COMPMID-550: Adds support for branches.

Change-Id: I778007c9221ce3156400284c4039b90245eb2b7f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90043
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 22 insertions, 15 deletions

diff --git a/src/graph/nodes/BatchNormalizationLayer.cpp b/src/graph/nodes/BatchNormalizationLayer.cpp
index a6a990fd3f..25e9e9bffb 100644
--- a/src/graph/nodes/BatchNormalizationLayer.cpp
+++ b/src/graph/nodes/BatchNormalizationLayer.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph;
 namespace
 {
 template <typename BatchBatchNormalizationLayer, typename TensorType, TargetHint target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
 {
     auto norm = arm_compute::support::cpp14::make_unique<BatchBatchNormalizationLayer>();
     norm->configure(
@@ -52,58 +52,65 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe
 }
 
 template <TargetHint                    target_hint>
-std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon);
+std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon);
 
 template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma,
+                                                                        float epsilon)
 {
     return instantiate_function<arm_compute::CLBatchNormalizationLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, output, mean, var, beta, gamma, epsilon);
 }
 
 template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *output, Tensor &mean, Tensor &var, Tensor &beta, Tensor &gamma, float epsilon)
 {
     return instantiate_function<arm_compute::NEBatchNormalizationLayer, arm_compute::ITensor, TargetHint::NEON>(input, output, mean, var, beta, gamma, epsilon);
 }
 } // namespace
 
-std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output)
+std::unique_ptr<arm_compute::IFunction> BatchNormalizationLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
 {
+    ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr);
+
     std::unique_ptr<arm_compute::IFunction> func;
     _target_hint = ctx.hints().target_hint();
 
-    unsigned int batch_norm_size = input->info()->dimension(2);
+    arm_compute::ITensor *in  = input->tensor();
+    arm_compute::ITensor *out = output->tensor();
+
+    unsigned int batch_norm_size = in->info()->dimension(2);
     if(_mean.tensor() == nullptr)
     {
-        _mean.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+        _mean.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
     }
     if(_var.tensor() == nullptr)
     {
-        _var.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+        _var.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
     }
     if(_beta.tensor() == nullptr)
     {
-        _beta.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+        _beta.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
     }
     if(_gamma.tensor() == nullptr)
     {
-        _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()));
+        _gamma.set_info(TensorInfo(TensorShape(batch_norm_size), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()));
     }
 
     if(_target_hint == TargetHint::OPENCL)
     {
-        func = instantiate<TargetHint::OPENCL>(input, output, _mean, _var, _beta, _gamma, _epsilon);
+        func = instantiate<TargetHint::OPENCL>(in, out, _mean, _var, _beta, _gamma, _epsilon);
         ARM_COMPUTE_LOG("Instantiating CLBatchNormalizationLayer");
     }
     else
     {
-        func = instantiate<TargetHint::NEON>(input, output, _mean, _var, _beta, _gamma, _epsilon);
+        func = instantiate<TargetHint::NEON>(in, out, _mean, _var, _beta, _gamma, _epsilon);
         ARM_COMPUTE_LOG("Instantiating NEBatchNormalizationLayer");
     }
 
-    ARM_COMPUTE_LOG(" Data Type: " << input->info()->data_type()
-                    << " Input shape: " << input->info()->tensor_shape()
-                    << " Output shape: " << output->info()->tensor_shape()
+    ARM_COMPUTE_LOG(" Data Type: " << in->info()->data_type()
+                    << " Input shape: " << in->info()->tensor_shape()
+                    << " Output shape: " << out->info()->tensor_shape()
                     << std::endl);
 
     return func;