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author | Finn Williams <Finn.Williams@arm.com> | 2020-11-13 13:23:15 +0000 |
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committer | Finn Williams <Finn.Williams@arm.com> | 2020-11-16 12:31:03 +0000 |
commit | 6f9f99024df71b6b7f7115b58d85eb100c66f3c5 (patch) | |
tree | 412f5c3b4f9ade8fa9cc66cfe84c76b5ac52b461 /delegate/src/DelegateUtils.hpp | |
parent | 72a7cf21453253ed4ac0c17c39773179c857935f (diff) | |
download | armnn-6f9f99024df71b6b7f7115b58d85eb100c66f3c5.tar.gz |
IVGCVSW-5508 Activate compiler warnings in ArmNN TfLite Delegate
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: I1a8e2aa618ff693c61010e6150f3ca41b8ab1201
Diffstat (limited to 'delegate/src/DelegateUtils.hpp')
-rw-r--r-- | delegate/src/DelegateUtils.hpp | 50 |
1 files changed, 29 insertions, 21 deletions
diff --git a/delegate/src/DelegateUtils.hpp b/delegate/src/DelegateUtils.hpp index dcad38503a..e9f579b699 100644 --- a/delegate/src/DelegateUtils.hpp +++ b/delegate/src/DelegateUtils.hpp @@ -70,7 +70,7 @@ TfLiteStatus ValidateNumInputs(TfLiteContext* tfLiteContext, 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 @@ TfLiteStatus ValidateNumOutputs(TfLiteContext* tfLiteContext, 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 @@ TfLiteStatus Connect(armnn::IConnectableLayer* layer, 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 @@ TfLiteStatus Connect(armnn::IConnectableLayer* layer, 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 @@ armnn::IConnectableLayer* BroadcastTensor(const armnn::TensorInfo& inputInfo0, } 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 @@ armnn::IConnectableLayer* BroadcastTensor(const armnn::TensorInfo& inputInfo0, 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 @@ TfLiteStatus FusedActivation(TfLiteContext* tfLiteContext, 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 @@ TfLiteStatus FusedActivation(TfLiteContext* tfLiteContext, // 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 @@ armnn::DataType GetDataType(const TfLiteTensor& tfLiteTensor) 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 @@ armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor, } 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 @@ armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor, 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]); } |