Francis Murtagh | c4fb0dd | 2023-03-16 17:01:56 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
Teresa Charlin | f69ae56 | 2023-04-27 14:42:23 +0100 | [diff] [blame] | 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <OpaqueDelegateUtils.hpp> |
| 9 | |
| 10 | namespace armnnOpaqueDelegate |
| 11 | { |
| 12 | |
| 13 | TfLiteStatus VisitL2NormalizationOperator(DelegateData& delegateData, |
| 14 | TfLiteOpaqueContext* tfLiteContext, |
| 15 | TfLiteOpaqueNode* tfLiteNode, |
| 16 | int nodeIndex, |
| 17 | int32_t tfLiteL2NormalizationOperatorCode) |
| 18 | { |
| 19 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 20 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 21 | |
| 22 | // Gather input indices and use to get input tensor. |
| 23 | int numInputs = 0; |
| 24 | const int* inputTensors; |
| 25 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 26 | { |
| 27 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 28 | tfLiteContext, |
| 29 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 30 | nodeIndex); |
| 31 | return kTfLiteError; |
| 32 | } |
| 33 | // Use input indices to get input tensor. |
| 34 | const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| 35 | if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex)) |
| 36 | { |
| 37 | return kTfLiteError; |
| 38 | } |
| 39 | // Gather output indices and use to get output tensor. |
| 40 | int numOutputs = 0; |
| 41 | const int* outputTensors; |
| 42 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 43 | { |
| 44 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 45 | tfLiteContext, |
| 46 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 47 | nodeIndex); |
| 48 | return kTfLiteError; |
| 49 | } |
| 50 | // Use output indices to get output tensor. |
| 51 | const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| 52 | if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteL2NormalizationOperatorCode, nodeIndex)) |
| 53 | { |
| 54 | return kTfLiteError; |
| 55 | } |
| 56 | |
| 57 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 58 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| 59 | |
| 60 | armnn::L2NormalizationDescriptor descriptor; |
| 61 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 62 | |
| 63 | bool isSupported = false; |
| 64 | armnn::BackendId setBackend; |
| 65 | auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) |
| 66 | { |
| 67 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("L2_NORMALIZATION", |
| 68 | tfLiteContext, |
| 69 | IsL2NormalizationSupported, |
| 70 | delegateData.m_Backends, |
| 71 | isSupported, |
| 72 | setBackend, |
| 73 | inputTensorInfo, |
| 74 | outInfo, |
| 75 | descriptor); |
| 76 | }; |
| 77 | |
| 78 | if (!delegateData.m_Network) |
| 79 | { |
| 80 | validateFunc(outputTensorInfo, isSupported); |
| 81 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 82 | } |
| 83 | |
| 84 | // Add a L2Normalization layer |
| 85 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddL2NormalizationLayer(descriptor); |
| 86 | layer->SetBackendId(setBackend); |
| 87 | ARMNN_ASSERT(layer != nullptr); |
| 88 | |
| 89 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 90 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 91 | |
| 92 | // try to connect the Constant Inputs if there are any |
| 93 | if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) |
| 94 | { |
| 95 | return kTfLiteError; |
| 96 | } |
| 97 | |
| 98 | // Connect |
| 99 | return Connect(layer, tfLiteContext, tfLiteNode, delegateData); |
| 100 | } |
| 101 | |
| 102 | |
| 103 | TfLiteStatus VisitLocalResponseNormalizationOperator(DelegateData& delegateData, |
| 104 | TfLiteOpaqueContext* tfLiteContext, |
| 105 | TfLiteOpaqueNode* tfLiteNode, |
| 106 | int nodeIndex, |
| 107 | int32_t tfLiteNormalizationOperatorCode) |
| 108 | { |
| 109 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 110 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 111 | |
| 112 | // Gather input indices and use to get input tensor. |
| 113 | int numInputs = 0; |
| 114 | const int* inputTensors; |
| 115 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 116 | { |
| 117 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 118 | tfLiteContext, |
| 119 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 120 | nodeIndex); |
| 121 | return kTfLiteError; |
| 122 | } |
| 123 | // Use input indices to get input tensor. |
| 124 | const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| 125 | if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteNormalizationOperatorCode, nodeIndex)) |
| 126 | { |
| 127 | return kTfLiteError; |
| 128 | } |
| 129 | // Gather output indices and use to get output tensor. |
| 130 | int numOutputs = 0; |
| 131 | const int* outputTensors; |
| 132 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 133 | { |
| 134 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 135 | tfLiteContext, |
| 136 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 137 | nodeIndex); |
| 138 | return kTfLiteError; |
| 139 | } |
| 140 | // Use output indices to get output tensor. |
| 141 | const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| 142 | if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteNormalizationOperatorCode, nodeIndex)) |
| 143 | { |
| 144 | return kTfLiteError; |
| 145 | } |
| 146 | |
| 147 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 148 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| 149 | |
| 150 | armnn::NormalizationDescriptor descriptor; |
| 151 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 152 | descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across; |
| 153 | descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 154 | |
| 155 | auto* nodeParams = reinterpret_cast<TfLiteLocalResponseNormParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 156 | descriptor.m_NormSize = nodeParams->radius; |
| 157 | descriptor.m_K = nodeParams->bias; |
| 158 | descriptor.m_Alpha = nodeParams->alpha; |
| 159 | descriptor.m_Beta = nodeParams->beta; |
| 160 | |
| 161 | // ArmNN expects normSize to be the full size of the normalization window |
| 162 | descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize); |
| 163 | |
| 164 | bool isSupported = false; |
| 165 | armnn::BackendId setBackend; |
| 166 | auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) |
| 167 | { |
| 168 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("NORMALIZATION", |
| 169 | tfLiteContext, |
| 170 | IsNormalizationSupported, |
| 171 | delegateData.m_Backends, |
| 172 | isSupported, |
| 173 | setBackend, |
| 174 | inputTensorInfo, |
| 175 | outInfo, |
| 176 | descriptor); |
| 177 | }; |
| 178 | |
| 179 | if (!delegateData.m_Network) |
| 180 | { |
| 181 | validateFunc(outputTensorInfo, isSupported); |
| 182 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 183 | } |
| 184 | |
| 185 | // Add a Normalization layer |
| 186 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddNormalizationLayer(descriptor); |
| 187 | layer->SetBackendId(setBackend); |
| 188 | ARMNN_ASSERT(layer != nullptr); |
| 189 | |
| 190 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 191 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 192 | |
| 193 | // try to connect the Constant Inputs if there are any |
| 194 | if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) |
| 195 | { |
| 196 | return kTfLiteError; |
| 197 | } |
| 198 | |
| 199 | // Connect |
| 200 | return Connect(layer, tfLiteContext, tfLiteNode, delegateData); |
| 201 | } |
| 202 | |
| 203 | } // namespace armnnOpaqueDelegate |