/* * Copyright (C) 2018 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. */ #include "CpuOperationUtils.h" #include "OperationResolver.h" #include <cfloat> #include <cmath> #include "Tracing.h" #include "tensorflow/lite/kernels/internal/common.h" namespace android { namespace nn { namespace transpose_conv_2d { constexpr char kOperationName[] = "TRANSPOSE_CONV_2D"; constexpr uint32_t kInputTensor = 0; constexpr uint32_t kFilterTensor = 1; constexpr uint32_t kBiasTensor = 2; constexpr uint32_t kNumOutputs = 1; constexpr uint32_t kOutputTensor = 0; namespace { // If possible we will use this static buffer for the tensor. constexpr size_t kStaticBufferSize = 1605632; char static_scratch_buffer[kStaticBufferSize]; // executionMutex is used to protect concurrent access of the static_scratch_buffer. // std::mutex is safe for pthreads on Android. std::mutex executionMutex; struct TransposeConv2dParam { int32_t paddingLeft, paddingRight; int32_t paddingTop, paddingBottom; int32_t strideWidth, strideHeight; int32_t activation; bool useNchw = false; bool initialize(const IOperationExecutionContext* context) { uint32_t inCount = context->getNumInputs(); int32_t paddingImplicit = 0; if (inCount == 9) { paddingImplicit = context->getInputValue<int32_t>(4); strideWidth = context->getInputValue<int32_t>(5); strideHeight = context->getInputValue<int32_t>(6); activation = context->getInputValue<int32_t>(7); useNchw = context->getInputValue<bool>(8); Shape filterShape = context->getInputShape(kFilterTensor); int32_t filterWidth = getSizeOfDimension(filterShape, 2); int32_t filterHeight = getSizeOfDimension(filterShape, 1); NN_RET_CHECK_EQ(getNumberOfDimensions(context->getInputShape(3)), 1); NN_RET_CHECK_EQ(getSizeOfDimension(context->getInputShape(3), 0), 4); const int32_t* outputShapeData = context->getInputBuffer<int32_t>(3); int32_t outputWidth = useNchw ? outputShapeData[3] : outputShapeData[2]; int32_t outputHeight = useNchw ? outputShapeData[2] : outputShapeData[1]; calculateExplicitPaddingTransposeConv(outputWidth, strideWidth, filterWidth, paddingImplicit, &paddingLeft, &paddingRight); calculateExplicitPaddingTransposeConv(outputHeight, strideHeight, filterHeight, paddingImplicit, &paddingTop, &paddingBottom); } else if (inCount == 11) { paddingLeft = context->getInputValue<int32_t>(3); paddingRight = context->getInputValue<int32_t>(4); paddingTop = context->getInputValue<int32_t>(5); paddingBottom = context->getInputValue<int32_t>(6); strideWidth = context->getInputValue<int32_t>(7); strideHeight = context->getInputValue<int32_t>(8); activation = context->getInputValue<int32_t>(9); useNchw = context->getInputValue<bool>(10); } else { NN_RET_CHECK_FAIL() << "Unsupported input spec for operation " << kOperationName; } // paddingRight and paddingBottom in transpose conv may be less than 0 to resolve the // ambiguous output shape issue in the case of stride > 1. NN_RET_CHECK_GE(paddingLeft, 0); NN_RET_CHECK_GE(paddingTop, 0); NN_RET_CHECK_GT(strideWidth, 0); NN_RET_CHECK_GT(strideHeight, 0); NN_RET_CHECK_GE(activation, 0); return true; } }; #define ANDROID_NN_TRANSPOSE_CONV_PARAMETERS \ uint32_t numBatches = getSizeOfDimension(inputShape, 0); \ uint32_t inputHeight = getSizeOfDimension(inputShape, 1); \ uint32_t inputWidth = getSizeOfDimension(inputShape, 2); \ uint32_t inputDepth = getSizeOfDimension(inputShape, 3); \ uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ uint32_t outputHeight = getSizeOfDimension(outputShape, 1); \ uint32_t outputWidth = getSizeOfDimension(outputShape, 2); \ uint32_t outputDepth = getSizeOfDimension(outputShape, 3); \ int32_t paddingLeft = param.paddingLeft, paddingRight = param.paddingRight; \ int32_t paddingTop = param.paddingTop, paddingBottom = param.paddingBottom; \ int32_t strideWidth = param.strideWidth, strideHeight = param.strideHeight; \ int32_t activation = param.activation; bool transposeConvNhwc(const float* inputData, const Shape& inputShape, const float* filterData, const Shape& filterShape, const float* biasData, const Shape& biasShape, const TransposeConv2dParam& param, float* outputData, const Shape& outputShape) { NNTRACE_TRANS("transposeConvFloat32"); ANDROID_NN_TRANSPOSE_CONV_PARAMETERS float outputActivationMin = 0.0f, outputActivationMax = 0.0f; CalculateActivationRangeFloat(activation, &outputActivationMin, &outputActivationMax); memset(outputData, 0, getNumberOfElements(outputShape) * sizeof(float)); const float* inputBase = inputData; float* outputBase = outputData; for (uint32_t b = 0; b < numBatches; b++) { for (uint32_t h = 0; h < inputHeight; h++) { for (uint32_t w = 0; w < inputWidth; w++) { int32_t wOutputOrigin = static_cast<int32_t>(w) * strideWidth - paddingLeft; int32_t hOutputOrigin = static_cast<int32_t>(h) * strideHeight - paddingTop; const float* filterBase = filterData; for (uint32_t k = 0; k < outputDepth; k++) { for (uint32_t i = 0; i < filterHeight; i++) { for (uint32_t j = 0; j < filterWidth; j++, filterBase += inputDepth) { 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)) { for (uint32_t d = 0; d < inputDepth; d++) { uint32_t outputIndex = hOutput * outputWidth * outputDepth + wOutput * outputDepth + k; outputBase[outputIndex] += inputBase[d] * filterBase[d]; } } } } } inputBase += inputDepth; } } outputBase += outputHeight * outputWidth * outputDepth; } const uint32_t outerSize = numBatches * outputHeight * outputWidth; float* outPtr = outputData; for (uint32_t i = 0; i < outerSize; i++) { for (uint32_t d = 0; d < outputDepth; d++, outPtr++) { *outPtr += biasData[d]; *outPtr = std::max(std::min(*outPtr, outputActivationMax), outputActivationMin); } } return true; } bool transposeConvNhwc(const uint8_t* inputData, const Shape& inputShape, const uint8_t* filterData, const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, const TransposeConv2dParam& param, uint8_t* outputData, const Shape& outputShape) { NNTRACE_TRANS("transposeConvQuant8"); ANDROID_NN_TRANSPOSE_CONV_PARAMETERS int32_t* tempBuffer = nullptr; std::unique_ptr<int32_t[]> bufferGuard; uint32_t tempBufferByteSize = getNumberOfElements(outputShape) * sizeof(int32_t); if (tempBufferByteSize <= kStaticBufferSize) { tempBuffer = reinterpret_cast<int32_t*>(static_scratch_buffer); } else { tempBuffer = new (std::nothrow) int32_t[tempBufferByteSize / sizeof(int32_t)]; if (tempBuffer == nullptr) { LOG(ERROR) << "ConvTranspose size is too large, not enough memory"; return false; } bufferGuard.reset(tempBuffer); } int32_t inputOffset = -inputShape.offset; int32_t filterOffset = -filterShape.offset; int32_t outputOffset = outputShape.offset; double realMultiplier = 0.0; int32_t outputMultiplier = 0; int32_t outputShift = 0; NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape, &realMultiplier)); int exponent; NN_RET_CHECK(QuantizeMultiplier(realMultiplier, &outputMultiplier, &exponent)); outputShift = -exponent; int32_t outputActivationMin = 0, outputActivationMax = 0; CalculateActivationRangeUint8(activation, outputShape, &outputActivationMin, &outputActivationMax); // Prevent concurrent executions that may access the scratch buffer std::unique_lock<std::mutex> lock(executionMutex); memset(tempBuffer, 0, tempBufferByteSize); const uint8_t* inputPtr = inputData; 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]) + filterOffset); } } } } inputPtr++; } } } outputBase += outputHeight * outputWidth * outputDepth; } const uint32_t outerSize = numBatches * outputHeight * outputWidth; int32_t* bufferPtr = tempBuffer; uint8_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, -outputShift); outVal += outputOffset; outVal = std::max(std::min(outVal, outputActivationMax), outputActivationMin); *outPtr = static_cast<uint8_t>(outVal); } } return true; } bool transposeConvNhwc(const _Float16* inputData, const Shape& inputShape, const _Float16* filterData, const Shape& filterShape, const _Float16* biasData, const Shape& biasShape, const TransposeConv2dParam& param, _Float16* outputData, const Shape& outputShape) { NNTRACE_TRANS("transposeConvFloat16"); std::vector<float> inputData_float32(getNumberOfElements(inputShape)); std::vector<float> filterData_float32(getNumberOfElements(filterShape)); std::vector<float> biasData_float32(getNumberOfElements(biasShape)); std::vector<float> outputData_float32(getNumberOfElements(outputShape)); convertFloat16ToFloat32(inputData, &inputData_float32); convertFloat16ToFloat32(filterData, &filterData_float32); convertFloat16ToFloat32(biasData, &biasData_float32); transposeConvNhwc(inputData_float32.data(), inputShape, filterData_float32.data(), filterShape, biasData_float32.data(), biasShape, param, outputData_float32.data(), outputShape); convertFloat32ToFloat16(outputData_float32, outputData); return true; } template <typename T_Input, typename T_Filter, typename T_Bias> bool transposeConv(const T_Input* inputData, const Shape& inputShape, const T_Filter* filterData, const Shape& filterShape, const T_Bias* biasData, const Shape& biasShape, const TransposeConv2dParam& param, T_Input* outputData, const Shape& outputShape) { InputWithLayout<T_Input> input(param.useNchw); OutputWithLayout<T_Input> output(param.useNchw); NN_RET_CHECK(input.initialize(inputData, inputShape)); NN_RET_CHECK(output.initialize(outputData, outputShape)); NN_RET_CHECK(transposeConvNhwc(input.getNhwcBuffer(), input.getNhwcShape(), filterData, filterShape, biasData, biasShape, param, output.getNhwcBuffer(), output.getNhwcShape())); NN_RET_CHECK(output.commit()); return true; } bool transposeConvQuant8PerChannelNhwc(const uint8_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, uint8_t* outputData, const Shape& outputShape) { NNTRACE_TRANS("transposeConvQuant8PerChannel"); ANDROID_NN_TRANSPOSE_CONV_PARAMETERS int32_t* tempBuffer = nullptr; std::unique_ptr<int32_t[]> bufferGuard; uint32_t tempBufferByteSize = getNumberOfElements(outputShape) * sizeof(int32_t); if (tempBufferByteSize <= kStaticBufferSize) { tempBuffer = reinterpret_cast<int32_t*>(static_scratch_buffer); } else { tempBuffer = new (std::nothrow) int32_t[tempBufferByteSize / sizeof(int32_t)]; if (tempBuffer == nullptr) { LOG(ERROR) << "ConvTranspose size is too large, not enough memory"; return false; } bufferGuard.reset(tempBuffer); } int32_t inputOffset = -inputShape.offset; int32_t outputOffset = outputShape.offset; std::vector<double> realMultiplier(outputDepth, 0.0); std::vector<int32_t> outputMultiplier(outputDepth, 0); std::vector<int32_t> outputShift(outputDepth, 0); for (int i = 0; i < outputDepth; ++i) { Shape filterChannelShape = filterShape; filterChannelShape.scale = filterScales[i]; Shape biasChannelShape = biasShape; biasChannelShape.scale = filterScales[i] * inputShape.scale; NN_RET_CHECK(GetQuantizedConvolutionMultipler( inputShape, filterChannelShape, biasChannelShape, outputShape, &realMultiplier[i])); int exponent; NN_RET_CHECK(QuantizeMultiplier(realMultiplier[i], &outputMultiplier[i], &exponent)); outputShift[i] = -exponent; } int32_t outputActivationMin = 0, outputActivationMax = 0; CalculateActivationRangeUint8(activation, outputShape, &outputActivationMin, &outputActivationMax); // Prevent concurrent executions that may access the scratch buffer std::unique_lock<std::mutex> lock(executionMutex); memset(tempBuffer, 0, tempBufferByteSize); const uint8_t* inputPtr = inputData; 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; uint8_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<uint8_t>(outVal); } } return true; } bool transposeConvQuant8PerChannel(const uint8_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, uint8_t* outputData, const Shape& outputShape) { InputWithLayout<uint8_t> input(param.useNchw); OutputWithLayout<uint8_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 } // namespace bool validate(const IOperationValidationContext* context) { NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); auto inputCount = context->getNumInputs(); auto inputType = context->getInputType(kInputTensor); auto filterType = context->getInputType(kFilterTensor); std::vector<OperandType> inExpectedTypes; if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_FLOAT16) { inExpectedTypes = {inputType, inputType, inputType}; } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { NN_RET_CHECK(filterType == OperandType::TENSOR_QUANT8_ASYMM || filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) << "Unsupported filter tensor type for operation " << kOperationName; if (filterType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) { NN_RET_CHECK_EQ(context->getInputExtraParams(kFilterTensor).channelQuant().channelDim, 0) << "Unsupported filter tensor channel dimension for operation " << kOperationName; } inExpectedTypes = {inputType, filterType, OperandType::TENSOR_INT32}; } 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(validateHalVersion(context, HalVersion::V1_2)); return validateInputTypes(context, inExpectedTypes) && validateOutputTypes(context, {inputType}); } 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); } else { NN_RET_CHECK(input.type == filter.type); } if (input.type == OperandType::TENSOR_QUANT8_ASYMM) { 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), context->getInputExtraParams(kFilterTensor).channelQuant().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; } default: NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; } } } // 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