COMPMID-3381: Implement graph example for YoLo v3 output detector
Add sub/exp/splitv support in graph api
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I4e08cc19a46655717068b12c93d67e619a595d9a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3309
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index 975e5fe..e21b8ed 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -816,6 +816,54 @@
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