commit 5dea19e58a5521b05e95375c8618a37072697bc0
author Gian Marco Iodice <gianmarco.iodice@arm.com> Fri Nov 08 12:13:48 2019 +0000
committer Gian Marco Iodice <gianmarco.iodice@arm.com> Tue Nov 12 11:52:32 2019 +0000
tree eac3414eb9a5f2290b3bb597e4c6d8e398678355
parent eaa01ab593428bc7267ebbe107b2d813a11b64b5

COMPMID-2579: Fuse batch normalization with convolution and depthwise convolution at graph level on NEON

Change-Id: Ib263a680bbd2dc1a4947102ee8d6da76b95f02bf
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2252
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

arm_compute/graph/backends/FunctionHelpers.h

diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index ee257e3..02bfe9d 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -174,11 +174,12 @@
  * @tparam TargetInfo                      Target-specific information
  *
  * @param[in] node Node to create the backend function for
+ * @param[in] ctx  Graph context
  *
  * @return Backend batch normalization layer function
  */
 template <typename FusedLayerTypes, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node)
+std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node, GraphContext &ctx)
 {
     validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */);
 
@@ -199,9 +200,16 @@
     const ActivationLayerInfo fused_act  = node.fused_activation();
     const float               epsilon    = node.epsilon();
 
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType);
+    std::unique_ptr<IFunction>      func;
+    std::string                     func_name;
+
+    using FType = FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>;
+
     // Create and configure function
-    auto func = support::cpp14::make_unique<FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>();
-    func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act);
+    std::tie(func, func_name) = create_named_memory_managed_function<FType>(
+                                    std::string("FusedConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act);
 
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
@@ -214,7 +222,7 @@
                                << " Output shape: " << output->info()->tensor_shape()
                                << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
                                << std::endl);
-    return std::move(func);
+    return func;
 }
 
 /** Create a backend fused depthwise convolution batch normalization layer function
@@ -223,11 +231,12 @@
  * @tparam TargetInfo                  Target-specific information
  *
  * @param[in] node Node to create the backend function for
+ * @param[in] ctx  Graph context
  *
  * @return Backend fused depthwise convolution batch normalization layer function
  */
 template <typename FusedLayerTypes, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node)
+std::unique_ptr<IFunction> create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node, GraphContext &ctx)
 {
     validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */);
 
@@ -247,9 +256,16 @@
     const ActivationLayerInfo fused_act        = node.fused_activation();
     const float               epsilon          = node.epsilon();
 
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType);
+    std::unique_ptr<IFunction>      func;
+    std::string                     func_name;
+
+    using FType = FusedDepthwiseConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>;
+
     // Create and configure function
-    auto func = support::cpp14::make_unique<FusedDepthwiseConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>();
-    func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, depth_multiplier, fused_act);
+    std::tie(func, func_name) = create_named_memory_managed_function<FType>(
+                                    std::string("FusedDepthwiseConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, depth_multiplier, fused_act);
 
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
@@ -262,7 +278,7 @@
                                << " Output shape: " << output->info()->tensor_shape()
                                << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
                                << std::endl);
-    return std::move(func);
+    return func;
 }
 
 /** Create a backend bounding box transform layer function