/* * Copyright (C) 2019 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 "OperationsUtils.h" #define LOG_TAG "Operations" #include "HalInterfaces.h" #include "IndexedShapeWrapper.h" #include "OperationResolver.h" #include "Tracing.h" #include <cmath> namespace android { namespace nn { namespace quantize { constexpr uint32_t kNumInputs = 1; constexpr uint32_t kInputTensor = 0; constexpr uint32_t kNumOutputs = 1; constexpr uint32_t kOutputTensor = 0; namespace { bool quantizeFloat32ToQuant8(const float* inputData, uint8_t* outputData, const Shape& outputShape) { NNTRACE_COMP("quantizeFloat32ToQuant8"); uint32_t size = getNumberOfElements(outputShape); for (uint32_t i = 0; i < size; ++i) { outputData[i] = static_cast<uint8_t>(std::max<float>( 0, std::min<float>(255, outputShape.offset + std::round(inputData[i] / outputShape.scale)))); } return true; } bool quantizeFloat16ToQuant8(const _Float16* inputData, uint8_t* outputData, const Shape& outputShape) { NNTRACE_COMP("quantizeFloat16ToQuant8"); uint32_t size = getNumberOfElements(outputShape); for (uint32_t i = 0; i < size; ++i) { outputData[i] = static_cast<uint8_t>(std::max<float>( 0, std::min<float>(255, outputShape.offset + std::round(inputData[i] / outputShape.scale)))); } return true; } } // namespace bool validate(const IOperationValidationContext* context) { NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs); NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); const OperandType inputType = context->getInputType(kInputTensor); const OperandType outputType = context->getOutputType(kOutputTensor); NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 || inputType == OperandType::TENSOR_FLOAT32) << "Unsupported input operand type for QUANTIZE op: " << toString(inputType); NN_RET_CHECK(outputType == OperandType::TENSOR_QUANT8_ASYMM) << "Unsupported output operand type for QUANTIZE op: " << toString(outputType); return validateHalVersion(context, HalVersion::V1_2); } bool prepare(IOperationExecutionContext* context) { const Shape& input = context->getInputShape(kInputTensor); Shape output = context->getOutputShape(kOutputTensor); output.dimensions = input.dimensions; 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; const OperandType inputType = context->getInputType(kInputTensor); if (inputType == OperandType::TENSOR_FLOAT32) { return quantizeFloat32ToQuant8(context->getInputBuffer<float>(kInputTensor), context->getOutputBuffer<uint8_t>(kOutputTensor), context->getOutputShape(kOutputTensor)); } else if (inputType == OperandType::TENSOR_FLOAT16) { return quantizeFloat16ToQuant8(context->getInputBuffer<_Float16>(kInputTensor), context->getOutputBuffer<uint8_t>(kOutputTensor), context->getOutputShape(kOutputTensor)); } NN_RET_CHECK_FAIL() << "Unsupported tensor types combination for QUANTIZE op. (input type: " << toString(inputType) << " output type: " << toString(context->getOutputType(kOutputTensor)) << ")"; } } // namespace quantize NN_REGISTER_OPERATION(QUANTIZE, "QUANTIZE", quantize::validate, quantize::prepare, quantize::execute, .allowZeroSizedInput = true); } // namespace nn } // namespace android