diff options
author | Jan Eilers <jan.eilers@arm.com> | 2021-06-02 12:01:25 +0100 |
---|---|---|
committer | Jan Eilers <jan.eilers@arm.com> | 2021-06-16 11:31:42 +0000 |
commit | 53ef79504b4c881c572735393c2eede5fa556c46 (patch) | |
tree | f6e0cd27c4d03075fa154074c5b12d7c8c3149f7 /delegate/src | |
parent | 77fe76bfa8cb798943821d1f3e432c228e1cdee3 (diff) | |
download | armnn-53ef79504b4c881c572735393c2eede5fa556c46.tar.gz |
IVGCVSW-5826 Change weights layout for depthwise to [1,H,W,I*M]
* This change is necessary because tflite uses a [1,H,W,I*M] format
and uses the I*M dimension for per axis quantization. Our previous
layout [M,I,H,W] can't handle the correlating quantization scales.
* Updates Onnx-, TfLiteParser and TfliteDelegate
* Updates the CpuRef, CpuAcc and GpuAcc backends
* Adjusts unit tests
* Adds test to ensure models with old layout can still be read and
executed
* Adds conversion function to previous layout [1,H,W,I*M] --> [M,I,H,W]
which can be used by backend developers
!android-nn-driver:5553
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ifef23368b8c3702cf315a5838d214f7dc13c0152
Diffstat (limited to 'delegate/src')
-rw-r--r-- | delegate/src/Convolution.hpp | 19 | ||||
-rw-r--r-- | delegate/src/DelegateUtils.hpp | 3 |
2 files changed, 5 insertions, 17 deletions
diff --git a/delegate/src/Convolution.hpp b/delegate/src/Convolution.hpp index 6566ffff44..96612e0214 100644 --- a/delegate/src/Convolution.hpp +++ b/delegate/src/Convolution.hpp @@ -289,8 +289,6 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); - // Mappings from TensorflowLite filter tensors to the ArmNN filter tensors (ArmNN weights have to be [M, I, H, W]) - armnn::PermutationVector permutationVector{ 2, 3, 1, 0 }; // [H, W, I, M] -> [M, I, H, W] armnn::TensorInfo filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); // Assuming input is NHWC @@ -301,12 +299,6 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, unsigned int filterHeight = filterTensorInfo.GetShape()[1]; unsigned int filterWidth = filterTensorInfo.GetShape()[2]; - // Reshape weights as [ H, W, I, M ] - filterTensorInfo.SetShape({ filterHeight, - filterWidth, - inputTensorInfo.GetShape()[3], - filterTensorInfo.GetShape()[3] / inputTensorInfo.GetShape()[3] }); - // Calculate padding CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); @@ -340,12 +332,8 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); } - std::vector<uint8_t> swizzledData(filterTensorInfo.GetNumBytes()); - auto filter = - CreateConstTensor(&tfLiteFilterTensor, - filterTensorInfo, - armnn::Optional<armnn::PermutationVector&>(permutationVector), - swizzledData.data()); + // For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required. + auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo); if (!delegateData.m_Network) { @@ -369,8 +357,7 @@ TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, { auto biases = CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]], - biasTensorInfo, - armnn::Optional<armnn::PermutationVector&>()); + biasTensorInfo); layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor, filter, armnn::Optional<armnn::ConstTensor>(biases)); diff --git a/delegate/src/DelegateUtils.hpp b/delegate/src/DelegateUtils.hpp index 5dea567761..b04baac36e 100644 --- a/delegate/src/DelegateUtils.hpp +++ b/delegate/src/DelegateUtils.hpp @@ -472,7 +472,8 @@ armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor) armnn::ConstTensor CreateConstTensor(const TfLiteTensor* tfLiteTensor, armnn::TensorInfo& tensorInfo, - armnn::Optional<armnn::PermutationVector&> permutationVector, + armnn::Optional<armnn::PermutationVector&> + permutationVector = armnn::EmptyOptional(), void* permutationData = nullptr) { if (tfLiteTensor->allocation_type != kTfLiteMmapRo) |