| /* |
| * 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. |
| */ |
| |
| #define LOG_TAG "Operations" |
| |
| #include <tensorflow/lite/kernels/internal/common.h> |
| |
| #include <algorithm> |
| #include <cfloat> |
| #include <cmath> |
| #include <memory> |
| #include <vector> |
| |
| #include "CpuOperationUtils.h" |
| #include "OperationResolver.h" |
| #include "Tracing.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 kNumInputs1 = 9; |
| constexpr uint32_t kNumInputs2 = 11; |
| 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; |
| } |
| |
| template <typename T> |
| bool transposeConvNhwc(const T* inputData, const Shape& inputShape, const T* filterData, |
| const Shape& filterShape, const int32_t* biasData, const Shape& biasShape, |
| const TransposeConv2dParam& param, 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; |
| CalculateActivationRange<T>(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 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; |
| 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<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; |
| } |
| |
| template <typename T> |
| bool transposeConvQuant8PerChannelNhwc(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) { |
| 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; |
| CalculateActivationRange<T>(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 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; |
| 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 |
| |
| } // 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; |
| } |
| |
| 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; |
| } |
| } |
| |
| } // 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 |
