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Diffstat (limited to 'delegate/classic/src/Convolution.hpp')
-rw-r--r--delegate/classic/src/Convolution.hpp58
1 files changed, 39 insertions, 19 deletions
diff --git a/delegate/classic/src/Convolution.hpp b/delegate/classic/src/Convolution.hpp
index a44f9eef1d..cf0134ec1f 100644
--- a/delegate/classic/src/Convolution.hpp
+++ b/delegate/classic/src/Convolution.hpp
@@ -131,14 +131,16 @@ TfLiteStatus VisitConv2dOperator(DelegateData& delegateData,
}
// Set up filter and biases
- armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor);
+ auto layerName = GetLayerName(armnn::LayerType::Convolution2d, nodeIndex);
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor, layerName.c_str());
layer->SetBackendId(setBackend);
- if(filterTensorInfo.IsConstant())
+ if (filterTensorInfo.IsConstant())
{
auto filter = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]], filterTensorInfo);
- armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
+ auto filterName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Filter");
+ armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter, filterName.c_str());
weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo);
}
@@ -149,7 +151,10 @@ TfLiteStatus VisitConv2dOperator(DelegateData& delegateData,
if(biasTensorInfo.IsConstant())
{
auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo);
- armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor);
+
+ auto biasName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Bias");
+ armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor,
+ biasName.c_str());
ARMNN_ASSERT(biasLayer != nullptr);
biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo);
@@ -157,11 +162,12 @@ TfLiteStatus VisitConv2dOperator(DelegateData& delegateData,
}
// The data input can also be constant, so we must check that this is also allocated to an input slot
- if(inputTensorInfo.IsConstant())
+ if (inputTensorInfo.IsConstant())
{
auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ auto inputName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Input");
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input, inputName.c_str());
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -183,7 +189,7 @@ TfLiteStatus VisitConv2dOperator(DelegateData& delegateData,
}
// Check and Create activation
- return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData);
+ return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData, nodeIndex);
}
// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5.
@@ -311,7 +317,8 @@ TfLiteStatus VisitConv3dOperator(DelegateData& delegateData,
return isSupported ? kTfLiteOk : kTfLiteError;
}
- armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor);
+ auto layerName = GetLayerName(armnn::LayerType::Convolution3d, nodeIndex);
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor, layerName.c_str());
layer->SetBackendId(setBackend);
ARMNN_ASSERT(layer != nullptr);
@@ -321,7 +328,8 @@ TfLiteStatus VisitConv3dOperator(DelegateData& delegateData,
{
auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo);
- armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
+ auto filterName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Filter");
+ armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter, filterName.c_str());
ARMNN_ASSERT(weightsLayer != nullptr);
weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
@@ -335,7 +343,8 @@ TfLiteStatus VisitConv3dOperator(DelegateData& delegateData,
{
auto biases = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo);
- armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases);
+ auto biasName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Bias");
+ armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases, biasName.c_str());
ARMNN_ASSERT(biasLayer != nullptr);
biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
@@ -348,7 +357,8 @@ TfLiteStatus VisitConv3dOperator(DelegateData& delegateData,
{
auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ auto inputName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Input");
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input, inputName.c_str());
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -368,7 +378,7 @@ TfLiteStatus VisitConv3dOperator(DelegateData& delegateData,
}
// Check and create activation
- return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData);
+ return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData, nodeIndex);
}
#endif
@@ -485,7 +495,9 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData,
return isSupported ? kTfLiteOk : kTfLiteError;
}
- armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor);
+ auto layerName = GetLayerName(armnn::LayerType::Convolution3d, nodeIndex);
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor,
+ layerName.c_str());
layer->SetBackendId(setBackend);
if(filterTensorInfo.IsConstant())
@@ -493,7 +505,8 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData,
// For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required.
auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo);
- armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
+ auto filterName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Filter");
+ armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter, filterName.c_str());
weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo);
}
@@ -504,7 +517,10 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData,
if(biasTensorInfo.IsConstant())
{
auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo);
- armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor);
+
+ auto biasName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Bias");
+ armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor,
+ biasName.c_str());
ARMNN_ASSERT(biasLayer != nullptr);
biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo);
@@ -516,7 +532,8 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData,
{
auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ auto inputName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Input");
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input, inputName.c_str());
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -537,7 +554,7 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData,
return kTfLiteOk;
}
// Check and create activation
- return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData);
+ return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData, nodeIndex);
}
TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData,
@@ -683,9 +700,11 @@ TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData,
return isSupported ? kTfLiteOk : kTfLiteError;
}
+ auto layerName = GetLayerName(armnn::LayerType::TransposeConvolution2d, nodeIndex);
armnn::IConnectableLayer* layer = delegateData.m_Network->AddTransposeConvolution2dLayer(descriptor,
filterTensor,
- armnn::EmptyOptional());
+ armnn::EmptyOptional(),
+ layerName.c_str());
layer->SetBackendId(setBackend);
ARMNN_ASSERT(layer != nullptr);
@@ -694,7 +713,8 @@ TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData,
{
auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]], inputTensorInfo);
- armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ auto inputName = GetLayerName(armnn::LayerType::Constant, nodeIndex, "Input");
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input, inputName.c_str());
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-06 01:43:18 +0000