| // |
| // Copyright © 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| // Copyright © 2020, 2023 The TensorFlow Authors. All Rights Reserved. |
| // SPDX-License-Identifier: Apache-2.0 |
| // |
| |
| #include <numeric> |
| #include "ResizeOperator.hpp" |
| |
| // This function is paraphrased from: |
| // tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc from function convertResizeOp |
| // tensorflow/lite/kernels/internal/reference/resize_utils.h |
| TosaSerializationBasicBlock* ConvertResizeToTosaOperator(const Layer* layer, |
| const std::vector<const TensorInfo*>& inputs, |
| const std::vector<const TensorInfo*>& outputs, |
| const ResizeDescriptor* resizeDescriptor) |
| { |
| ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( inputs.size() == 1, |
| "ConvertResizeToTosaOperator: Resize must have only one input." ); |
| ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( resizeDescriptor->m_DataLayout == DataLayout::NHWC, |
| "ConvertResizeToTosaOperator: NCHW not supported."); |
| |
| ResizeMode mode; |
| if (resizeDescriptor->m_Method == ResizeMethod::NearestNeighbor) |
| { |
| mode = tosa::ResizeMode_NEAREST; |
| } |
| else if (resizeDescriptor->m_Method == ResizeMethod::Bilinear) |
| { |
| mode = tosa::ResizeMode_BILINEAR; |
| throw armnn::InvalidArgumentException("ConvertResizeToTosaOperator: Unimplemented Resize method."); |
| } |
| else |
| { |
| throw armnn::InvalidArgumentException("ConvertResizeToTosaOperator: Unsupported Resize method."); |
| } |
| |
| std::string inputName = std::string("input_"); |
| std::string outputName = std::string("output0_"); |
| std::string blockName = std::string("Op_RESIZE_block_") + GetUniqueTosaMappingID(); |
| |
| // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| if(layer != nullptr) |
| { |
| inputName = GenerateUniqueInputName(layer->GetInputSlot(0)); |
| outputName = GenerateUniqueOutputName(*layer); |
| } |
| |
| int32_t inputHeight = static_cast<int32_t>(inputs[0]->GetShape()[1]); |
| int32_t inputWidth = static_cast<int32_t>(inputs[0]->GetShape()[2]); |
| |
| int32_t outputHeight = static_cast<int32_t>(resizeDescriptor->m_TargetHeight); |
| int32_t outputWidth = static_cast<int32_t>(resizeDescriptor->m_TargetWidth); |
| bool alignCorners = resizeDescriptor->m_AlignCorners; |
| bool halfPixel = resizeDescriptor->m_HalfPixelCenters; |
| |
| // Go from ArmNN parameters (outputShape, halfPixel and alignedCorners) |
| // to TOSA parameters (scale, offset and border) |
| // Align corners sets the scaling ratio to (O - 1)/(I - 1) rather than O / I. |
| auto preprocessResizeParameters = [&](int inputSize, int outputSize, int& scale_n, int& scale_d, int& offset) |
| { |
| // Dimension is length 1, we are just sampling from one value. |
| if (inputSize == 1) |
| { |
| scale_n = outputSize; |
| scale_d = 1; |
| offset = 0; |
| return; |
| } |
| |
| // Apply if aligned and capable to be aligned. |
| // Align corners sets the scaling ratio to (OH - 1)/(IH - 1) rather than OH / IH. Same for width. |
| bool applyAligned = alignCorners && (outputSize > 1); |
| scale_n = applyAligned ? (outputSize - 1) : outputSize; |
| scale_d = applyAligned ? (inputSize - 1) : inputSize; |
| |
| // Simplify the scales, make sure they are even values. |
| int gcd = std::gcd(scale_n, scale_d); |
| scale_n = 2 * scale_n / gcd; |
| scale_d = 2 * scale_d / gcd; |
| |
| // If half pixel centers then input and output sampling positions are offset by 1/2 pixel. |
| offset = halfPixel ? (scale_d / 2 - scale_n / 2) : 0; |
| |
| // Reduce the scaling ratio if possible, we know scale_n and scale_d are even |
| if ((offset & 1) == 0) |
| { |
| scale_n /= 2; |
| scale_d /= 2; |
| offset /= 2; |
| } |
| }; |
| |
| int scale_y_n, scale_y_d, offset_y; |
| int scale_x_n, scale_x_d, offset_x; |
| preprocessResizeParameters(inputHeight, outputHeight, scale_y_n, scale_y_d, offset_y); |
| preprocessResizeParameters(inputWidth, outputWidth, scale_x_n, scale_x_d, offset_x); |
| |
| int border_y = scale_y_d * (outputHeight - 1) - scale_y_n * (inputHeight - 1) + offset_y; |
| int border_x = scale_x_d * (outputWidth - 1) - scale_x_n * (inputWidth - 1) + offset_x; |
| |
| // [scale_y_n, scale_y_d, scale_x_n, scale_x_d] |
| std::vector<int16_t> scale = { static_cast<int16_t>(scale_y_n), |
| static_cast<int16_t>(scale_y_d), |
| static_cast<int16_t>(scale_x_n), |
| static_cast<int16_t>(scale_x_d) }; |
| |
| // [offset_y, offset_x] |
| std::vector<int16_t> offset = { static_cast<int16_t>(offset_y), |
| static_cast<int16_t>(offset_x) }; |
| // [border_y, border_x] |
| std::vector<int16_t> border = { static_cast<int16_t>(border_y), |
| static_cast<int16_t>(border_x) }; |
| |
| auto isInt16Range = [](int x) |
| { |
| return (x <= std::numeric_limits<int16_t>::max()) && (x >= std::numeric_limits<int16_t>::min()); |
| }; |
| |
| if (inputs[0]->IsQuantized()) |
| { |
| // It isn't commonly seen these numbers aren't fit within 16 bits, and won't match TFLite reference. |
| if (!isInt16Range(scale_y_n) || !isInt16Range(scale_y_d) || |
| !isInt16Range(scale_x_n) || !isInt16Range(scale_x_d) || |
| !isInt16Range(offset_y) || !isInt16Range(offset_x) || |
| !isInt16Range(border_y) || !isInt16Range(border_x)) |
| { |
| throw armnn::Exception("ConvertResizeToTosaOperator: stride or offset out of 16 bit range"); |
| } |
| } |
| |
| TosaResizeAttribute resizeAttribute(scale, offset, border, mode); |
| |
| auto* op = new TosaSerializationOperator(Op_RESIZE, |
| Attribute_ResizeAttribute, |
| &resizeAttribute, |
| {inputName}, |
| {outputName}); |
| |
| std::vector<TosaSerializationTensor*> tensors; |
| |
| // Only add input tensors if connected layer is an input layer. |
| // As intermediate or constant tensors will be created separately. |
| // There also can't be duplicate tensor. |
| if(inputName.find("input_") != std::string::npos) |
| { |
| std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape()); |
| DType inputDType = ArmNNToDType(inputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(inputName, inputShape, inputDType, {})); |
| } |
| |
| std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape()); |
| DType outputDType = ArmNNToDType(outputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(outputName, outputShape, outputDType, {})); |
| |
| // operatorInputNames/operatorOutputNames ends up being the same as |
| // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |
| return new TosaSerializationBasicBlock(blockName, // name |
| mainName, // region name |
| {op}, // operators |
| tensors, // tensors |
| {inputName}, // inputs |
| {outputName}); // outputs |
| } |