diff options
Diffstat (limited to 'arm_compute/graph/backends/FunctionHelpers.h')
| -rw-r--r-- | arm_compute/graph/backends/FunctionHelpers.h | 41 |
1 files changed, 41 insertions, 0 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index 7f5093aa24..32ef0aaf13 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -911,6 +911,47 @@ std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphCon return std::move(func); } +/** Create a backend Upsample layer function + * + * @tparam UpsampleLayerFunction Backend Upsample function + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * @param[in] ctx Graph context + * + * @return Backend Upsample layer function + */ +template <typename UpsampleLayerFunction, typename TargetInfo> +std::unique_ptr<IFunction> create_upsample_layer(UpsampleLayerNode &node, GraphContext &ctx) +{ + 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 Size2D info = node.info(); + const InterpolationPolicy upsampling_policy = node.upsampling_policy(); + ARM_COMPUTE_ERROR_ON(upsampling_policy != InterpolationPolicy::NEAREST_NEIGHBOR); + ARM_COMPUTE_ERROR_ON(info.x() != 2 || info.y() != 2); + ARM_COMPUTE_ERROR_ON(input == nullptr); + ARM_COMPUTE_ERROR_ON(output == nullptr); + + // Create and configure function + auto func = support::cpp14::make_unique<UpsampleLayerFunction>(); + func->configure(input, output, info, upsampling_policy); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type() + << " Target " << TargetInfo::TargetType + << " Data Type: " << input->info()->data_type() + << " Input shape: " << input->info()->tensor_shape() + << " Output shape: " << output->info()->tensor_shape() + << " Strides: " << info + << " Upsampling policy: " << upsampling_policy + << std::endl); + + return std::move(func); +} /** Create a backend YOLO layer function * * @tparam YoloLayerFunction Backend YOLO function |