| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| // Contains the implementation of the operations. |
| |
| #define LOG_TAG "Operations" |
| |
| #pragma clang diagnostic push |
| #pragma clang diagnostic ignored "-Wunused-parameter" |
| #pragma clang diagnostic ignored "-Wsign-compare" |
| #pragma clang diagnostic ignored "-Winvalid-partial-specialization" |
| #include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h> |
| #include <tensorflow/lite/kernels/internal/reference/reference_ops.h> |
| #pragma clang diagnostic pop |
| |
| #include <vector> |
| |
| #include "CpuOperationUtils.h" |
| #include "LegacyUtils.h" |
| #include "Operations.h" |
| #include "Reshape.h" |
| #include "Tracing.h" |
| |
| namespace android { |
| namespace nn { |
| |
| bool copyData(const void* inputData, const Shape& inputShape, void* outputData, |
| const Shape& /*outputShape*/) { |
| NNTRACE_COMP("copyData"); |
| size_t count = nonExtensionOperandSizeOfData(inputShape.type, inputShape.dimensions); |
| memcpy(outputData, inputData, count); |
| return true; |
| } |
| |
| template <typename T> |
| bool depthToSpaceGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, |
| T* outputData, const Shape& outputShape) { |
| NNTRACE_COMP("optimized_ops::DepthToSpace"); |
| tflite::optimized_ops::DepthToSpace(inputData, convertShapeToDims(inputShape), blockSize, |
| outputData, convertShapeToDims(outputShape)); |
| return true; |
| } |
| template bool depthToSpaceGeneric<float>(const float* inputData, const Shape& inputShape, |
| int32_t blockSize, float* outputData, |
| const Shape& outputShape); |
| template bool depthToSpaceGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| int32_t blockSize, _Float16* outputData, |
| const Shape& outputShape); |
| template bool depthToSpaceGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, uint8_t* outputData, |
| const Shape& outputShape); |
| template bool depthToSpaceGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, int8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool spaceToDepthGeneric(const T* inputData, const Shape& inputShape, int32_t blockSize, |
| T* outputData, const Shape& outputShape) { |
| NNTRACE_COMP("optimized_ops::SpaceToDepth"); |
| tflite::optimized_ops::SpaceToDepth(inputData, convertShapeToDims(inputShape), blockSize, |
| outputData, convertShapeToDims(outputShape)); |
| return true; |
| } |
| template bool spaceToDepthGeneric<float>(const float* inputData, const Shape& inputShape, |
| int32_t blockSize, float* outputData, |
| const Shape& outputShape); |
| template bool spaceToDepthGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| int32_t blockSize, _Float16* outputData, |
| const Shape& outputShape); |
| template bool spaceToDepthGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, uint8_t* outputData, |
| const Shape& outputShape); |
| template bool spaceToDepthGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| int32_t blockSize, int8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool padGeneric(const T* inputData, const Shape& inputShape, const int32_t* paddings, T padValue, |
| T* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("padGeneric"); |
| |
| // Based on |
| // http://google3/third_party/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h?l=6194&rcl=213557260 |
| |
| // TFLite runtime calls are currently fixed at 4 dimensions. Copy inputs so |
| // we can pad them to 4 dims (yes, we are "padding the padding"). |
| int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape)); |
| NN_OPS_CHECK(numInputDims <= 4); |
| std::vector<int> leftPaddings(4 - numInputDims, 0); |
| std::vector<int> rightPaddings(4 - numInputDims, 0); |
| for (int32_t i = 0; i < numInputDims; ++i) { |
| leftPaddings.push_back(paddings[i * 2]); |
| rightPaddings.push_back(paddings[i * 2 + 1]); |
| } |
| const int leftBPadding = leftPaddings[0]; |
| const int leftHPadding = leftPaddings[1]; |
| const int leftWPadding = leftPaddings[2]; |
| const int leftDPadding = leftPaddings[3]; |
| const int rightBPadding = rightPaddings[0]; |
| const int rightHPadding = rightPaddings[1]; |
| const int rightWPadding = rightPaddings[2]; |
| const int rightDPadding = rightPaddings[3]; |
| |
| const auto extInputShape = |
| tflite::RuntimeShape::ExtendedShape(4, convertShapeToTflshape(inputShape)); |
| const auto extOutputShape = |
| tflite::RuntimeShape::ExtendedShape(4, convertShapeToTflshape(outputShape)); |
| |
| const int outputBatch = extOutputShape.Dims(0); |
| const int outputHeight = extOutputShape.Dims(1); |
| const int outputWidth = extOutputShape.Dims(2); |
| const int outputDepth = extOutputShape.Dims(3); |
| |
| const int inputDepth = extInputShape.Dims(3); |
| |
| NNTRACE_COMP_SWITCH("padGeneric"); |
| |
| if (leftBPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData, padValue, leftBPadding * outputHeight * outputWidth * outputDepth); |
| } |
| for (int outB = leftBPadding; outB < outputBatch - rightBPadding; ++outB) { |
| if (leftHPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, 0, 0, 0), padValue, |
| leftHPadding * outputWidth * outputDepth); |
| } |
| for (int outH = leftHPadding; outH < outputHeight - rightHPadding; ++outH) { |
| if (leftWPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, outH, 0, 0), padValue, |
| leftWPadding * outputDepth); |
| } |
| for (int outW = leftWPadding; outW < outputWidth - rightWPadding; ++outW) { |
| if (leftDPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, outH, outW, 0), |
| padValue, leftDPadding); |
| } |
| |
| T* out = |
| outputData + tflite::Offset(extOutputShape, outB, outH, outW, leftDPadding); |
| const T* in = |
| inputData + tflite::Offset(extInputShape, outB - leftBPadding, |
| outH - leftHPadding, outW - leftWPadding, 0); |
| memcpy(out, in, inputDepth * sizeof(T)); |
| |
| if (rightDPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, outH, outW, |
| outputDepth - rightDPadding), |
| padValue, rightDPadding); |
| } |
| } |
| if (rightWPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, outH, |
| outputWidth - rightWPadding, 0), |
| padValue, rightWPadding * outputDepth); |
| } |
| } |
| if (rightHPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outB, outputHeight - rightHPadding, |
| 0, 0), |
| padValue, rightHPadding * outputWidth * outputDepth); |
| } |
| } |
| if (rightBPadding != 0) { |
| tflite::optimized_ops::TypedMemset<T>( |
| outputData + tflite::Offset(extOutputShape, outputBatch - rightBPadding, 0, 0, 0), |
| padValue, rightBPadding * outputHeight * outputWidth * outputDepth); |
| } |
| |
| return true; |
| } |
| template bool padGeneric<float>(const float* inputData, const Shape& inputShape, |
| const int32_t* paddings, float padValue, float* outputData, |
| const Shape& outputShape); |
| template bool padGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| const int32_t* paddings, _Float16 padValue, _Float16* outputData, |
| const Shape& outputShape); |
| template bool padGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* paddings, uint8_t padValue, uint8_t* outputData, |
| const Shape& outputShape); |
| template bool padGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| const int32_t* paddings, int8_t padValue, int8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool batchToSpaceGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize, |
| T* outputData, const Shape& outputShape) { |
| // Needed by low level implementation, but not really used. |
| tflite::Dims<4> blockSizeDim, cropsDim; |
| const int32 crops[4] = {0, 0, 0, 0}; |
| NNTRACE_COMP("optimized_ops::BatchToSpaceND"); |
| tflite::optimized_ops::BatchToSpaceND(inputData, convertShapeToDims(inputShape), blockSize, |
| blockSizeDim, crops, cropsDim, outputData, |
| convertShapeToDims(outputShape)); |
| return true; |
| } |
| template bool batchToSpaceGeneric<float>(const float* inputData, const Shape& inputShape, |
| const int32_t* blockSize, float* outputData, |
| const Shape& outputShape); |
| template bool batchToSpaceGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| const int32_t* blockSize, _Float16* outputData, |
| const Shape& outputShape); |
| template bool batchToSpaceGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, uint8_t* outputData, |
| const Shape& outputShape); |
| template bool batchToSpaceGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, int8_t* outputData, |
| const Shape& outputShape); |
| |
| template <typename T> |
| bool spaceToBatchGeneric(const T* inputData, const Shape& inputShape, const int32_t* blockSize, |
| const int32_t* padding, const Shape& paddingShape, T* outputData, |
| const Shape& outputShape) { |
| // Needed by low level implementation, but not really used. |
| tflite::RuntimeShape blockSizeDim; |
| NNTRACE_COMP("optimized_ops::SpaceToBatchND"); |
| tflite::optimized_ops::SpaceToBatchND( |
| {.output_offset = outputShape.offset}, convertShapeToTflshape(inputShape), inputData, |
| blockSizeDim, blockSize, convertShapeToTflshape(paddingShape), padding, |
| convertShapeToTflshape(outputShape), outputData); |
| return true; |
| } |
| template bool spaceToBatchGeneric<float>(const float* inputData, const Shape& inputShape, |
| const int32_t* blockSize, const int32_t* padding, |
| const Shape& paddingShape, float* outputData, |
| const Shape& outputShape); |
| template bool spaceToBatchGeneric<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| const int32_t* blockSize, const int32_t* padding, |
| const Shape& paddingShape, _Float16* outputData, |
| const Shape& outputShape); |
| template bool spaceToBatchGeneric<uint8_t>(const uint8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, const int32_t* padding, |
| const Shape& paddingShape, uint8_t* outputData, |
| const Shape& outputShape); |
| template bool spaceToBatchGeneric<int8_t>(const int8_t* inputData, const Shape& inputShape, |
| const int32_t* blockSize, const int32_t* padding, |
| const Shape& paddingShape, int8_t* outputData, |
| const Shape& outputShape); |
| |
| } // namespace nn |
| } // namespace android |
