COMPMID-1386: Add support for converting weights for CL.

Change-Id: I62e3ead903366baeeb1488f233a9b8b0c388c9de
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/140403
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

4 files changed, 71 insertions, 53 deletions

diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 0d8a943737..f3aa266c50 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -60,7 +60,7 @@ public:
 
         // Checks
         ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
-        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!");
+        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!");
 
         // Print parameter values
         std::cout << common_params << std::endl;
@@ -69,33 +69,40 @@ public:
         std::string  data_path = common_params.data_path;
         unsigned int batches   = 4; /** Number of batches */
 
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Set weights trained layout
+        const DataLayout weights_layout = DataLayout::NCHW;
+
         //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
         graph << common_params.target
               << common_params.fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), common_params.data_type), get_input_accessor(common_params))
+              << InputLayer(input_descriptor, get_input_accessor(common_params))
               << ConvolutionLayer(
                   5U, 5U, 20U,
-                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv1")
               << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               << ConvolutionLayer(
                   5U, 5U, 50U,
-                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv2")
               << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               << FullyConnectedLayer(
                   500U,
-                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
               .set_name("ip1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu")
               << FullyConnectedLayer(
                   10U,
-                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
               .set_name("ip2")
               << SoftmaxLayer().set_name("prob")
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index b30f4c5fd3..a747b3cd11 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -78,12 +78,10 @@ public:
 
         // Create input descriptor
         unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160;
-        TensorShape  tensor_shape = TensorShape(spatial_size, spatial_size, 3U, 1U);
-        if(common_params.data_layout == DataLayout::NHWC)
-        {
-            arm_compute::permute(tensor_shape, arm_compute::PermutationVector(2U, 0U, 1U));
-        }
-        TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(spatial_size, spatial_size, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
 
         // Set graph hints
         graph << common_params.target
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index e677650d04..e23ea65dd7 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -60,7 +60,7 @@ public:
 
         // Checks
         ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
-        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!");
+        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!");
 
         // Print parameter values
         std::cout << common_params << std::endl;
@@ -72,14 +72,21 @@ public:
         const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Set weights trained layout
+        const DataLayout weights_layout = DataLayout::NCHW;
+
+        // Create graph
         graph << common_params.target
               << common_params.fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
-                            get_input_accessor(common_params, std::move(preprocessor)))
+              << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
               // Layer 1
               << ConvolutionLayer(
                   3U, 3U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_1")
@@ -87,7 +94,7 @@ public:
               // Layer 2
               << ConvolutionLayer(
                   3U, 3U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_2")
@@ -96,7 +103,7 @@ public:
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 128U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_1")
@@ -104,7 +111,7 @@ public:
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 128U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_2")
@@ -113,7 +120,7 @@ public:
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_1")
@@ -121,7 +128,7 @@ public:
               // Layer 6
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_2")
@@ -129,7 +136,7 @@ public:
               // Layer 7
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_3")
@@ -138,7 +145,7 @@ public:
               // Layer 8
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_1")
@@ -146,7 +153,7 @@ public:
               // Layer 9
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_2")
@@ -154,7 +161,7 @@ public:
               // Layer 10
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_3")
@@ -163,7 +170,7 @@ public:
               // Layer 11
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_1")
@@ -171,7 +178,7 @@ public:
               // Layer 12
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_2")
@@ -179,7 +186,7 @@ public:
               // Layer 13
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_3")
@@ -188,21 +195,21 @@ public:
               // Layer 14
               << FullyConnectedLayer(
                   4096U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
               .set_name("fc6")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
               // Layer 15
               << FullyConnectedLayer(
                   4096U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
               .set_name("fc7")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
               // Layer 16
               << FullyConnectedLayer(
                   1000U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
               .set_name("fc8")
               // Softmax
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index c7fc333e0a..6cb6b1fae2 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -59,7 +59,7 @@ public:
 
         // Checks
         ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
-        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!");
+        ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!");
 
         // Print parameter values
         std::cout << common_params << std::endl;
@@ -71,21 +71,27 @@ public:
         const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Set weights trained layout
+        const DataLayout weights_layout = DataLayout::NCHW;
+
         graph << common_params.target
               << common_params.fast_math_hint
-              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type),
-                            get_input_accessor(common_params, std::move(preprocessor)))
+              << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
               // Layer 1
               << ConvolutionLayer(
                   3U, 3U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_2")
@@ -94,14 +100,14 @@ public:
               // Layer 2
               << ConvolutionLayer(
                   3U, 3U, 128U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 128U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_2")
@@ -110,28 +116,28 @@ public:
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_4")
@@ -140,28 +146,28 @@ public:
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_4")
@@ -170,28 +176,28 @@ public:
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_4")
@@ -200,21 +206,21 @@ public:
               // Layer 6
               << FullyConnectedLayer(
                   4096U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
               .set_name("fc6")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
               // Layer 7
               << FullyConnectedLayer(
                   4096U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
               .set_name("fc7")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
               // Layer 8
               << FullyConnectedLayer(
                   1000U,
-                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
               .set_name("fc8")
               // Softmax