IVGCVSW-2084: Edit HAL Policy NHWC DepthwiseConvolution
Change-Id: Iee54c42e9abd927447d958377f3aee266a60ca5d
diff --git a/1.0/HalPolicy.cpp b/1.0/HalPolicy.cpp
index 5920c73..d0d298c 100644
--- a/1.0/HalPolicy.cpp
+++ b/1.0/HalPolicy.cpp
@@ -457,13 +457,10 @@
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
- const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
-
// 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 ]
- // but in ArmNN it needs to be [ M, I, H, W ]
+ // which is equal to [ M, H, W, I ]
const Operand* weightsOperand = GetInputOperand(operation, 1, model);
if (weightsOperand == nullptr)
@@ -476,10 +473,11 @@
inputInfo.GetShape()[3],
weightsOperand->dimensions[3] / inputInfo.GetShape()[3] });
- // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
- const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
+ // Swizzle weight data [ H, W, I, M ] -> [ M, H, W, I ]
+ const armnn::PermutationVector HWIMToMHWI = { 1U, 2U, 3U, 0U };
+
ConstTensorPin weightsPin =
- ConvertOperationInputToConstTensorPin(operation, 1, model, data, HWIMToMIHW, &weightsShape);
+ ConvertOperationInputToConstTensorPin(operation, 1, model, data, HWIMToMHWI, &weightsShape);
// Bias is a 1D tensor
ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
@@ -491,19 +489,20 @@
armnn::ConstTensor weights = weightsPin.GetConstTensor();
armnn::ConstTensor bias = biasPin.GetConstTensor();
- SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
+ SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo);
armnn::DepthwiseConvolution2dDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
ActivationFn activation;
if (operation.inputs.size() == 11)
{
- if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
- !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
- !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
+ if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
+ !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
+ !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction(operation, 10, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
@@ -512,18 +511,18 @@
else if (operation.inputs.size() == 8)
{
android::nn::PaddingScheme paddingScheme;
- if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) ||
- !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
- !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
+ if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
!GetInputActivationFunction(operation, 7, activation, model, data))
{
return Fail("%s: Operation has invalid inputs", __func__);
}
- const uint32_t kernelX = weights.GetShape()[3];
- const uint32_t kernelY = weights.GetShape()[2];
- const uint32_t inputX = swizzledInputInfo.GetShape()[3];
- const uint32_t inputY = swizzledInputInfo.GetShape()[2];
+ 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);
@@ -539,8 +538,8 @@
if (!IsLayerSupported(__func__,
armnn::IsDepthwiseConvolutionSupported,
data.m_Compute,
- swizzledInputInfo,
- swizzledOutputInfo,
+ inputInfo,
+ outputInfo,
desc,
weights.GetInfo(),
biases))
@@ -549,18 +548,20 @@
}
armnn::IConnectableLayer* startLayer = data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, bias);
- armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer, data);
-
- if (endLayer != nullptr)
+ if (!startLayer)
{
- armnn::IConnectableLayer& outSwizzleLayer =
- SwizzleInDeswizzleOut(*data.m_Network, input, *startLayer, *endLayer);
- return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+ return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
}
- else
+
+ armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data);
+ if (!endLayer)
{
return Fail("%s: ProcessActivation failed", __func__);
}
+
+ input.Connect(startLayer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
}
bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
