IVGCVSW-3166 Add support for TransposeConv2D to 1.2 HalPolicy
Signed-off-by: David Monahan <david.monahan@arm.com>
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I7b855efaddb18e8f184f4b85196b3b95b69bca6e
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index dee2175..64d808b 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -158,6 +158,8 @@
return ConvertResize(operation, model, data, armnn::ResizeMethod::Bilinear);
case V1_2::OperationType::RESIZE_NEAREST_NEIGHBOR:
return ConvertResize(operation, model, data, armnn::ResizeMethod::NearestNeighbor);
+ case V1_2::OperationType::TRANSPOSE_CONV_2D:
+ return ConvertTransposeConvolution2d(operation, model, data);
case V1_2::OperationType::SOFTMAX:
return ConvertSoftmax(operation, model, data);
case V1_2::OperationType::SPACE_TO_DEPTH:
@@ -1485,5 +1487,157 @@
SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 3, *layer, 3, model, data));
}
+bool HalPolicy::ConvertTransposeConvolution2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data);
+
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model);
+
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ // ArmNN does not currently support non-fixed weights or bias
+ // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
+ const Operand* weightsOperand = GetInputOperand<hal_1_2::HalPolicy>(operation, 1, model);
+
+ if (weightsOperand == nullptr)
+ {
+ return Fail("%s: Operand is invalid", __func__);
+ }
+ armnn::TransposeConvolution2dDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+
+ // Determine whether padding is implicit or explicit
+ bool implicitPadding = operation.inputs.size() == 9;
+
+ if (implicitPadding )
+ {
+ desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 8, model, data);
+ }
+ else
+ {
+ desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data);
+ }
+
+ armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
+ unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
+ unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
+
+ const armnn::PermutationVector OHWIToOIHW = {0, 2, 3, 1};
+
+ // The shape of the weight is [depth_out, filter_height, filter_width, depth_in].
+ // We have to permute it to OIHW if the data layout is NCHW.
+ const ConstTensorPin weightsPin = (desc.m_DataLayout == armnn::DataLayout::NCHW) ?
+ ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data, OHWIToOIHW) :
+ ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
+
+ // Bias is a 1D tensor
+ const ConstTensorPin biasPin =
+ ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data);
+
+ if (!weightsPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid weights", __func__);
+ }
+
+ if (!biasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid biases", __func__);
+ }
+
+ armnn::ConstTensor weights = weightsPin.GetConstTensor();
+ armnn::ConstTensor bias = biasPin.GetConstTensor();
+ SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
+
+ ActivationFn activation;
+
+ if (implicitPadding)
+ {
+ android::nn::PaddingScheme paddingScheme;
+ if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 4, paddingScheme, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[widthIndex];
+ const uint32_t kernelY = weights.GetShape()[heightIndex];
+ const uint32_t inputX = inputInfo.GetShape()[widthIndex];
+ const uint32_t inputY = inputInfo.GetShape()[heightIndex];
+
+ CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
+ CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
+ }
+ else if (operation.inputs.size() == 11)
+ {
+ // explicit padding
+ if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 9, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (explicit padding)", __func__);
+ }
+ }
+ else
+ {
+ return Fail("%s: Unsupported number of operation inputs", __func__);
+ }
+
+ desc.m_BiasEnabled = true;
+ armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsTransposeConvolution2dSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer =
+ data.m_Network->AddTransposeConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
+ if (!startLayer)
+ {
+ return Fail("%s: AddTransposeConvolution2dLayer failed", __func__);
+ }
+
+ armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
+ if (!endLayer)
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+
+ input.Connect(startLayer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data);
+}
+
} // namespace hal_1_2
} // namespace armnn_driver
diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp
index a51b9a6..5d6158a 100644
--- a/1.2/HalPolicy.hpp
+++ b/1.2/HalPolicy.hpp
@@ -73,6 +73,8 @@
static bool ConvertTanH(const Operation& operation, const Model& model, ConversionData& data);
static bool ConvertLstm(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertTransposeConvolution2d(const Operation& operation, const Model& model, ConversionData& data);
};
} // namespace hal_1_2
diff --git a/NnapiSupport.txt b/NnapiSupport.txt
index 47c5149..4a49430 100644
--- a/NnapiSupport.txt
+++ b/NnapiSupport.txt
@@ -57,6 +57,7 @@
QUANTIZE (FLOAT32,QUANT8_ASYMM)
RESIZE_NEAREST_NEIGHBOR (FLOAT32,QUANT8_ASYMM)
SOFTMAX (FLOAT32,QUANT8_ASYMM)
+TRANSPOSE_CONV_2D (FLOAT32,QUANT8_ASYMM)
--- Unsupported operators ---
@@ -74,7 +75,6 @@
CONCATENATION
LSTM
QUANTIZED_16BIT_LSTM
-TRANSPOSE_CONV_2D
Where operations are not supported by the ArmNN Android NN Driver, the driver indicates this to the framework
appropriately and the framework implements those operations using a CPU implementation.