diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2017-10-18 17:29:27 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | 0c29cd3aac40e512d648506b1b3b8332bb45f063 (patch) | |
tree | d1ca728a9adf233f590ab7dfff8e627166c30b83 /src/graph/nodes | |
parent | daaa1fa506834c9d9ff44e5b38f05781ec416912 (diff) | |
download | ComputeLibrary-0c29cd3aac40e512d648506b1b3b8332bb45f063.tar.gz |
COMPMID-630 : Rework nodes for selective target compilation.
Reworked nodes:
-ActivationLayer
Change-Id: Iaa394531ef208db48caa2c18a41ad9a845471f94
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92281
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src/graph/nodes')
-rw-r--r-- | src/graph/nodes/ActivationLayer.cpp | 63 | ||||
-rw-r--r-- | src/graph/nodes/ConvolutionLayer.cpp | 6 |
2 files changed, 14 insertions, 55 deletions
diff --git a/src/graph/nodes/ActivationLayer.cpp b/src/graph/nodes/ActivationLayer.cpp index df73ba7078..ea87fd9592 100644 --- a/src/graph/nodes/ActivationLayer.cpp +++ b/src/graph/nodes/ActivationLayer.cpp @@ -23,45 +23,11 @@ */ #include "arm_compute/graph/nodes/ActivationLayer.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/CL/functions/CLActivationLayer.h" -#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" -#include "arm_compute/runtime/Tensor.h" -#include "support/ToolchainSupport.h" -#include "utils/TypePrinter.h" +#include "arm_compute/graph/NodeContext.h" +#include "arm_compute/graph/OperationRegistry.h" using namespace arm_compute::graph; -namespace -{ -template <typename ActivationType, typename TensorType, TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *output, const ActivationLayerInfo &activation_info) -{ - auto activation = arm_compute::support::cpp14::make_unique<ActivationType>(); - activation->configure( - dynamic_cast<TensorType *>(input), - dynamic_cast<TensorType *>(output), - activation_info); - - return std::move(activation); -} - -template <TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *output, const ActivationLayerInfo &activation_info); - -template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *output, const ActivationLayerInfo &activation_info) -{ - return instantiate_function<arm_compute::CLActivationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, activation_info); -} - -template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *output, const ActivationLayerInfo &activation_info) -{ - return instantiate_function<arm_compute::NEActivationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, activation_info); -} -} // namespace - ActivationLayer::ActivationLayer(const ActivationLayerInfo activation_info) : _activation_info(activation_info) { @@ -78,23 +44,14 @@ std::unique_ptr<arm_compute::IFunction> ActivationLayer::instantiate_node(GraphC arm_compute::ITensor *in = input->tensor(); arm_compute::ITensor *out = output->tensor(); - if(_target_hint == TargetHint::OPENCL) - { - func = instantiate<TargetHint::OPENCL>(in, out, _activation_info); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLActivationLayer"); - } - else - { - func = instantiate<TargetHint::NEON>(in, out, _activation_info); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEActivationLayer"); - } + // Create node context + NodeContext node_ctx("ActivationLayer"); + node_ctx.add_input(in); + node_ctx.add_output(out); + node_ctx.add_parameter<ActivationLayerInfo>("ActivationLayerInfo", _activation_info); + + // Get function + func = OperationRegistry::get().find_operation("ActivationLayer", _target_hint)->configure(node_ctx); - ARM_COMPUTE_LOG_GRAPH_INFO(" Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Activation function: " << _activation_info.activation() - << " a: " << _activation_info.a() - << " b: " << _activation_info.b() - << std::endl); return func; } diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp index 07d42617e2..d3ab97fb2d 100644 --- a/src/graph/nodes/ConvolutionLayer.cpp +++ b/src/graph/nodes/ConvolutionLayer.cpp @@ -216,12 +216,10 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Graph if(_num_groups == 1) { func = instantiate_convolution(in, out, conv_method_hint); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); } else { func = instantiate_grouped_convolution(in, out, conv_method_hint); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); } // Fill weights @@ -253,10 +251,12 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolutio std::unique_ptr<arm_compute::IFunction> func; if(_target_hint == TargetHint::OPENCL) { + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); func = instantiate<TargetHint::OPENCL>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); } else { + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); func = instantiate<TargetHint::NEON>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); } return func; @@ -318,10 +318,12 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_co // Instantiate convolution function if(_target_hint == TargetHint::OPENCL) { + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); } else { + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); } |