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
diff --git a/arm_compute/graph/backends/ValidateHelpers.h b/arm_compute/graph/backends/ValidateHelpers.h
index 673caf9..d689c1f 100644
--- a/arm_compute/graph/backends/ValidateHelpers.h
+++ b/arm_compute/graph/backends/ValidateHelpers.h
@@ -579,6 +579,79 @@
// Validate function
return YOLOLayer::validate(input, output, node.activation_info(), node.num_classes());
}
+/** Validates a element-wise layer node
+ *
+ * @param[in] node Node to validate
+ *
+ * @return Status
+ */
+template <typename EltwiseLayerFunctions>
+Status validate_eltwise_Layer(EltwiseLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 2);
+ ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract input and output
+ const arm_compute::ITensorInfo *input1 = detail::get_backing_tensor_info(node.input(0));
+ const arm_compute::ITensorInfo *input2 = detail::get_backing_tensor_info(node.input(1));
+ const arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
+ const EltwiseOperation eltwise_op = node.eltwise_operation();
+ const ConvertPolicy convert_policy = node.convert_policy();
+ const RoundingPolicy round_policy = node.rounding_policy();
+ const ActivationLayerInfo act_info = node.fused_activation();
+ const QuantizationInfo quant_info = node.output_quant_info();
+ const float scale = (quant_info.scale().empty()) ? 1.0f : quant_info.scale()[0];
+
+ // Validate function
+ if(eltwise_op == EltwiseOperation::Add)
+ {
+ return EltwiseLayerFunctions::ArithmeticAddition::validate(input1, input2, output, convert_policy, act_info);
+ }
+ else if(eltwise_op == EltwiseOperation::Sub)
+ {
+ return EltwiseLayerFunctions::ArithmeticSubtraction::validate(input1, input2, output, convert_policy, act_info);
+ }
+ else if(eltwise_op == EltwiseOperation::Mul)
+ {
+ return EltwiseLayerFunctions::PixelWiseMultiplication::validate(input1, input2, output, scale, convert_policy, round_policy, act_info);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Unsupported element-wise operation!");
+ }
+ return Status{};
+}
+/** Validates a unary element-wise layer node
+ *
+ * @param[in] node Node to validate
+ *
+ * @return Status
+ */
+template <typename UnaryEltwiseLayerFunctions>
+Status validate_unary_eltwise_layer(UnaryEltwiseLayerNode &node)
+{
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Validating EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_RETURN_ERROR_ON(node.num_inputs() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(node.num_outputs() != 1);
+
+ // Extract input and output
+ arm_compute::ITensorInfo *input = detail::get_backing_tensor_info(node.input(0));
+ arm_compute::ITensorInfo *output = get_backing_tensor_info(node.output(0));
+ const UnaryEltwiseOperation eltwise_op = node.eltwise_descriptor().op;
+
+ // Validate function
+ if(eltwise_op == UnaryEltwiseOperation::Exp)
+ {
+ return UnaryEltwiseLayerFunctions::ExpLayer::validate(input, output);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Unsupported unary element-wise operation!");
+ }
+
+ return Status{};
+}
} // namespace detail
} // namespace backends
} // namespace graph