MLCE-825: Give reason when workload unsupported for Non Constant Weights/Bias

 * BackendHelper.cpp IsXXXLayerSupported doesn't get as far as Neon/Cl
   Validate functions where arm_compute::Status is returned.
 * Conv2d, Depthwise, DilatedDepthwise and FullyConnected
 * Tidy up if() -> if ()
 * Clean up logic in FullyConnected so that isLayerSupported gets called

Signed-off-by: Francis Murtagh <francis.murtagh@arm.com>

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>

Change-Id: I5da1a882f4a2f55e90aa984b2b9548a847cb3a2d

1 files changed, 0 insertions, 9 deletions

diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
index 762645bfba..6b0a3b8352 100644
--- a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
+++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
@@ -28,15 +28,6 @@ arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input,
                                                     bool isFastMathEnabled,
                                                     const ActivationDescriptor* activationDescriptor)
 {
-    // The arm_compute::CLConvolutionLayer supports both const and non const
-    // weights. However, in the case of non const weights we'd have to call
-    // prepare or configure for each inference which we're not setup to do just yet.
-    if (!weights.IsConstant())
-    {
-        return arm_compute::Status{arm_compute::ErrorCode::RUNTIME_ERROR,
-                                   "ArmNN ClConvolution2dWorkload does not support non constant weights."};
-    }
-
     const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
     const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
     arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);