compmid-2573: Investigate FP16 Winograd reference implementations

Change-Id: I5a3e692c046a5ad28a676c03e3e51950c64cf503
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1845
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

6 files changed, 122 insertions, 65 deletions

diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index bce093d0f5..1de6a5fad7 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -85,8 +85,8 @@ public:
                                                               "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                          get_weights_accessor(data_path,
                                                               "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
-                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
-                                                                                             "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                         nullptr, get_weights_accessor(data_path,
+                                                                       "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                          0.001f)
               .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
@@ -98,8 +98,8 @@ public:
                                                               "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
                                          get_weights_accessor(data_path,
                                                               "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
-                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
-                                                                                             "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
+                                         nullptr, get_weights_accessor(data_path,
+                                                                       "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
                                          0.001f)
               .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
@@ -112,8 +112,8 @@ public:
                                                               "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
                                          get_weights_accessor(data_path,
                                                               "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
-                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
-                                                                                             "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
+                                         nullptr, get_weights_accessor(data_path,
+                                                                       "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
                                          0.001f)
               .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
@@ -128,8 +128,8 @@ public:
                                                               "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
                                          get_weights_accessor(data_path,
                                                               "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
-                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
-                                                                                             "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
+                                         nullptr, get_weights_accessor(data_path,
+                                                                       "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
                                          0.001f)
               .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
@@ -142,8 +142,8 @@ public:
                                                               "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
                                          get_weights_accessor(data_path,
                                                               "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
-                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
-                                                                                             "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
+                                         nullptr, get_weights_accessor(data_path,
+                                                                       "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
                                          0.001f)
               .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
@@ -249,7 +249,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -265,7 +265,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm")
@@ -279,7 +279,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm")
@@ -295,7 +295,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -309,7 +309,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
@@ -323,7 +323,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm")
@@ -340,7 +340,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
@@ -364,7 +364,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm")
@@ -380,7 +380,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -394,7 +394,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm")
@@ -408,7 +408,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm")
@@ -437,7 +437,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -453,7 +453,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -467,7 +467,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
@@ -481,7 +481,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
@@ -497,7 +497,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -511,7 +511,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm")
@@ -525,7 +525,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm")
@@ -539,7 +539,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm")
@@ -553,7 +553,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm")
@@ -570,7 +570,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
@@ -594,7 +594,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -608,7 +608,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm")
@@ -624,7 +624,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -638,7 +638,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
@@ -652,7 +652,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
@@ -666,7 +666,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm")
@@ -703,7 +703,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -719,7 +719,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -735,7 +735,7 @@ private:
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
-                 get_random_accessor(1.f, 1.f),
+                 nullptr,
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
                  0.001f)
              .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm")
@@ -751,7 +751,7 @@ private:
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"),
-                 get_random_accessor(1.f, 1.f),
+                 nullptr,
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"),
                  0.001f)
              .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm")
@@ -770,7 +770,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
@@ -784,7 +784,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
@@ -800,7 +800,7 @@ private:
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
-                 get_random_accessor(1.f, 1.f),
+                 nullptr,
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
                  0.001f)
              .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm")
@@ -816,7 +816,7 @@ private:
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"),
-                 get_random_accessor(1.f, 1.f),
+                 nullptr,
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"),
                  0.001f)
              .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm")
@@ -836,7 +836,7 @@ private:
             << BatchNormalizationLayer(
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
-                get_random_accessor(1.f, 1.f),
+                nullptr,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                 0.001f)
             .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp
index f7f3454fad..4c3a905598 100644
--- a/src/graph/mutators/NodeFusionMutator.cpp
+++ b/src/graph/mutators/NodeFusionMutator.cpp
@@ -71,11 +71,10 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge
         FastMathHint fast_math_hint  = conv_node->fast_math_hint();
 
         // Extract bn inputs
-        const auto bn_mean_id  = bn_node->input_edge(1)->producer_id();
-        const auto bn_var_id   = bn_node->input_edge(2)->producer_id();
-        const auto bn_beta_id  = bn_node->input_edge(3)->producer_id();
-        const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
-        const auto epsilon     = bn_node->epsilon();
+        const auto bn_mean_id = bn_node->input_edge(1)->producer_id();
+        const auto bn_var_id  = bn_node->input_edge(2)->producer_id();
+
+        const auto epsilon = bn_node->epsilon();
 
         // Create the fused node
         const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info);
@@ -91,8 +90,18 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge
         g.add_connection(conv_weights_id, 0, fused_id, 1);
         g.add_connection(bn_mean_id, 0, fused_id, 3);
         g.add_connection(bn_var_id, 0, fused_id, 4);
-        g.add_connection(bn_beta_id, 0, fused_id, 5);
-        g.add_connection(bn_gamma_id, 0, fused_id, 6);
+
+        if(bn_node->input_edge(3) != nullptr)
+        {
+            const auto bn_beta_id = bn_node->input_edge(3)->producer_id();
+            g.add_connection(bn_beta_id, 0, fused_id, 5);
+        }
+
+        if(bn_node->input_edge(4) != nullptr)
+        {
+            const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
+            g.add_connection(bn_gamma_id, 0, fused_id, 6);
+        }
 
         auto                     fused_node       = g.node(fused_id);
         std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);
diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index 00165cd6c2..3646facab2 100644
--- a/utils/GraphUtils.cpp
+++ b/utils/GraphUtils.cpp
@@ -63,17 +63,33 @@ TFPreproccessor::TFPreproccessor(float min_range, float max_range)
 }
 void TFPreproccessor::preprocess(ITensor &tensor)
 {
+    if(tensor.info()->data_type() == DataType::F32)
+    {
+        preprocess_typed<float>(tensor);
+    }
+    else if(tensor.info()->data_type() == DataType::F16)
+    {
+        preprocess_typed<half>(tensor);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+}
+
+template <typename T>
+void TFPreproccessor::preprocess_typed(ITensor &tensor)
+{
     Window window;
     window.use_tensor_dimensions(tensor.info()->tensor_shape());
 
     const float range = _max_range - _min_range;
-
     execute_window_loop(window, [&](const Coordinates & id)
     {
-        const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id));
-        float       res                                       = value / 255.f;            // Normalize to [0, 1]
-        res                                                   = res * range + _min_range; // Map to [min_range, max_range]
-        *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = res;
+        const T value                                     = *reinterpret_cast<T *>(tensor.ptr_to_element(id));
+        float   res                                       = value / 255.f;            // Normalize to [0, 1]
+        res                                               = res * range + _min_range; // Map to [min_range, max_range]
+        *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = res;
     });
 }
 
@@ -88,15 +104,31 @@ CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr, floa
 
 void CaffePreproccessor::preprocess(ITensor &tensor)
 {
+    if(tensor.info()->data_type() == DataType::F32)
+    {
+        preprocess_typed<float>(tensor);
+    }
+    else if(tensor.info()->data_type() == DataType::F16)
+    {
+        preprocess_typed<half>(tensor);
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+}
+
+template <typename T>
+void CaffePreproccessor::preprocess_typed(ITensor &tensor)
+{
     Window window;
     window.use_tensor_dimensions(tensor.info()->tensor_shape());
-
     const int channel_idx = get_data_layout_dimension_index(tensor.info()->data_layout(), DataLayoutDimension::CHANNEL);
 
     execute_window_loop(window, [&](const Coordinates & id)
     {
-        const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - _mean[id[channel_idx]];
-        *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value * _scale;
+        const T value                                     = *reinterpret_cast<T *>(tensor.ptr_to_element(id)) - T(_mean[id[channel_idx]]);
+        *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value * T(_scale);
     });
 }
 
@@ -370,6 +402,9 @@ bool ValidationOutputAccessor::access_tensor(arm_compute::ITensor &tensor)
             case DataType::QASYMM8:
                 tensor_results = access_predictions_tensor<uint8_t>(tensor);
                 break;
+            case DataType::F16:
+                tensor_results = access_predictions_tensor<half>(tensor);
+                break;
             case DataType::F32:
                 tensor_results = access_predictions_tensor<float>(tensor);
                 break;
diff --git a/utils/GraphUtils.h b/utils/GraphUtils.h
index 3417135f17..4c25dd2460 100644
--- a/utils/GraphUtils.h
+++ b/utils/GraphUtils.h
@@ -70,6 +70,9 @@ public:
     void preprocess(ITensor &tensor) override;
 
 private:
+    template <typename T>
+    void preprocess_typed(ITensor &tensor);
+
     std::array<float, 3> _mean;
     bool  _bgr;
     float _scale;
@@ -90,6 +93,9 @@ public:
     void preprocess(ITensor &tensor) override;
 
 private:
+    template <typename T>
+    void preprocess_typed(ITensor &tensor);
+
     float _min_range;
     float _max_range;
 };
diff --git a/utils/ImageLoader.h b/utils/ImageLoader.h
index 24fcbe179a..5d3a84c59a 100644
--- a/utils/ImageLoader.h
+++ b/utils/ImageLoader.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -249,14 +249,14 @@ public:
      *
      * @note If the image is a CLImage, the function maps and unmaps the image
      *
-     * @param[in,out] tensor Tensor with 3 planes to fill (Must be allocated, and of matching dimensions with the opened image). Data types supported: U8/F32
+     * @param[in,out] tensor Tensor with 3 planes to fill (Must be allocated, and of matching dimensions with the opened image). Data types supported: U8/F16/F32
      * @param[in]     bgr    (Optional) Fill the first plane with blue channel (default = false)
      */
     template <typename T>
     void fill_planar_tensor(T &tensor, bool bgr = false)
     {
         ARM_COMPUTE_ERROR_ON(!is_open());
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::U8, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::U8, DataType::F32, DataType::F16);
 
         const DataLayout  data_layout  = tensor.info()->data_layout();
         const TensorShape tensor_shape = tensor.info()->tensor_shape();
@@ -324,6 +324,13 @@ public:
                         *reinterpret_cast<float *>(out.ptr() + 2 * stride_z) = static_cast<float>(bgr ? red : blue);
                         break;
                     }
+                    case DataType::F16:
+                    {
+                        *reinterpret_cast<half *>(out.ptr() + 0 * stride_z) = static_cast<half>(bgr ? blue : red);
+                        *reinterpret_cast<half *>(out.ptr() + 1 * stride_z) = static_cast<half>(green);
+                        *reinterpret_cast<half *>(out.ptr() + 2 * stride_z) = static_cast<half>(bgr ? red : blue);
+                        break;
+                    }
                     default:
                     {
                         ARM_COMPUTE_ERROR("Unsupported data type");
diff --git a/utils/Utils.h b/utils/Utils.h
index ec08896257..7fa74ab08b 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -401,7 +401,7 @@ public:
     void fill_tensor(T &tensor)
     {
         ARM_COMPUTE_ERROR_ON(!is_open());
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, arm_compute::DataType::F32);
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::QASYMM8, arm_compute::DataType::S32, arm_compute::DataType::F32, arm_compute::DataType::F16);
         try
         {
             // Map buffer if creating a CLTensor