IVGCVSW-5508 Activate compiler warnings in ArmNN TfLite Delegate
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: I1a8e2aa618ff693c61010e6150f3ca41b8ab1201
diff --git a/delegate/src/DelegateUtils.hpp b/delegate/src/DelegateUtils.hpp
index dcad385..e9f579b 100644
--- a/delegate/src/DelegateUtils.hpp
+++ b/delegate/src/DelegateUtils.hpp
@@ -70,7 +70,7 @@
int nodeIndex)
{
auto numInputs = tfLiteNode->inputs->size;
- if (numInputs != expectedSize)
+ if (static_cast<unsigned int >(numInputs) != expectedSize)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of inputs (%d != %d) in node #%d",
@@ -86,7 +86,7 @@
int nodeIndex)
{
auto numOutputs = tfLiteNode->outputs->size;
- if (numOutputs != expectedSize)
+ if (static_cast<unsigned int >(numOutputs) != expectedSize)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of outputs (%d != %d) in node #%d",
@@ -137,7 +137,7 @@
TfLiteNode* tfLiteNode,
armnnDelegate::DelegateData& data)
{
- ARMNN_ASSERT(tfLiteNode->outputs->size == layer->GetNumOutputSlots());
+ ARMNN_ASSERT(static_cast<unsigned int >(tfLiteNode->outputs->size) == layer->GetNumOutputSlots());
// Connect the input slots
for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex)
@@ -152,7 +152,7 @@
for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex)
{
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex);
- data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ data.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot;
}
return kTfLiteOk;
@@ -175,8 +175,8 @@
}
unsigned int biggerInputDimensions = std::max(inputDimensions0, inputDimensions1);
- unsigned int dimDifference =
- std::abs(armnn::numeric_cast<int>(inputDimensions0) - armnn::numeric_cast<int>(inputDimensions1));
+ unsigned int dimDifference = static_cast<unsigned int>(std::abs(armnn::numeric_cast<int>(inputDimensions0) -
+ armnn::numeric_cast<int>(inputDimensions1)));
bool input0IsSmaller = inputDimensions0 < inputDimensions1;
const armnn::TensorInfo& smallInfo = input0IsSmaller ? inputInfo0 : inputInfo1;
@@ -217,22 +217,27 @@
if (input0IsSmaller)
{
- delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0));
+ delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->inputs->data[0])]
+ ->Connect(reshapeLayer->GetInputSlot(0));
reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
- delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(startLayer->GetInputSlot(1));
+ delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->inputs->data[1])]
+ ->Connect(startLayer->GetInputSlot(1));
}
else
{
- delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(reshapeLayer->GetInputSlot(0));
+ delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->inputs->data[1])]
+ ->Connect(reshapeLayer->GetInputSlot(0));
reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(1));
- delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(startLayer->GetInputSlot(0));
+ delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->inputs->data[0])]
+ ->Connect(startLayer->GetInputSlot(0));
}
// Prepare output slots
for (unsigned int outputIndex = 0; outputIndex < startLayer->GetNumOutputSlots(); ++outputIndex)
{
armnn::IOutputSlot& outputSlot = startLayer->GetOutputSlot(outputIndex);
- delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ delegateData.m_OutputSlotForNode
+ [static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot;
}
return reshapeLayer;
@@ -246,8 +251,7 @@
armnnDelegate::DelegateData& data)
{
- armnn::IOutputSlot& outputSlot = prevLayer->GetOutputSlot(outputSlotIndex);
- const armnn::TensorInfo& activationOutputInfo = outputSlot.GetTensorInfo();
+ const armnn::TensorInfo& activationOutputInfo = prevLayer->GetOutputSlot(outputSlotIndex).GetTensorInfo();
armnn::ActivationDescriptor activationDesc;
@@ -314,9 +318,11 @@
// Connect and prepare output slots
for (unsigned int outputIndex = 0; outputIndex < activationLayer->GetNumOutputSlots(); ++outputIndex)
{
- data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]]->Connect(activationLayer->GetInputSlot(0));
+ data.m_OutputSlotForNode[static_cast<unsigned long>(
+ tfLiteNode->outputs->data[outputIndex])]->Connect(activationLayer->GetInputSlot(0));
armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(outputIndex);
- data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ data.m_OutputSlotForNode[static_cast<unsigned long>(
+ tfLiteNode->outputs->data[outputIndex])] = &outputSlot;
}
return kTfLiteOk;
}
@@ -347,7 +353,7 @@
case kTfLiteInt32:
return armnn::DataType::Signed32;
default:
- throw armnn::Exception("TfLiteArmnnDelegate: Unsupported data type: " + tfLiteTensor.type);
+ throw armnn::Exception(&"TfLiteArmnnDelegate: Unsupported data type: " [ tfLiteTensor.type]);
}
}
@@ -364,17 +370,19 @@
}
else
{
- std::vector<unsigned int> tensorDims(tensorDimensionSize);
+ std::vector<unsigned int> tensorDims(static_cast<unsigned int>(tensorDimensionSize));
bool dimensionsSpecificity[5] = { true, true, true, true, true };
- for (unsigned int i = 0; i < tensorDimensionSize; ++i) {
+ for (unsigned int i = 0; i < static_cast<unsigned int>(tensorDimensionSize); ++i) {
auto dim = tfLiteTensor.dims->data[i];
if (dim == 0)
{
dimensionsSpecificity[i] = false;
}
- tensorDims[i] = dim;
+ tensorDims[i] = static_cast<unsigned int>(dim);
}
- armnn::TensorShape tensorShape(tensorDimensionSize, tensorDims.data(), dimensionsSpecificity);
+ armnn::TensorShape tensorShape(static_cast<unsigned int>(tensorDimensionSize),
+ tensorDims.data(),
+ dimensionsSpecificity);
ret = armnn::TensorInfo(tensorShape, type);
}
@@ -387,7 +395,7 @@
if (affineQuantization->scale->size > 1)
{
std::vector<float> quantizationScales;
- for (unsigned int i = 1; i < affineQuantization->scale->size; ++i)
+ for (unsigned int i = 1; i < static_cast<unsigned int>(affineQuantization->scale->size); ++i)
{
quantizationScales.push_back(affineQuantization->scale->data[i]);
}