IVGCVSW-3279 Fix conversion methods for for HAL1.2 CONV2D and DEPTHWISE_CONV2D operators
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Ic5eae083ff2de3ced8d115d1768a97f66625b1c5
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index 11a1cef..b3b1d69 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -131,14 +131,292 @@
switch (operation.type)
{
case V1_2::OperationType::CONV_2D:
- return ConvertConv2d<hal_1_2::HalPolicy>(operation, model, data);
+ return ConvertConv2d(operation, model, data);
case V1_2::OperationType::DEPTHWISE_CONV_2D:
- return ConvertDepthwiseConv2d<hal_1_2::HalPolicy>(operation, model, data);
+ return ConvertDepthwiseConv2d(operation, model, data);
default:
return Fail("%s: Operation type %s not supported in ArmnnDriver",
__func__, toString(operation.type).c_str());
}
}
+bool HalPolicy::ConvertConv2d(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);
+
+ // ArmNN does not currently support non-fixed weights or bias
+ const ConstTensorPin weightsPin =
+ ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data);
+ 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);
+
+ armnn::Convolution2dDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+ ActivationFn activation;
+
+ // Determine whether padding is implicit or explicit
+ bool implicitPadding = operation.inputs.size() == 7 ||
+ (operation.inputs.size() >= 8 &&
+ GetInputOperand<hal_1_2::HalPolicy>(operation, 7, model)->type == OperandType::BOOL);
+
+ if (implicitPadding)
+ {
+ android::nn::PaddingScheme paddingScheme;
+ if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 6, activation, model, data) ||
+ !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 8, desc, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[2];
+ const uint32_t kernelY = weights.GetShape()[1];
+ const uint32_t inputX = inputInfo.GetShape()[2];
+ const uint32_t inputY = inputInfo.GetShape()[1];
+
+ 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);
+
+ desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 7, model, data);
+ }
+ else if (operation.inputs.size() >= 10)
+ {
+ // 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) ||
+ !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 11, desc, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (explicit padding)", __func__);
+ }
+ desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data);
+ }
+ else
+ {
+ return Fail("%s: Unsupported number of operation inputs", __func__);
+ }
+
+ desc.m_BiasEnabled = true;
+ armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
+
+ if (!IsLayerSupportedForAnyBackend(__func__,
+ armnn::IsConvolution2dSupported,
+ data.m_Backends,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer =
+ data.m_Network->AddConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
+
+ if (!startLayer)
+ {
+ return Fail("%s: AddConvolution2dLayer 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);
+}
+
+bool HalPolicy::ConvertDepthwiseConv2d(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);
+
+ // 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::DepthwiseConvolution2dDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+
+ // Determine whether padding is implicit or explicit
+ bool implicitPadding = operation.inputs.size() == 8 ||
+ (operation.inputs.size() >= 9 &&
+ GetInputOperand<hal_1_2::HalPolicy>(operation, 8, model)->type == OperandType::BOOL);
+
+ // Look ahead to find the optional DataLayout, if present
+ const uint32_t dataLayoutFlagIndex = implicitPadding ? 8 : 11;
+ desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, dataLayoutFlagIndex, model, data);
+
+ armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout);
+ unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
+ unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
+ unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
+
+ // Reinterpret weight data as [ H, W, I, M ]
+ armnn::TensorShape weightsShape({ weightsOperand->dimensions[1],
+ weightsOperand->dimensions[2],
+ inputInfo.GetShape()[channelsIndex],
+ weightsOperand->dimensions[3] / inputInfo.GetShape()[channelsIndex] });
+
+ // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
+ const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
+
+ const ConstTensorPin weightsPin =
+ ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation,
+ 1,
+ model,
+ data,
+ HWIMToMIHW,
+ &weightsShape);
+
+ // 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, 3, paddingScheme, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data) ||
+ !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 9, desc, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs (implicit padding)", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[3];
+ const uint32_t kernelY = weights.GetShape()[2];
+ 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, 10, activation, model, data) ||
+ !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 12, desc, 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());
+
+ if (!IsLayerSupportedForAnyBackend(__func__,
+ armnn::IsDepthwiseConvolutionSupported,
+ data.m_Backends,
+ inputInfo,
+ outputInfo,
+ desc,
+ weights.GetInfo(),
+ biases))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer =
+ data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
+ if (!startLayer)
+ {
+ return Fail("%s: AddDepthwiseConvolution2dLayer 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
\ No newline at end of file
diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp
index e6001c4..516e1eb 100644
--- a/1.2/HalPolicy.hpp
+++ b/1.2/HalPolicy.hpp
@@ -27,6 +27,10 @@
using getSupportedOperations_cb = V1_2::IDevice::getSupportedOperations_1_2_cb;
static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
+
+private:
+ static bool ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data);
+ static bool ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data);
};
} // namespace hal_1_2