diff options
Diffstat (limited to 'src/graph/nodes/BatchNormalizationLayer.cpp')
-rw-r--r-- | src/graph/nodes/BatchNormalizationLayer.cpp | 74 |
1 files changed, 15 insertions, 59 deletions
diff --git a/src/graph/nodes/BatchNormalizationLayer.cpp b/src/graph/nodes/BatchNormalizationLayer.cpp index db809f4ee4..bce19016d7 100644 --- a/src/graph/nodes/BatchNormalizationLayer.cpp +++ b/src/graph/nodes/BatchNormalizationLayer.cpp @@ -23,60 +23,20 @@ */ #include "arm_compute/graph/nodes/BatchNormalizationLayer.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h" -#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h" -#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/graph/NodeContext.h" +#include "arm_compute/graph/OperationRegistry.h" #include "support/ToolchainSupport.h" -#include "utils/TypePrinter.h" using namespace arm_compute::graph; -namespace -{ -template <typename BatchBatchNormalizationLayer, typename TensorType, TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) -{ - auto norm = arm_compute::support::cpp14::make_unique<BatchBatchNormalizationLayer>(); - norm->configure( - dynamic_cast<TensorType *>(input), - dynamic_cast<TensorType *>(output), - dynamic_cast<TensorType *>(mean.set_target(target_hint)), - dynamic_cast<TensorType *>(var.set_target(target_hint)), - dynamic_cast<TensorType *>(beta.set_target(target_hint)), - dynamic_cast<TensorType *>(gamma.set_target(target_hint)), - epsilon); - - return std::move(norm); -} - -template <TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon); - -template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, - float epsilon) -{ - return instantiate_function<arm_compute::CLBatchNormalizationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, mean, var, beta, gamma, epsilon); -} - -template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon) -{ - return instantiate_function<arm_compute::NEBatchNormalizationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, mean, var, beta, gamma, epsilon); -} -} // namespace - std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) { ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr); ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr); - std::unique_ptr<arm_compute::IFunction> func; - _target_hint = ctx.hints().target_hint(); - arm_compute::ITensor *in = input->tensor(); arm_compute::ITensor *out = output->tensor(); + _target_hint = ctx.hints().target_hint(); unsigned int batch_norm_size = in->info()->dimension(2); if(_mean.tensor() == nullptr) @@ -96,21 +56,17 @@ std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_nod _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } - if(_target_hint == TargetHint::OPENCL) - { - func = instantiate<TargetHint::OPENCL>(in, out, _mean, _var, _beta, _gamma, _epsilon); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLBatchNormalizationLayer"); - } - else - { - func = instantiate<TargetHint::NEON>(in, out, _mean, _var, _beta, _gamma, _epsilon); - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEBatchNormalizationLayer"); - } - - ARM_COMPUTE_LOG_GRAPH_INFO(" Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); + // Create node context + NodeContext node_ctx(OperationType::BatchNormalizationLayer); + node_ctx.set_target(_target_hint); + node_ctx.add_input(in); + node_ctx.add_input(_mean.tensor()); + node_ctx.add_input(_var.tensor()); + node_ctx.add_input(_beta.tensor()); + node_ctx.add_input(_gamma.tensor()); + node_ctx.add_output(out); + node_ctx.add_parameter<float>("epsilon", _epsilon); - return func; + // Get function + return OperationRegistry::get().find_operation(OperationType::BatchNormalizationLayer, _target_hint)->configure(node_ctx); }
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