From 08346e9b9a7dadd2f0765aea64e656902d843e8a Mon Sep 17 00:00:00 2001
From: Georgios Pinitas <georgios.pinitas@arm.com>
Date: Tue, 16 Oct 2018 19:10:46 +0100
Subject: COMPMID-1451:Fuse RELU,LU_BOUNDED_RELU with requantization in
 NEGEMMConvolutionLayer.

Change-Id: Iea5f2c5bcac8051c4c7655a6eabb2c43772eb31f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154104
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
---
 src/graph/backends/NEON/NEFunctionFactory.cpp | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

(limited to 'src/graph/backends/NEON/NEFunctionFactory.cpp')

diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index 286c890088..e967c1be61 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -97,8 +97,9 @@ std::unique_ptr<IFunction> create_convolution_layer<NEConvolutionLayerFunctions,
         biases->info()->set_data_type(DataType::S32);
     }
 
-    const PadStrideInfo     conv_info      = node.convolution_info();
-    const ConvolutionMethod conv_algorithm = node.convolution_method();
+    const PadStrideInfo       conv_info      = node.convolution_info();
+    const ConvolutionMethod   conv_algorithm = node.convolution_method();
+    const ActivationLayerInfo fused_act      = node.fused_activation();
 
     // Create and configure function (we assume that functions have been validated before creation)
     std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::NEON);
@@ -107,22 +108,22 @@ std::unique_ptr<IFunction> create_convolution_layer<NEConvolutionLayerFunctions,
     if(conv_algorithm == ConvolutionMethod::Direct)
     {
         std::tie(func, func_name) = create_named_memory_managed_function<NEDirectConvolutionLayer>(
-                                        std::string("DirectConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+                                        std::string("DirectConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
     }
     else if(conv_algorithm == ConvolutionMethod::GEMM)
     {
         std::tie(func, func_name) = create_named_memory_managed_function<NEGEMMConvolutionLayer>(
-                                        std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+                                        std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
     }
     else if(conv_algorithm == ConvolutionMethod::Winograd)
     {
         std::tie(func, func_name) = create_named_memory_managed_function<NEWinogradConvolutionLayer>(
-                                        std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+                                        std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
     }
     else
     {
         std::tie(func, func_name) = create_named_memory_managed_function<NEConvolutionLayer>(
-                                        std::string("ConvolutionLayer"), mm, input, weights, biases, output, conv_info);
+                                        std::string("ConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
     }
 
     // Log info
@@ -140,6 +141,7 @@ std::unique_ptr<IFunction> create_convolution_layer<NEConvolutionLayerFunctions,
                                << " Input shape: " << input->info()->tensor_shape()
                                << " Weights shape: " << weights->info()->tensor_shape()
                                << " Output shape: " << output->info()->tensor_shape()
+                               << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
                                << std::endl);
     return func;
 }
-- 
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