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 | // |
John Mcloughlin | 083586d | 2023-04-28 18:36:52 +0100 | [diff] [blame] | 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <OpaqueDelegateUtils.hpp> |
| 9 | |
| 10 | namespace armnnOpaqueDelegate |
| 11 | { |
| 12 | |
| 13 | TfLiteStatus ValidateResizeOperator(DelegateData& delegateData, |
| 14 | TfLiteOpaqueContext* tfLiteContext, |
| 15 | const armnn::TensorInfo& inputInfo, |
| 16 | const armnn::TensorInfo& outputInfo, |
| 17 | const armnn::ResizeDescriptor& descriptor) |
| 18 | { |
| 19 | bool isSupported = false; |
| 20 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESIZE", |
| 21 | tfLiteContext, |
| 22 | IsResizeSupported, |
| 23 | delegateData.m_Backends, |
| 24 | isSupported, |
| 25 | armnn::BackendId(), |
| 26 | inputInfo, |
| 27 | outputInfo, |
| 28 | descriptor); |
| 29 | |
| 30 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 31 | } |
| 32 | |
| 33 | TfLiteStatus VisitResizeOperator(DelegateData& delegateData, |
| 34 | TfLiteOpaqueContext* tfLiteContext, |
| 35 | TfLiteOpaqueNode* tfLiteNode, |
| 36 | int nodeIndex, |
| 37 | int32_t resizeOperatorCode) |
| 38 | { |
| 39 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| 40 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 41 | |
| 42 | // Gather input indices and use to get input tensor. |
| 43 | auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| 44 | const int* inputTensors; |
| 45 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 46 | { |
| 47 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 48 | tfLiteContext, |
| 49 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 50 | nodeIndex); |
| 51 | return kTfLiteError; |
| 52 | } |
| 53 | |
| 54 | // The first input contains the data of the image that should be resized [batch, height, width, channels] |
| 55 | const TfLiteOpaqueTensor* tfLiteInputTensor = |
| 56 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| 57 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 58 | { |
| 59 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 60 | tfLiteContext, |
| 61 | "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 62 | resizeOperatorCode, nodeIndex); |
| 63 | return kTfLiteError; |
| 64 | } |
| 65 | |
| 66 | // The second input contains a size tensor. The size tensor contains two integer values |
| 67 | // that describe the new height and width of the image [new_height, new_width] |
| 68 | const TfLiteOpaqueTensor* tfLiteSizeTensor = |
| 69 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); |
| 70 | if (IsDynamicTensor(tfLiteSizeTensor)) |
| 71 | { |
| 72 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 73 | tfLiteContext, |
| 74 | "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 75 | resizeOperatorCode, nodeIndex); |
| 76 | return kTfLiteError; |
| 77 | } |
| 78 | |
| 79 | // Gather output indices and use to get output tensors. |
| 80 | int numOutputs = 0; |
| 81 | const int* outputTensors; |
| 82 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 83 | { |
| 84 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 85 | tfLiteContext, |
| 86 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 87 | nodeIndex); |
| 88 | return kTfLiteError; |
| 89 | } |
| 90 | |
| 91 | // The output tensor should have the shape [batch, new_height, new_width, channels] |
| 92 | const TfLiteOpaqueTensor* tfLiteOutputTensor = |
| 93 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| 94 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 95 | { |
| 96 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 97 | tfLiteContext, |
| 98 | "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 99 | resizeOperatorCode, nodeIndex); |
| 100 | return kTfLiteError; |
| 101 | } |
| 102 | |
| 103 | const armnn::TensorInfo& inputTensorInfo = |
| 104 | GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 105 | const armnn::TensorInfo& outputTensorInfo = |
| 106 | GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| 107 | |
| 108 | std::string layerName("Resize"); |
| 109 | |
| 110 | // Fill descriptor |
| 111 | armnn::ResizeDescriptor desc; |
| 112 | switch (resizeOperatorCode) |
| 113 | { |
| 114 | case kTfLiteBuiltinResizeBilinear: |
| 115 | { |
| 116 | desc.m_Method = armnn::ResizeMethod::Bilinear; |
| 117 | |
| 118 | layerName += "Bilinear:" + std::to_string(nodeIndex); |
| 119 | |
| 120 | TfLiteResizeBilinearParams* bilinearOptions = |
| 121 | reinterpret_cast<TfLiteResizeBilinearParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 122 | |
| 123 | desc.m_AlignCorners = bilinearOptions->align_corners; |
| 124 | desc.m_HalfPixelCenters = bilinearOptions->half_pixel_centers; |
| 125 | break; |
| 126 | } |
| 127 | case kTfLiteBuiltinResizeNearestNeighbor: |
| 128 | { |
| 129 | desc.m_Method = armnn::ResizeMethod::NearestNeighbor; |
| 130 | layerName += "NearestNeighbor:" + std::to_string(nodeIndex); |
| 131 | |
| 132 | TfLiteResizeNearestNeighborParams* nearestNeighborOptions = |
| 133 | reinterpret_cast<TfLiteResizeNearestNeighborParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 134 | |
| 135 | desc.m_AlignCorners = nearestNeighborOptions->align_corners; |
| 136 | desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers; |
| 137 | break; |
| 138 | } |
| 139 | default: |
| 140 | { |
| 141 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 142 | tfLiteContext, |
| 143 | "TfLiteArmnnOpaqueDelegate: Unknown TfLite built in operation for Resize. " |
| 144 | "Given operator: #%d node #%d: ", |
| 145 | resizeOperatorCode, nodeIndex); |
| 146 | return kTfLiteError; |
| 147 | } |
| 148 | } |
| 149 | |
| 150 | // In Arm NN the values of the size input tensor [new_height, new_width] is saved in the operator |
| 151 | // descriptor. We have to read it from the input tensor and write it to the descriptor. |
| 152 | |
| 153 | auto* sizeTensorDataPtr = static_cast<int*>(TfLiteOpaqueTensorData(tfLiteSizeTensor)); |
| 154 | auto sizeTensorNumDimensions = TfLiteOpaqueTensorNumDims(tfLiteSizeTensor); |
| 155 | // The size tensor is only a 1D tensor -> [new_height, new width] |
| 156 | if (sizeTensorNumDimensions != 1) |
| 157 | { |
| 158 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 159 | tfLiteContext, |
| 160 | "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " |
| 161 | "dynamic tensor. Operator: #%d node #%d: ", |
| 162 | resizeOperatorCode, nodeIndex); |
| 163 | return kTfLiteError; |
| 164 | } |
| 165 | |
| 166 | // Get number of values in the size tensor |
| 167 | auto sizeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteSizeTensor,0); |
| 168 | if (sizeTensorNumValues == 0) |
| 169 | { |
| 170 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 171 | tfLiteContext, |
| 172 | "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " |
| 173 | "dynamic tensor. Operator: #%d node #%d: ", |
| 174 | resizeOperatorCode, nodeIndex); |
| 175 | return kTfLiteError; |
| 176 | } |
| 177 | else if (sizeTensorNumValues != 2) |
| 178 | { |
| 179 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 180 | tfLiteContext, |
| 181 | "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation requires to " |
| 182 | "have a dimension of 2 [new_height, new width] but a tensor with a dimension of #%d was given. " |
| 183 | "Operator: #%d node #%d: ", |
| 184 | sizeTensorNumValues, resizeOperatorCode, nodeIndex); |
| 185 | return kTfLiteError; |
| 186 | } |
| 187 | // get size tensor data |
| 188 | std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues); |
| 189 | |
| 190 | desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]); |
| 191 | desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]); |
| 192 | desc.m_DataLayout = armnn::DataLayout::NHWC; |
| 193 | |
| 194 | // No network pointer indicates that only support for this operator should be checked |
| 195 | if (!delegateData.m_Network) |
| 196 | { |
| 197 | return ValidateResizeOperator(delegateData, |
| 198 | tfLiteContext, |
| 199 | inputTensorInfo, |
| 200 | outputTensorInfo, |
| 201 | desc); |
| 202 | } |
| 203 | |
| 204 | |
| 205 | armnn::IConnectableLayer* resizeLayer = nullptr; |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 206 | layerName += ":"; |
| 207 | layerName += nodeIndex; |
| 208 | |
John Mcloughlin | 083586d | 2023-04-28 18:36:52 +0100 | [diff] [blame] | 209 | resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str()); |
| 210 | |
| 211 | armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0); |
| 212 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 213 | |
| 214 | // try to connect the Constant Inputs if there are any |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 215 | if (ProcessInputs(resizeLayer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk) |
John Mcloughlin | 083586d | 2023-04-28 18:36:52 +0100 | [diff] [blame] | 216 | { |
| 217 | return kTfLiteError; |
| 218 | } |
| 219 | |
| 220 | ARMNN_ASSERT(resizeLayer != nullptr); |
| 221 | |
| 222 | return Connect(resizeLayer, tfLiteContext, tfLiteNode, delegateData); |
| 223 | } |
| 224 | |
| 225 | } // namespace armnnOpaqueDelegate |