BugFix: Calculate explicit padding for Delegate Transpose Convolution using output size
* Added fix to Classic and Opaque Delegate which now matches
the TfLiteParser.
* Removed uses of single parameter IsValid function in classic
Convolution visit functions.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I5be91ec25c11354a9b8472d0a429e71e02df6f9c
diff --git a/delegate/classic/src/Convolution.hpp b/delegate/classic/src/Convolution.hpp
index 107d4de..a44f9ee 100644
--- a/delegate/classic/src/Convolution.hpp
+++ b/delegate/classic/src/Convolution.hpp
@@ -46,55 +46,20 @@
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
- if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]]))
+ if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
- if (IsDynamicTensor(tfLiteInputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
+
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
- if(!IsValid(&tfLiteOutputTensor))
+ if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteOutputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
- if(!IsValid(&tfLiteFilterTensor))
+ if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteFilterTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ",
- nodeIndex);
return kTfLiteError;
}
@@ -121,20 +86,8 @@
if(biasEnabled)
{
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
- if(!IsValid(&tfLiteBiasTensor))
+ if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteBiasTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ",
- nodeIndex);
return kTfLiteError;
}
biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor);
@@ -183,11 +136,9 @@
if(filterTensorInfo.IsConstant())
{
- auto filter =
- CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]],
- filterTensorInfo);
+ auto filter = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]], filterTensorInfo);
- armnn::IConnectableLayer *weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
+ armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo);
}
@@ -208,11 +159,9 @@
// The data input can also be constant, so we must check that this is also allocated to an input slot
if(inputTensorInfo.IsConstant())
{
- auto input =
- CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]],
- inputTensorInfo);
+ auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -232,9 +181,9 @@
// No Activation
return kTfLiteOk;
}
+
// Check and Create activation
return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData);
-
}
// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5.
@@ -370,8 +319,7 @@
// which are connected to the Convolution3d layer as inputs.
if (filterTensorInfo.IsConstant())
{
- auto filter = CreateConstTensor(&tfLiteFilterTensor,
- filterTensorInfo);
+ auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo);
armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter);
ARMNN_ASSERT(weightsLayer != nullptr);
@@ -385,8 +333,7 @@
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
if(biasTensorInfo.IsConstant())
{
- auto biases = CreateConstTensor(&tfLiteBiasTensor,
- biasTensorInfo);
+ auto biases = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo);
armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases);
ARMNN_ASSERT(biasLayer != nullptr);
@@ -399,11 +346,9 @@
// The data input can also be constant, so we must check that this is also allocated to an input slot
if(inputTensorInfo.IsConstant())
{
- auto input =
- CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]],
- inputTensorInfo);
+ auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -457,55 +402,20 @@
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
- if(!IsValid(&tfLiteInputTensor))
+ if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
- if (IsDynamicTensor(tfLiteInputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
+
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
- if(!IsValid(&tfLiteOutputTensor))
+ if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteOutputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
- if(!IsValid(&tfLiteFilterTensor))
+ if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteFilterTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ",
- nodeIndex);
return kTfLiteError;
}
@@ -546,20 +456,8 @@
if(biasEnabled)
{
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
- if(!IsValid(&tfLiteBiasTensor))
+ if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteBiasTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ",
- nodeIndex);
return kTfLiteError;
}
biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor);
@@ -616,11 +514,9 @@
// The data input can also be constant, so we must check that this is also allocated to an input slot
if(inputTensorInfo.IsConstant())
{
- auto input =
- CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]],
- inputTensorInfo);
+ auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], inputTensorInfo);
- armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}
@@ -662,98 +558,26 @@
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteOutputShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
- if(!IsValid(&tfLiteOutputShapeTensor))
+ if (!IsValid(tfLiteContext, tfLiteOutputShapeTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
- if (IsDynamicTensor(tfLiteOutputShapeTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
-
- const armnn::TensorInfo outputShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputShapeTensor);
- std::vector<int32_t> outputShape(outputShapeTensorInfo.GetNumElements());
- if (outputShapeTensorInfo.GetDataType() == armnn::DataType::Signed32)
- {
- for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++)
- {
- outputShape[i] = ::tflite::GetTensorData<int32_t>(&tfLiteOutputShapeTensor)[i];
- }
- }
-
- if (outputShapeTensorInfo.GetDataType() == armnn::DataType::QAsymmU8)
- {
- for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++)
- {
- outputShape[i] = ::tflite::GetTensorData<uint8_t>(&tfLiteOutputShapeTensor)[i];
- }
- }
- // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor.
- for (int dimension : outputShape)
- {
- descriptor.m_OutputShape.push_back(static_cast<unsigned int>(dimension));
- }
- descriptor.m_OutputShapeEnabled = true;
const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
- if(!IsValid(&tfLiteInputTensor))
+ if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteInputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
- if(!IsValid(&tfLiteOutputTensor))
+ if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteOutputTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
- if(!IsValid(&tfLiteFilterTensor))
+ if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex))
{
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ",
- operatorCode, nodeIndex);
- return kTfLiteError;
- }
- if (IsDynamicTensor(tfLiteFilterTensor))
- {
- TF_LITE_MAYBE_KERNEL_LOG(
- tfLiteContext,
- "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in operator #%d node #%d: ",
- operatorCode, nodeIndex);
return kTfLiteError;
}
@@ -768,21 +592,75 @@
const unsigned int filterHeight = filterTensorInfo.GetShape()[1];
const unsigned int filterWidth = filterTensorInfo.GetShape()[2];
- // Calculate padding
- CalcPadding(inputHeight,
- filterHeight,
- descriptor.m_StrideY,
- 1, // dilation y
- descriptor.m_PadTop,
- descriptor.m_PadBottom,
- parameters->padding);
- CalcPadding(inputWidth,
- filterWidth,
- descriptor.m_StrideX,
- 1, // dilation x
- descriptor.m_PadLeft,
- descriptor.m_PadRight,
- parameters->padding);
+ // This block determines the output shape of the transpose convolution.
+ // If the output shape tensor is a constant, we can access the data at load time and set the shape of the layer.
+ // If this is not constant, we do not have access to the shape data, so we have to use infer output shape.
+ if (tflite::IsConstantTensor(&tfLiteOutputShapeTensor))
+ {
+ const armnn::TensorInfo outputShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputShapeTensor);
+ std::vector<int32_t> outputShape(outputShapeTensorInfo.GetNumElements());
+ if (outputShapeTensorInfo.GetDataType() == armnn::DataType::Signed32)
+ {
+ for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); ++i)
+ {
+ outputShape[i] = ::tflite::GetTensorData<int32_t>(&tfLiteOutputShapeTensor)[i];
+ }
+ }
+
+ if (outputShapeTensorInfo.GetDataType() == armnn::DataType::QAsymmU8)
+ {
+ for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); ++i)
+ {
+ outputShape[i] = ::tflite::GetTensorData<uint8_t>(&tfLiteOutputShapeTensor)[i];
+ }
+ }
+ // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor.
+ for (int dimension : outputShape)
+ {
+ descriptor.m_OutputShape.push_back(static_cast<unsigned int>(dimension));
+ }
+ descriptor.m_OutputShapeEnabled = true;
+
+ // TfLite uses NHWC tensors
+ const unsigned int outputHeight = descriptor.m_OutputShape[1];
+ const unsigned int outputWidth = descriptor.m_OutputShape[2];
+
+ CalcPadding(inputHeight,
+ filterHeight,
+ descriptor.m_StrideY,
+ 1, // DilationY
+ descriptor.m_PadTop,
+ descriptor.m_PadBottom,
+ parameters->padding,
+ outputHeight);
+
+ CalcPadding(inputWidth,
+ filterWidth,
+ descriptor.m_StrideX,
+ 1, // DilationX
+ descriptor.m_PadLeft,
+ descriptor.m_PadRight,
+ parameters->padding,
+ outputWidth);
+ }
+ else
+ {
+ CalcPadding(inputHeight,
+ filterHeight,
+ descriptor.m_StrideY,
+ 1, // DilationY
+ descriptor.m_PadTop,
+ descriptor.m_PadBottom,
+ parameters->padding);
+
+ CalcPadding(inputWidth,
+ filterWidth,
+ descriptor.m_StrideX,
+ 1, // DilationX
+ descriptor.m_PadLeft,
+ descriptor.m_PadRight,
+ parameters->padding);
+ }
// Set up filter
auto filterTensor = CreateConstTensor(&tfLiteFilterTensor,
@@ -814,11 +692,9 @@
// The data input can be constant, so we must check that this is allocated to an input slot
if(inputTensorInfo.IsConstant())
{
- auto input =
- CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]],
- inputTensorInfo);
+ auto input = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]], inputTensorInfo);
- armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input);
+ armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input);
inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
}