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author | Matthew Sloyan <matthew.sloyan@arm.com> | 2020-11-09 12:25:05 +0000 |
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committer | Jim Flynn <jim.flynn@arm.com> | 2020-11-10 16:50:49 +0000 |
commit | 0d35a93d68e321e8c4b16baa8b9754b98cc9faf3 (patch) | |
tree | d4143415dbd8d7ea2b65b802fe9e18ead9a09e75 /delegate/src/Quantization.hpp | |
parent | 8b9858d891439fd1b0710e5d245e2116a3b88d30 (diff) | |
download | armnn-0d35a93d68e321e8c4b16baa8b9754b98cc9faf3.tar.gz |
IVGCVSW-5398 TfLiteDelegate: Implement the Quantization operators
* Enabled quantization operators DEQUANTIZE and QUANTIZE.
* Implemented unit tests for quantization operators.
* Added utils function for checking if affine quantization.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I84b5c75bda629d9234f5ed198b04f527705a54aa
Diffstat (limited to 'delegate/src/Quantization.hpp')
-rw-r--r-- | delegate/src/Quantization.hpp | 132 |
1 files changed, 122 insertions, 10 deletions
diff --git a/delegate/src/Quantization.hpp b/delegate/src/Quantization.hpp index 31196233f9..4adbd11616 100644 --- a/delegate/src/Quantization.hpp +++ b/delegate/src/Quantization.hpp @@ -13,22 +13,134 @@ namespace armnnDelegate { +TfLiteStatus VisitDequantizeOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteDequantizeOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + tfLiteDequantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + tfLiteDequantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsDequantizeSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfo); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* dequantizeLayer = delegateData.m_Network->AddDequantizeLayer(); + ARMNN_ASSERT(dequantizeLayer != nullptr); + + armnn::IOutputSlot& outputSlot = dequantizeLayer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + return Connect(dequantizeLayer, tfLiteNode, delegateData); +} + TfLiteStatus VisitQuantizeOperator(DelegateData& delegateData, TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode, int nodeIndex, - int32_t operatorCode) + int32_t tfLiteQuantizeOperatorCode) { - return kTfLiteError; -} + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); -TfLiteStatus VisitDequantizeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - return kTfLiteError; + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + // Only affine per-layer quantization is supported. + if (!IsAffineQuantization(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Only affine per-layer quantization is supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsQuantizeSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfo); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* quantizeLayer = delegateData.m_Network->AddQuantizeLayer(); + ARMNN_ASSERT(quantizeLayer != nullptr); + + armnn::IOutputSlot& outputSlot = quantizeLayer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + return Connect(quantizeLayer, tfLiteNode, delegateData); } } // namespace armnnDelegate |