/*
* 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