diff options
Diffstat (limited to 'arm_compute/graph/backends/FunctionHelpers.h')
-rw-r--r-- | arm_compute/graph/backends/FunctionHelpers.h | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index 975e5fe55e..e21b8ed288 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -816,6 +816,54 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node) return RETURN_UNIQUE_PTR(func); } +/** Create a backend unary element-wise operation layer function + * + * @tparam UnaryEltwiseFunctions Backend unary element-wise function + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * + * @return Backend unary element-wise operation layer function + */ +template <typename UnaryEltwiseFunctions, typename TargetInfo> +std::unique_ptr<IFunction> create_unary_eltwise_layer(UnaryEltwiseLayerNode &node) +{ + validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); + + // Extract IO and info + typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); + typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); + const UnaryEltwiseOperation eltwise_op = node.eltwise_descriptor().op; + + ARM_COMPUTE_ERROR_ON(input == nullptr); + ARM_COMPUTE_ERROR_ON(output == nullptr); + + std::unique_ptr<IFunction> func = nullptr; + std::string func_name; + if(eltwise_op == UnaryEltwiseOperation::Exp) + { + std::tie(func, func_name) = create_named_function<typename UnaryEltwiseFunctions::Exp>( + std::string("Exp"), + input, output); + } + else + { + ARM_COMPUTE_ERROR("Unsupported unary element-wise operation!"); + } + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " + << node.name() + << " Type: " << node.type() + << " Target: " << TargetInfo::TargetType + << " Operation: " << func_name + << " Data Type: " << input->info()->data_type() + << " Shape: " << input->info()->tensor_shape() + << std::endl); + + return RETURN_UNIQUE_PTR(func); +} + /** Create a backend flatten layer function * * @tparam FlattenLayerFunction Backend flatten function |