COMPMID-1042: Graph / Stream not working on x86

Avoid unspecified behavior in graph construction.
This is fixed in C++17.

Change-Id: I4ef45cb139bbd838103a9922441e32d2d16c33d2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127975
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

6 files changed, 132 insertions, 132 deletions

diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 6c3fac4af2..85384d48d3 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -113,19 +113,19 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
               << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-              << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
-              << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-              << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
-              << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
-              << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
-              << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
-              << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-              << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-              << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
+        graph << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U);
+        graph << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
+        graph << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U);
+        graph << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U);
+        graph << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U);
+        graph << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U);
+        graph << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
+        graph << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U);
+        graph << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
               << FullyConnectedLayer(
                   1000U,
                   get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 22e6f6e489..ed79ba65d8 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -152,35 +152,35 @@ public:
                                          0.001f)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
 
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-
-              << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
-                                      32U)
-              << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
-                                      64U, true)
-              << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
-                                      64U)
-
-              << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U))
-
-              << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
-                                      std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
-              << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
-                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
-              << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
-                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
-              << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
-                                      std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
-
-              << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
-                                      std::make_tuple(192U, 192U, 192U, 192U))
-
-              << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
-                                      std::make_tuple(448U, 384U, 384U, 384U), 192U)
-              << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
-                                      std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
-
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)));
+
+        graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      32U);
+        graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      64U, true);
+        graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      64U);
+
+        graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U));
+
+        graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
+                                      std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U);
+        graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
+                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U);
+        graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
+                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U);
+        graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
+                                      std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U);
+
+        graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
+                                      std::make_tuple(192U, 192U, 192U, 192U));
+
+        graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
+                                      std::make_tuple(448U, 384U, 384U, 384U), 192U);
+        graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
+                                      std::make_tuple(448U, 384U, 384U, 384U), 192U, true);
+
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
               << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
                                                                       "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"),
                                   get_weights_accessor(data_path,
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index 1f7875176f..d505133833 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -123,33 +123,33 @@ public:
                                          get_random_accessor(1.f, 1.f),
                                          get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
                                          0.001f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
-              << get_mixed_3a(data_path)
-              << get_mixed_4a(data_path)
-              << get_mixed_5a(data_path)
-              // 4 inception A blocks
-              << get_inceptionA_block(data_path, "Mixed_5b")
-              << get_inceptionA_block(data_path, "Mixed_5c")
-              << get_inceptionA_block(data_path, "Mixed_5d")
-              << get_inceptionA_block(data_path, "Mixed_5e")
-              // reduction A block
-              << get_reductionA_block(data_path)
-              // 7 inception B blocks
-              << get_inceptionB_block(data_path, "Mixed_6b")
-              << get_inceptionB_block(data_path, "Mixed_6c")
-              << get_inceptionB_block(data_path, "Mixed_6d")
-              << get_inceptionB_block(data_path, "Mixed_6e")
-              << get_inceptionB_block(data_path, "Mixed_6f")
-              << get_inceptionB_block(data_path, "Mixed_6g")
-              << get_inceptionB_block(data_path, "Mixed_6h")
-              // reduction B block
-              << get_reductionB_block(data_path)
-              // 3 inception C blocks
-              << get_inceptionC_block(data_path, "Mixed_7b")
-              << get_inceptionC_block(data_path, "Mixed_7c")
-              << get_inceptionC_block(data_path, "Mixed_7d")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        graph << get_mixed_3a(data_path);
+        graph << get_mixed_4a(data_path);
+        graph << get_mixed_5a(data_path);
+        // 4 inception A blocks
+        graph << get_inceptionA_block(data_path, "Mixed_5b");
+        graph << get_inceptionA_block(data_path, "Mixed_5c");
+        graph << get_inceptionA_block(data_path, "Mixed_5d");
+        graph << get_inceptionA_block(data_path, "Mixed_5e");
+        // reduction A block
+        graph << get_reductionA_block(data_path);
+        // 7 inception B blocks
+        graph << get_inceptionB_block(data_path, "Mixed_6b");
+        graph << get_inceptionB_block(data_path, "Mixed_6c");
+        graph << get_inceptionB_block(data_path, "Mixed_6d");
+        graph << get_inceptionB_block(data_path, "Mixed_6e");
+        graph << get_inceptionB_block(data_path, "Mixed_6f");
+        graph << get_inceptionB_block(data_path, "Mixed_6g");
+        graph << get_inceptionB_block(data_path, "Mixed_6h");
+        // reduction B block
+        graph << get_reductionB_block(data_path);
+        // 3 inception C blocks
+        graph << get_inceptionC_block(data_path, "Mixed_7b");
+        graph << get_inceptionC_block(data_path, "Mixed_7c");
+        graph << get_inceptionC_block(data_path, "Mixed_7d");
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << FlattenLayer()
               << FullyConnectedLayer(
                   1001U,
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 055f289f52..743dd1a50c 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -123,21 +123,21 @@ public:
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"),
                   0.001f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
-              << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+        graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 32be14305f..e9bd8a9c5f 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -106,60 +106,60 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire2", 64U, 64U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire2", 64U, 64U);
+        graph << ConvolutionLayer(
                   1U, 1U, 16U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire3", 64U, 64U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire3", 64U, 64U);
+        graph << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire4", 128U, 128U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire4", 128U, 128U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
               << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire5", 128U, 128U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire5", 128U, 128U);
+        graph << ConvolutionLayer(
                   1U, 1U, 48U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire6", 192U, 192U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire6", 192U, 192U);
+        graph << ConvolutionLayer(
                   1U, 1U, 48U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire7", 192U, 192U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire7", 192U, 192U);
+        graph << ConvolutionLayer(
                   1U, 1U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire8", 256U, 256U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire8", 256U, 256U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
               << ConvolutionLayer(
                   1U, 1U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire9", 256U, 256U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire9", 256U, 256U);
+        graph << ConvolutionLayer(
                   1U, 1U, 1000U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 2bdb00d7c2..1696b7df43 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -110,60 +110,60 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire2", 64U, 64U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire2", 64U, 64U);
+        graph << ConvolutionLayer(
                   1U, 1U, 16U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire3", 64U, 64U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire3", 64U, 64U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
               << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire4", 128U, 128U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire4", 128U, 128U);
+        graph << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire5", 128U, 128U)
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire5", 128U, 128U);
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
               << ConvolutionLayer(
                   1U, 1U, 48U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire6", 192U, 192U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire6", 192U, 192U);
+        graph << ConvolutionLayer(
                   1U, 1U, 48U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire7", 192U, 192U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire7", 192U, 192U);
+        graph << ConvolutionLayer(
                   1U, 1U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire8", 256U, 256U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire8", 256U, 256U);
+        graph << ConvolutionLayer(
                   1U, 1U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << get_expand_fire_node(data_path, "fire9", 256U, 256U)
-              << ConvolutionLayer(
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        graph << get_expand_fire_node(data_path, "fire9", 256U, 256U);
+        graph << ConvolutionLayer(
                   1U, 1U, 1000U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_b.npy"),