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639 lines
31 KiB
639 lines
31 KiB
4 months ago
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/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "Operations"
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#include <algorithm>
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#include <cfloat>
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#include <cmath>
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#include <memory>
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#include <vector>
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#include "OperationResolver.h"
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#include "Tracing.h"
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#include <tensorflow/lite/kernels/internal/common.h>
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#include "CpuOperationUtils.h"
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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namespace android {
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namespace nn {
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namespace transpose_conv_2d {
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constexpr char kOperationName[] = "TRANSPOSE_CONV_2D";
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kFilterTensor = 1;
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constexpr uint32_t kBiasTensor = 2;
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constexpr uint32_t kNumInputs1 = 9;
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constexpr uint32_t kNumInputs2 = 11;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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namespace {
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// If possible we will use this static buffer for the tensor.
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constexpr size_t kStaticBufferSize = 1605632;
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char static_scratch_buffer[kStaticBufferSize];
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// executionMutex is used to protect concurrent access of the static_scratch_buffer.
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// std::mutex is safe for pthreads on Android.
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std::mutex executionMutex;
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struct TransposeConv2dParam {
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int32_t paddingLeft, paddingRight;
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int32_t paddingTop, paddingBottom;
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int32_t strideWidth, strideHeight;
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int32_t activation;
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bool useNchw = false;
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bool initialize(const IOperationExecutionContext* context) {
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uint32_t inCount = context->getNumInputs();
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int32_t paddingImplicit = 0;
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if (inCount == 9) {
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paddingImplicit = context->getInputValue<int32_t>(4);
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strideWidth = context->getInputValue<int32_t>(5);
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strideHeight = context->getInputValue<int32_t>(6);
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activation = context->getInputValue<int32_t>(7);
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useNchw = context->getInputValue<bool>(8);
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Shape filterShape = context->getInputShape(kFilterTensor);
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int32_t filterWidth = getSizeOfDimension(filterShape, 2);
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int32_t filterHeight = getSizeOfDimension(filterShape, 1);
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NN_RET_CHECK_EQ(getNumberOfDimensions(context->getInputShape(3)), 1);
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NN_RET_CHECK_EQ(getSizeOfDimension(context->getInputShape(3), 0), 4);
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const int32_t* outputShapeData = context->getInputBuffer<int32_t>(3);
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int32_t outputWidth = useNchw ? outputShapeData[3] : outputShapeData[2];
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int32_t outputHeight = useNchw ? outputShapeData[2] : outputShapeData[1];
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calculateExplicitPaddingTransposeConv(outputWidth, strideWidth, filterWidth,
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paddingImplicit, &paddingLeft, &paddingRight);
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calculateExplicitPaddingTransposeConv(outputHeight, strideHeight, filterHeight,
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paddingImplicit, &paddingTop, &paddingBottom);
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} else if (inCount == 11) {
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paddingLeft = context->getInputValue<int32_t>(3);
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paddingRight = context->getInputValue<int32_t>(4);
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paddingTop = context->getInputValue<int32_t>(5);
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paddingBottom = context->getInputValue<int32_t>(6);
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strideWidth = context->getInputValue<int32_t>(7);
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strideHeight = context->getInputValue<int32_t>(8);
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activation = context->getInputValue<int32_t>(9);
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useNchw = context->getInputValue<bool>(10);
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported input spec for operation " << kOperationName;
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}
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// paddingRight and paddingBottom in transpose conv may be less than 0 to resolve the
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// ambiguous output shape issue in the case of stride > 1.
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NN_RET_CHECK_GE(paddingLeft, 0);
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NN_RET_CHECK_GE(paddingTop, 0);
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NN_RET_CHECK_GT(strideWidth, 0);
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NN_RET_CHECK_GT(strideHeight, 0);
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NN_RET_CHECK_GE(activation, 0);
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return true;
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}
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};
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#define ANDROID_NN_TRANSPOSE_CONV_PARAMETERS \
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uint32_t numBatches = getSizeOfDimension(inputShape, 0); \
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uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \
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uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \
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uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \
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uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \
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uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \
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uint32_t outputHeight = getSizeOfDimension(outputShape, 1); \
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uint32_t outputWidth = getSizeOfDimension(outputShape, 2); \
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uint32_t outputDepth = getSizeOfDimension(outputShape, 3); \
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int32_t paddingLeft = param.paddingLeft, paddingRight = param.paddingRight; \
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int32_t paddingTop = param.paddingTop, paddingBottom = param.paddingBottom; \
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int32_t strideWidth = param.strideWidth, strideHeight = param.strideHeight; \
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int32_t activation = param.activation;
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bool transposeConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData,
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const Shape& filterShape, const float* biasData, const Shape& biasShape,
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const TransposeConv2dParam& param, float* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("transposeConvFloat32");
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ANDROID_NN_TRANSPOSE_CONV_PARAMETERS
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float outputActivationMin = 0.0f, outputActivationMax = 0.0f;
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CalculateActivationRangeFloat(activation, &outputActivationMin, &outputActivationMax);
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memset(outputData, 0, getNumberOfElements(outputShape) * sizeof(float));
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const float* inputBase = inputData;
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float* outputBase = outputData;
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for (uint32_t b = 0; b < numBatches; b++) {
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for (uint32_t h = 0; h < inputHeight; h++) {
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for (uint32_t w = 0; w < inputWidth; w++) {
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int32_t wOutputOrigin = static_cast<int32_t>(w) * strideWidth - paddingLeft;
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int32_t hOutputOrigin = static_cast<int32_t>(h) * strideHeight - paddingTop;
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const float* filterBase = filterData;
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for (uint32_t k = 0; k < outputDepth; k++) {
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for (uint32_t i = 0; i < filterHeight; i++) {
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for (uint32_t j = 0; j < filterWidth; j++, filterBase += inputDepth) {
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int32_t hOutput = hOutputOrigin + static_cast<int32_t>(i);
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int32_t wOutput = wOutputOrigin + static_cast<int32_t>(j);
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if (hOutput >= 0 && hOutput < static_cast<int32_t>(outputHeight) &&
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wOutput >= 0 && wOutput < static_cast<int32_t>(outputWidth)) {
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for (uint32_t d = 0; d < inputDepth; d++) {
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uint32_t outputIndex = hOutput * outputWidth * outputDepth +
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wOutput * outputDepth + k;
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outputBase[outputIndex] += inputBase[d] * filterBase[d];
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}
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}
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}
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}
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}
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inputBase += inputDepth;
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}
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}
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outputBase += outputHeight * outputWidth * outputDepth;
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}
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const uint32_t outerSize = numBatches * outputHeight * outputWidth;
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float* outPtr = outputData;
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for (uint32_t i = 0; i < outerSize; i++) {
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for (uint32_t d = 0; d < outputDepth; d++, outPtr++) {
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*outPtr += biasData[d];
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*outPtr = std::max(std::min(*outPtr, outputActivationMax), outputActivationMin);
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}
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}
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return true;
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}
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template <typename T>
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bool transposeConvNhwc(const T* inputData, const Shape& inputShape, const T* filterData,
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const Shape& filterShape, const int32_t* biasData, const Shape& biasShape,
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const TransposeConv2dParam& param, T* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("transposeConvQuant8");
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ANDROID_NN_TRANSPOSE_CONV_PARAMETERS
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int32_t* tempBuffer = nullptr;
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std::unique_ptr<int32_t[]> bufferGuard;
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uint32_t tempBufferByteSize = getNumberOfElements(outputShape) * sizeof(int32_t);
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if (tempBufferByteSize <= kStaticBufferSize) {
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tempBuffer = reinterpret_cast<int32_t*>(static_scratch_buffer);
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} else {
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tempBuffer = new (std::nothrow) int32_t[tempBufferByteSize / sizeof(int32_t)];
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if (tempBuffer == nullptr) {
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LOG(ERROR) << "ConvTranspose size is too large, not enough memory";
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return false;
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}
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bufferGuard.reset(tempBuffer);
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}
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int32_t inputOffset = -inputShape.offset;
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int32_t filterOffset = -filterShape.offset;
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int32_t outputOffset = outputShape.offset;
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double realMultiplier = 0.0;
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int32_t outputMultiplier = 0;
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int32_t outputShift = 0;
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NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape,
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&realMultiplier));
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int exponent;
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NN_RET_CHECK(QuantizeMultiplier(realMultiplier, &outputMultiplier, &exponent));
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outputShift = -exponent;
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int32_t outputActivationMin = 0, outputActivationMax = 0;
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CalculateActivationRange<T>(activation, outputShape, &outputActivationMin,
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&outputActivationMax);
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// Prevent concurrent executions that may access the scratch buffer
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std::unique_lock<std::mutex> lock(executionMutex);
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memset(tempBuffer, 0, tempBufferByteSize);
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const T* inputPtr = inputData;
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int32_t* outputBase = tempBuffer;
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for (uint32_t b = 0; b < numBatches; b++) {
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for (uint32_t h = 0; h < inputHeight; h++) {
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for (uint32_t w = 0; w < inputWidth; w++) {
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for (uint32_t d = 0; d < inputDepth; d++) {
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int32_t wOutputOrigin = static_cast<int32_t>(w) * strideWidth - paddingLeft;
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int32_t hOutputOrigin = static_cast<int32_t>(h) * strideHeight - paddingTop;
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for (uint32_t i = 0; i < filterHeight; i++) {
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for (uint32_t j = 0; j < filterWidth; j++) {
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for (uint32_t k = 0; k < outputDepth; k++) {
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int32_t hOutput = hOutputOrigin + static_cast<int32_t>(i);
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int32_t wOutput = wOutputOrigin + static_cast<int32_t>(j);
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if (hOutput >= 0 && hOutput < static_cast<int32_t>(outputHeight) &&
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wOutput >= 0 && wOutput < static_cast<int32_t>(outputWidth)) {
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uint32_t filterIndex =
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k * filterHeight * filterWidth * inputDepth +
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i * filterWidth * inputDepth + j * inputDepth + d;
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uint32_t outputIndex = hOutput * outputWidth * outputDepth +
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wOutput * outputDepth + k;
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outputBase[outputIndex] +=
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(static_cast<int32_t>(*inputPtr) + inputOffset) *
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(static_cast<int32_t>(filterData[filterIndex]) +
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filterOffset);
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}
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}
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}
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}
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inputPtr++;
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}
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}
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}
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outputBase += outputHeight * outputWidth * outputDepth;
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}
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const uint32_t outerSize = numBatches * outputHeight * outputWidth;
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int32_t* bufferPtr = tempBuffer;
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T* outPtr = outputData;
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for (uint32_t i = 0; i < outerSize; i++) {
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for (uint32_t d = 0; d < outputDepth; d++, bufferPtr++, outPtr++) {
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int32_t outVal = *bufferPtr + biasData[d];
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outVal = tflite::MultiplyByQuantizedMultiplier(outVal, outputMultiplier, -outputShift);
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outVal += outputOffset;
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outVal = std::max(std::min(outVal, outputActivationMax), outputActivationMin);
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*outPtr = static_cast<T>(outVal);
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}
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}
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return true;
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}
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bool transposeConvNhwc(const _Float16* inputData, const Shape& inputShape,
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const _Float16* filterData, const Shape& filterShape,
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const _Float16* biasData, const Shape& biasShape,
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const TransposeConv2dParam& param, _Float16* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("transposeConvFloat16");
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std::vector<float> inputData_float32(getNumberOfElements(inputShape));
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std::vector<float> filterData_float32(getNumberOfElements(filterShape));
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std::vector<float> biasData_float32(getNumberOfElements(biasShape));
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std::vector<float> outputData_float32(getNumberOfElements(outputShape));
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convertFloat16ToFloat32(inputData, &inputData_float32);
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convertFloat16ToFloat32(filterData, &filterData_float32);
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convertFloat16ToFloat32(biasData, &biasData_float32);
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transposeConvNhwc(inputData_float32.data(), inputShape, filterData_float32.data(), filterShape,
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biasData_float32.data(), biasShape, param, outputData_float32.data(),
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outputShape);
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convertFloat32ToFloat16(outputData_float32, outputData);
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return true;
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}
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template <typename T_Input, typename T_Filter, typename T_Bias>
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bool transposeConv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData,
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const Shape& filterShape, const T_Bias* biasData, const Shape& biasShape,
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const TransposeConv2dParam& param, T_Input* outputData,
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const Shape& outputShape) {
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InputWithLayout<T_Input> input(param.useNchw);
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OutputWithLayout<T_Input> output(param.useNchw);
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NN_RET_CHECK(input.initialize(inputData, inputShape));
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NN_RET_CHECK(output.initialize(outputData, outputShape));
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NN_RET_CHECK(transposeConvNhwc(input.getNhwcBuffer(), input.getNhwcShape(), filterData,
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filterShape, biasData, biasShape, param, output.getNhwcBuffer(),
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output.getNhwcShape()));
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NN_RET_CHECK(output.commit());
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return true;
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}
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template <typename T>
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bool transposeConvQuant8PerChannelNhwc(const T* inputData, const Shape& inputShape,
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const int8_t* filterData, const Shape& filterShape,
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const float* filterScales, const int32_t* biasData,
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const Shape& biasShape, const TransposeConv2dParam& param,
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T* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("transposeConvQuant8PerChannel");
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ANDROID_NN_TRANSPOSE_CONV_PARAMETERS
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int32_t* tempBuffer = nullptr;
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std::unique_ptr<int32_t[]> bufferGuard;
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uint32_t tempBufferByteSize = getNumberOfElements(outputShape) * sizeof(int32_t);
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if (tempBufferByteSize <= kStaticBufferSize) {
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tempBuffer = reinterpret_cast<int32_t*>(static_scratch_buffer);
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} else {
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tempBuffer = new (std::nothrow) int32_t[tempBufferByteSize / sizeof(int32_t)];
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if (tempBuffer == nullptr) {
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LOG(ERROR) << "ConvTranspose size is too large, not enough memory";
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return false;
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}
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bufferGuard.reset(tempBuffer);
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}
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int32_t inputOffset = -inputShape.offset;
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int32_t outputOffset = outputShape.offset;
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std::vector<double> realMultiplier(outputDepth, 0.0);
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std::vector<int32_t> outputMultiplier(outputDepth, 0);
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std::vector<int32_t> outputShift(outputDepth, 0);
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for (int i = 0; i < outputDepth; ++i) {
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Shape filterChannelShape = filterShape;
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filterChannelShape.scale = filterScales[i];
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Shape biasChannelShape = biasShape;
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biasChannelShape.scale = filterScales[i] * inputShape.scale;
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NN_RET_CHECK(GetQuantizedConvolutionMultipler(
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inputShape, filterChannelShape, biasChannelShape, outputShape, &realMultiplier[i]));
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int exponent;
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NN_RET_CHECK(QuantizeMultiplier(realMultiplier[i], &outputMultiplier[i], &exponent));
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outputShift[i] = -exponent;
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}
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int32_t outputActivationMin = 0, outputActivationMax = 0;
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CalculateActivationRange<T>(activation, outputShape, &outputActivationMin,
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&outputActivationMax);
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// Prevent concurrent executions that may access the scratch buffer
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std::unique_lock<std::mutex> lock(executionMutex);
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memset(tempBuffer, 0, tempBufferByteSize);
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const T* inputPtr = inputData;
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int32_t* outputBase = tempBuffer;
|
||
|
for (uint32_t b = 0; b < numBatches; b++) {
|
||
|
for (uint32_t h = 0; h < inputHeight; h++) {
|
||
|
for (uint32_t w = 0; w < inputWidth; w++) {
|
||
|
for (uint32_t d = 0; d < inputDepth; d++) {
|
||
|
int32_t wOutputOrigin = static_cast<int32_t>(w) * strideWidth - paddingLeft;
|
||
|
int32_t hOutputOrigin = static_cast<int32_t>(h) * strideHeight - paddingTop;
|
||
|
|
||
|
for (uint32_t i = 0; i < filterHeight; i++) {
|
||
|
for (uint32_t j = 0; j < filterWidth; j++) {
|
||
|
for (uint32_t k = 0; k < outputDepth; k++) {
|
||
|
int32_t hOutput = hOutputOrigin + static_cast<int32_t>(i);
|
||
|
int32_t wOutput = wOutputOrigin + static_cast<int32_t>(j);
|
||
|
if (hOutput >= 0 && hOutput < static_cast<int32_t>(outputHeight) &&
|
||
|
wOutput >= 0 && wOutput < static_cast<int32_t>(outputWidth)) {
|
||
|
uint32_t filterIndex =
|
||
|
k * filterHeight * filterWidth * inputDepth +
|
||
|
i * filterWidth * inputDepth + j * inputDepth + d;
|
||
|
uint32_t outputIndex = hOutput * outputWidth * outputDepth +
|
||
|
wOutput * outputDepth + k;
|
||
|
outputBase[outputIndex] +=
|
||
|
(static_cast<int32_t>(*inputPtr) + inputOffset) *
|
||
|
static_cast<int32_t>(filterData[filterIndex]);
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
|
||
|
inputPtr++;
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
outputBase += outputHeight * outputWidth * outputDepth;
|
||
|
}
|
||
|
|
||
|
const uint32_t outerSize = numBatches * outputHeight * outputWidth;
|
||
|
int32_t* bufferPtr = tempBuffer;
|
||
|
T* outPtr = outputData;
|
||
|
for (uint32_t i = 0; i < outerSize; i++) {
|
||
|
for (uint32_t d = 0; d < outputDepth; d++, bufferPtr++, outPtr++) {
|
||
|
int32_t outVal = *bufferPtr + biasData[d];
|
||
|
outVal = tflite::MultiplyByQuantizedMultiplier(outVal, outputMultiplier[d],
|
||
|
-outputShift[d]);
|
||
|
outVal += outputOffset;
|
||
|
outVal = std::max(std::min(outVal, outputActivationMax), outputActivationMin);
|
||
|
*outPtr = static_cast<T>(outVal);
|
||
|
}
|
||
|
}
|
||
|
|
||
|
return true;
|
||
|
}
|
||
|
|
||
|
template <typename T>
|
||
|
bool transposeConvQuant8PerChannel(const T* inputData, const Shape& inputShape,
|
||
|
const int8_t* filterData, const Shape& filterShape,
|
||
|
const float* filterScales, const int32_t* biasData,
|
||
|
const Shape& biasShape, const TransposeConv2dParam& param,
|
||
|
T* outputData, const Shape& outputShape) {
|
||
|
InputWithLayout<T> input(param.useNchw);
|
||
|
OutputWithLayout<T> output(param.useNchw);
|
||
|
NN_RET_CHECK(input.initialize(inputData, inputShape));
|
||
|
NN_RET_CHECK(output.initialize(outputData, outputShape));
|
||
|
NN_RET_CHECK(transposeConvQuant8PerChannelNhwc(
|
||
|
input.getNhwcBuffer(), input.getNhwcShape(), filterData, filterShape, filterScales,
|
||
|
biasData, biasShape, param, output.getNhwcBuffer(), output.getNhwcShape()));
|
||
|
NN_RET_CHECK(output.commit());
|
||
|
return true;
|
||
|
}
|
||
|
|
||
|
#undef ANDROID_NN_TRANSPOSE_CONV_PARAMETERS
|
||
|
#endif // NN_INCLUDE_CPU_IMPLEMENTATION
|
||
|
|
||
|
} // namespace
|
||
|
|
||
|
Result<Version> validate(const IOperationValidationContext* context) {
|
||
|
const uint32_t inputCount = context->getNumInputs();
|
||
|
NN_RET_CHECK(inputCount == kNumInputs1 || inputCount == kNumInputs2);
|
||
|
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
|
||
|
const auto inputType = context->getInputType(kInputTensor);
|
||
|
const auto filterType = context->getInputType(kFilterTensor);
|
||
|
std::vector<OperandType> inExpectedTypes;
|
||
|
Version minSupportedVersion = Version::ANDROID_Q;
|
||
|
if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_FLOAT16) {
|
||
|
inExpectedTypes = {inputType, inputType, inputType};
|
||
|
} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM ||
|
||
|
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
||
|
NN_RET_CHECK(filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
|
||
|
filterType == inputType)
|
||
|
<< "Unsupported filter tensor type for operation " << kOperationName;
|
||
|
if (filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
|
||
|
NN_RET_CHECK_EQ(std::get<Operand::SymmPerChannelQuantParams>(
|
||
|
context->getInputExtraParams(kFilterTensor))
|
||
|
.channelDim,
|
||
|
0)
|
||
|
<< "Unsupported filter tensor channel dimension for operation "
|
||
|
<< kOperationName;
|
||
|
}
|
||
|
inExpectedTypes = {inputType, filterType, OperandType::TENSOR_INT32};
|
||
|
if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
||
|
minSupportedVersion = Version::ANDROID_R;
|
||
|
}
|
||
|
} else {
|
||
|
NN_RET_CHECK_FAIL() << "Unsupported input tensor type for operation " << kOperationName;
|
||
|
}
|
||
|
|
||
|
std::vector<OperandType> argExpectedTypes;
|
||
|
if (inputCount == 11) {
|
||
|
argExpectedTypes = {OperandType::INT32, OperandType::INT32, OperandType::INT32,
|
||
|
OperandType::INT32, OperandType::INT32, OperandType::INT32,
|
||
|
OperandType::INT32, OperandType::BOOL};
|
||
|
} else {
|
||
|
argExpectedTypes = {OperandType::TENSOR_INT32, OperandType::INT32, OperandType::INT32,
|
||
|
OperandType::INT32, OperandType::INT32, OperandType::BOOL};
|
||
|
}
|
||
|
inExpectedTypes.insert(inExpectedTypes.end(), argExpectedTypes.begin(), argExpectedTypes.end());
|
||
|
NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
|
||
|
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
|
||
|
return minSupportedVersion;
|
||
|
}
|
||
|
|
||
|
#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
|
||
|
bool prepare(IOperationExecutionContext* context) {
|
||
|
Shape input = context->getInputShape(kInputTensor);
|
||
|
Shape filter = context->getInputShape(kFilterTensor);
|
||
|
Shape bias = context->getInputShape(kBiasTensor);
|
||
|
|
||
|
if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
|
||
|
NN_RET_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM ||
|
||
|
input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED);
|
||
|
} else {
|
||
|
NN_RET_CHECK(input.type == filter.type);
|
||
|
}
|
||
|
if (input.type == OperandType::TENSOR_QUANT8_ASYMM ||
|
||
|
input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
||
|
NN_RET_CHECK(bias.type == OperandType::TENSOR_INT32);
|
||
|
} else {
|
||
|
NN_RET_CHECK(input.type == bias.type);
|
||
|
}
|
||
|
NN_RET_CHECK_EQ(getNumberOfDimensions(input), 4);
|
||
|
NN_RET_CHECK_EQ(getNumberOfDimensions(filter), 4);
|
||
|
NN_RET_CHECK_EQ(getNumberOfDimensions(bias), 1);
|
||
|
|
||
|
TransposeConv2dParam param;
|
||
|
NN_RET_CHECK(param.initialize(context));
|
||
|
|
||
|
uint32_t batches = getSizeOfDimension(input, 0);
|
||
|
uint32_t height = getSizeOfDimension(input, param.useNchw ? 2 : 1);
|
||
|
uint32_t width = getSizeOfDimension(input, param.useNchw ? 3 : 2);
|
||
|
uint32_t channels_in = getSizeOfDimension(input, param.useNchw ? 1 : 3);
|
||
|
uint32_t channels_out = getSizeOfDimension(filter, 0);
|
||
|
uint32_t filterHeight = getSizeOfDimension(filter, 1);
|
||
|
uint32_t filterWidth = getSizeOfDimension(filter, 2);
|
||
|
// Only batches can be zero.
|
||
|
NN_RET_CHECK_EQ(channels_in, getSizeOfDimension(filter, 3));
|
||
|
NN_RET_CHECK_EQ(channels_out, getSizeOfDimension(bias, 0));
|
||
|
NN_RET_CHECK_GT(height, 0);
|
||
|
NN_RET_CHECK_GT(width, 0);
|
||
|
NN_RET_CHECK_GT(channels_in, 0);
|
||
|
NN_RET_CHECK_GT(channels_out, 0);
|
||
|
NN_RET_CHECK_GT(filterWidth, 0);
|
||
|
NN_RET_CHECK_GT(filterHeight, 0);
|
||
|
|
||
|
uint32_t outWidth = computeOutSizeTransposeConv(width, filterWidth, param.strideWidth,
|
||
|
param.paddingLeft, param.paddingRight);
|
||
|
uint32_t outHeight = computeOutSizeTransposeConv(height, filterHeight, param.strideHeight,
|
||
|
param.paddingTop, param.paddingBottom);
|
||
|
NN_RET_CHECK_GT(outWidth, 0);
|
||
|
NN_RET_CHECK_GT(outHeight, 0);
|
||
|
|
||
|
Shape output = context->getOutputShape(kOutputTensor);
|
||
|
output.type = input.type;
|
||
|
if (param.useNchw) {
|
||
|
output.dimensions = {batches, channels_out, outHeight, outWidth};
|
||
|
} else {
|
||
|
output.dimensions = {batches, outHeight, outWidth, channels_out};
|
||
|
}
|
||
|
return context->setOutputShape(kOutputTensor, output);
|
||
|
}
|
||
|
|
||
|
bool execute(IOperationExecutionContext* context) {
|
||
|
// Bypass execution in the case of zero-sized input.
|
||
|
if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
|
||
|
TransposeConv2dParam param;
|
||
|
NN_RET_CHECK(param.initialize(context));
|
||
|
switch (context->getInputType(kInputTensor)) {
|
||
|
case OperandType::TENSOR_FLOAT32:
|
||
|
return transposeConv(context->getInputBuffer<float>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<float>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
context->getInputBuffer<float>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<float>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
case OperandType::TENSOR_FLOAT16:
|
||
|
return transposeConv(context->getInputBuffer<_Float16>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<_Float16>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
context->getInputBuffer<_Float16>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<_Float16>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
case OperandType::TENSOR_QUANT8_ASYMM:
|
||
|
if (context->getInputType(kFilterTensor) ==
|
||
|
OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
|
||
|
return transposeConvQuant8PerChannel(
|
||
|
context->getInputBuffer<uint8_t>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<int8_t>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
std::get<Operand::SymmPerChannelQuantParams>(
|
||
|
context->getInputExtraParams(kFilterTensor))
|
||
|
.scales.data(),
|
||
|
context->getInputBuffer<int32_t>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<uint8_t>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
} else if (context->getInputType(kFilterTensor) == OperandType::TENSOR_QUANT8_ASYMM) {
|
||
|
return transposeConv(context->getInputBuffer<uint8_t>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<uint8_t>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
context->getInputBuffer<int32_t>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<uint8_t>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
} else {
|
||
|
NN_RET_CHECK_FAIL() << "Unsupported filter type for operation " << kOperationName;
|
||
|
}
|
||
|
case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
|
||
|
if (context->getInputType(kFilterTensor) ==
|
||
|
OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
|
||
|
return transposeConvQuant8PerChannel(
|
||
|
context->getInputBuffer<int8_t>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<int8_t>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
std::get<Operand::SymmPerChannelQuantParams>(
|
||
|
context->getInputExtraParams(kFilterTensor))
|
||
|
.scales.data(),
|
||
|
context->getInputBuffer<int32_t>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<int8_t>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
} else if (context->getInputType(kFilterTensor) ==
|
||
|
OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
||
|
return transposeConv(context->getInputBuffer<int8_t>(kInputTensor),
|
||
|
context->getInputShape(kInputTensor),
|
||
|
context->getInputBuffer<int8_t>(kFilterTensor),
|
||
|
context->getInputShape(kFilterTensor),
|
||
|
context->getInputBuffer<int32_t>(kBiasTensor),
|
||
|
context->getInputShape(kBiasTensor), param,
|
||
|
context->getOutputBuffer<int8_t>(kOutputTensor),
|
||
|
context->getOutputShape(kOutputTensor));
|
||
|
} else {
|
||
|
NN_RET_CHECK_FAIL() << "Unsupported filter type for operation " << kOperationName;
|
||
|
}
|
||
|
default:
|
||
|
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
|
||
|
}
|
||
|
}
|
||
|
#endif // NN_INCLUDE_CPU_IMPLEMENTATION
|
||
|
|
||
|
} // namespace transpose_conv_2d
|
||
|
|
||
|
NN_REGISTER_OPERATION(TRANSPOSE_CONV_2D, transpose_conv_2d::kOperationName,
|
||
|
transpose_conv_2d::validate, transpose_conv_2d::prepare,
|
||
|
transpose_conv_2d::execute, .allowZeroSizedInput = true);
|
||
|
|
||
|
} // namespace nn
|
||
|
} // namespace android
|