if_empty(*_output->info(), output_shape, 1, _input->info()->data_type(), _input->info()->fixed_point_position()); + + // Create appropriate convolution function + // TODO(geopin01): Fix convolution layer hints once the GraphContext has been added + if(_num_groups == 1) { - func = instantiate<Hint::OPENCL>(input, _weights, _biases, output, _conv_info, _weights_info); + func = instantiate_convolution(ConvolutionMethodHint::GEMM); } else { - func = instantiate<Hint::NEON>(input, _weights, _biases, output, _conv_info, _weights_info); + func = instantiate_grouped_convolution(ConvolutionMethodHint::GEMM); + } + + // Fill weights + if(!weights_are_loaded) + { + _weights.allocate_and_fill_if_needed(); + } + // Fill biases + if(!biases_are_loaded) + { + _biases.allocate_and_fill_if_needed(); } return func; @@ -112,6 +234,97 @@ void ConvolutionLayer::print_info() { 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; + std::cout << " Data 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 + << " Groups: " << _num_groups + << " WeightsInfo: " << _weights_info + << std::endl; +} + +std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ConvolutionMethodHint conv_method_hint) +{ + std::unique_ptr<arm_compute::IFunction> func; + if(_hint == Hint::OPENCL) + { + func = instantiate<Hint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); + } + else + { + func = instantiate<Hint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); + } + return func; +} + +std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_convolution(ConvolutionMethodHint conv_method_hint) +{ + // Get tensor shapes + TensorShape input_shape = _input->info()->tensor_shape(); + TensorShape output_shape = _output->info()->tensor_shape(); + TensorShape weights_shape = _weights.info().tensor_shape(); + TensorShape biases_shape = _biases.info().tensor_shape(); + + ARM_COMPUTE_ERROR_ON_MSG((input_shape.z() % _num_groups) != 0, "Input depth not multiple of the number of groups!"); + ARM_COMPUTE_ERROR_ON_MSG((output_shape.z() % _num_groups) != 0, "Output depth not multiple of the number of groups!"); + ARM_COMPUTE_ERROR_ON_MSG((weights_shape[3] % _num_groups) != 0, "Number of kernels not multiple of the number of groups!"); + ARM_COMPUTE_ERROR_ON_MSG((biases_shape.x() % _num_groups) != 0, "Biases not multiple of the number of groups!"); + + // Create a grouped convolution function + auto grouped_conv = arm_compute::support::cpp14::make_unique<GroupedConvolutionFunction>(); + + // Create sub-tensors vectors + _is = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); + _os = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); + _ws = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); + _bs = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); + + // Calculate sub-tensor splits + const int input_split = input_shape.z() / _num_groups; + const int output_split = output_shape.z() / _num_groups; + const int weights_split = weights_shape[3] / _num_groups; + const int biases_split = biases_shape.x() / _num_groups; + + // Calculate sub-tensor shapes + input_shape.set(2, input_split); + output_shape.set(2, output_split); + weights_shape.set(3, weights_split); + biases_shape.set(0, biases_split); + + // Configure sub-tensors + for(int i = 0; i < static_cast<int>(_num_groups); ++i) + { + // Create convolution function + std::unique_ptr<arm_compute::IFunction> func; + + // Calculate sub-tensors starting coordinates + Coordinates input_coord(0, 0, input_split * i); + Coordinates output_coord(0, 0, output_split * i); + Coordinates weights_coord(0, 0, 0, weights_split * i); + Coordinates biases_coord(biases_split * i); + + // Create sub-tensors for input, output, weights and bias + auto hint_to_use = (_hint == Hint::OPENCL) ? Hint::OPENCL : Hint::NEON; + _is[i] = SubTensor(_input, input_shape, input_coord, hint_to_use); + _os[i] = SubTensor(_output, output_shape, output_coord, hint_to_use); + _ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use); + _bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use); + + // Instantiate convolution function + if(_hint == Hint::OPENCL) + { + func = instantiate<Hint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); + } + else + { + func = instantiate<Hint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); + } + + // Add convolution function to the list of convolutions for the grouped convolution + grouped_conv->add_convolution_function(std::move(func)); + } + + return std::move(grouped_conv); } diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp index 8d244cb515..fcc86be8fa 100644 --- a/src/graph/nodes/FullyConnectedLayer.cpp +++ b/src/graph/nodes/FullyConnectedLayer.cpp @@ -33,6 +33,16 @@ 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, Hint hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) { @@ -95,8 +105,10 @@ std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hi _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()); + // 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; _hint = hint; @@ -125,6 +137,10 @@ void FullyConnectedLayer::print_info() { 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; + 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; } diff --git a/src/graph/nodes/NormalizationLayer.cpp b/src/graph/nodes/NormalizationLayer.cpp new file mode 100644 index 0000000000..55ef9bf243 --- /dev/null +++ b/src/graph/nodes/NormalizationLayer.cpp @@ -0,0 +1,105 @@ +/* + * 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/NormalizationLayer.h" + +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/functions/CLNormalizationLayer.h" +#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "support/ToolchainSupport.h" +#include "utils/TypePrinter.h" + +using namespace arm_compute::graph; + +namespace +{ +template <typename NormalizationType, typename TensorType, Hint hint> +std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) +{ + auto norm = arm_compute::support::cpp14::make_unique<NormalizationType>(); + norm->configure( + dynamic_cast<TensorType *>(input), + dynamic_cast<TensorType *>(output), + norm_info); + + return std::move(norm); +} + +template <Hint hint> +std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info); + +template <> +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) +{ + return instantiate_function<arm_compute::CLNormalizationLayer, arm_compute::CLTensor, Hint::OPENCL>(input, output, norm_info); +} + +template <> +std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) +{ + return instantiate_function<arm_compute::NENormalizationLayer, arm_compute::Tensor, Hint::NEON>(input, output, norm_info); +} +} // namespace + +NormalizationLayer::NormalizationLayer(const NormalizationLayerInfo norm_info) + : _norm_info(norm_info) +{ +} + +std::unique_ptr<arm_compute::IFunction> NormalizationLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +{ + std::unique_ptr<arm_compute::IFunction> func; + _hint = hint; + _input = input; + _output = output; + + if(_hint == Hint::OPENCL) + { + func = instantiate<Hint::OPENCL>(input, output, _norm_info); + } + else + { + func = instantiate<Hint::NEON>(input, output, _norm_info); + } + + return func; +} + +void NormalizationLayer::print_info() +{ + if(_hint == Hint::OPENCL) + { + std::cout << "Instantiating CLNormalizationLayer"; + } + else + { + std::cout << "Instantiating NENormalizationLayer"; + } + + std::cout << " Data Type: " << _input->info()->data_type() + << " Input shape: " << _input->info()->tensor_shape() + << " Output shape: " << _output->info()->tensor_shape() + << " Normalization info: " << _norm_info + << std::endl; +} |