1 files changed, 30 insertions, 8 deletions

diff --git a/src/armnn/layers/Convolution2dLayer.cpp b/src/armnn/layers/Convolution2dLayer.cpp
index df971a517d..2fcc4aa755 100644
--- a/src/armnn/layers/Convolution2dLayer.cpp
+++ b/src/armnn/layers/Convolution2dLayer.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -63,15 +63,30 @@ Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
 
 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    ARMNN_ASSERT(inputShapes.size() == 2);
+    if (inputShapes.size() != 2)
+    {
+        throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
+                               "\" - should be \"2\".");
+    }
+
     const TensorShape& inputShape = inputShapes[0];
     const TensorShape filterShape = inputShapes[1];
 
     // If we support multiple batch dimensions in the future, then this assert will need to change.
-    ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
+    if (inputShape.GetNumDimensions() != 4)
+    {
+        throw armnn::Exception("Convolutions will always have 4D input.");
+    }
+
+    if (m_Param.m_StrideX == 0)
+    {
+        throw armnn::Exception("m_StrideX cannot be 0.");
+    }
 
-    ARMNN_ASSERT( m_Param.m_StrideX > 0);
-    ARMNN_ASSERT( m_Param.m_StrideY > 0);
+    if (m_Param.m_StrideY == 0)
+    {
+        throw armnn::Exception("m_StrideY cannot be 0.");
+    }
 
     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
@@ -107,14 +122,21 @@ void Convolution2dLayer::ValidateTensorShapesFromInputs()
 
     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
-    ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(),
-                     "Convolution2dLayer: Weights should be connected to input slot 1.");
+    if (!GetInputSlot(1).GetConnection())
+    {
+        throw armnn::NullPointerException("Convolution2dLayer: Weights should be connected to input slot 1.");
+    }
 
     std::vector<TensorShape> inferredShapes = InferOutputShapes({
              GetInputSlot(0).GetTensorInfo().GetShape(),
              GetInputSlot(1).GetTensorInfo().GetShape() });
 
-    ARMNN_ASSERT(inferredShapes.size() == 1);
+    if (inferredShapes.size() != 1)
+    {
+        throw armnn::Exception("inferredShapes has "
+                               + std::to_string(inferredShapes.size()) +
+                               " elements - should only have 1.");
+    }
 
     ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer");
 }