diff options
Diffstat (limited to 'arm_compute/graph/backends/FunctionHelpers.h')
-rw-r--r-- | arm_compute/graph/backends/FunctionHelpers.h | 38 |
1 files changed, 38 insertions, 0 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index b1446ea493..88d6ce15b9 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -621,6 +621,44 @@ std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &no return std::move(func); } +/** Create a backend normalize planar YUV layer function + * + * @tparam NormalizePlanarYUVLayerFunction Backend normalize planar YUV function + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * + * @return Backend normalize plnar YUV layer function + */ +template <typename NormalizePlanarYUVLayerFunction, typename TargetInfo> +std::unique_ptr<IFunction> create_normalize_planar_yuv_layer(NormalizePlanarYUVLayerNode &node) +{ + validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); + + // Extract IO and info + typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); + typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1)); + typename TargetInfo::TensorType *std = get_backing_tensor<TargetInfo>(node.input(2)); + typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); + ARM_COMPUTE_ERROR_ON(input == nullptr); + ARM_COMPUTE_ERROR_ON(mean == nullptr); + ARM_COMPUTE_ERROR_ON(std == nullptr); + ARM_COMPUTE_ERROR_ON(output == nullptr); + + // Create and configure function + auto func = support::cpp14::make_unique<NormalizePlanarYUVLayerFunction>(); + func->configure(input, output, mean, std); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type() + << " Target " << TargetInfo::TargetType + << " Data Type: " << input->info()->data_type() + << " Shape: " << input->info()->tensor_shape() + << std::endl); + + return std::move(func); +} + /** Create a backend permute layer function * * @tparam PermuteLayerFunction Backend permute function |