/* * 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 "ChannelShuffle.h" #include "OperationResolver.h" #include "OperationsExecutionUtils.h" #include "Tracing.h" namespace android { namespace nn { namespace channel_shuffle { template inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis, T* outputData) { const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); const uint32_t axisSize = getSizeOfDimension(inputShape, axis); const uint32_t innerSize = getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); const uint32_t groupSize = axisSize / numGroups; for (uint32_t outer = 0; outer < outerSize; ++outer) { for (uint32_t inner = 0; inner < innerSize; ++inner) { const T* inputBase = inputData + outer * axisSize * innerSize + inner; T* outputBase = outputData + outer * axisSize * innerSize + inner; for (uint32_t i = 0; i < groupSize; i++) { for (uint32_t j = 0; j < static_cast(numGroups); j++, outputBase += innerSize) { *outputBase = inputBase[innerSize * (i + j * groupSize)]; } } } } return true; } bool prepare(IOperationExecutionContext* context) { Shape input = context->getInputShape(kInputTensor); int32_t numGroups = context->getInputValue(kNumGroups); int32_t axis = context->getInputValue(kInputAxis); NN_RET_CHECK(handleNegativeAxis(input, &axis)); NN_RET_CHECK(numGroups > 0); NN_RET_CHECK(getSizeOfDimension(input, axis) % numGroups == 0); return context->setOutputShape(kOutputTensor, input); } bool execute(IOperationExecutionContext* context) { int32_t numGroups = context->getInputValue(kNumGroups); int32_t axis = context->getInputValue(kInputAxis); NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis)); switch (context->getInputType(kInputTensor)) { case OperandType::TENSOR_FLOAT16: return eval(context->getInputBuffer<_Float16>(kInputTensor), context->getInputShape(kInputTensor), numGroups, axis, context->getOutputBuffer<_Float16>(kOutputTensor)); case OperandType::TENSOR_FLOAT32: return eval(context->getInputBuffer(kInputTensor), context->getInputShape(kInputTensor), numGroups, axis, context->getOutputBuffer(kOutputTensor)); case OperandType::TENSOR_QUANT8_ASYMM: return eval(context->getInputBuffer(kInputTensor), context->getInputShape(kInputTensor), numGroups, axis, context->getOutputBuffer(kOutputTensor)); case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: return eval(context->getInputBuffer(kInputTensor), context->getInputShape(kInputTensor), numGroups, axis, context->getOutputBuffer(kOutputTensor)); default: NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; } } } // namespace channel_shuffle NN_REGISTER_OPERATION_DEFAULT_VALIDATION(CHANNEL_SHUFFLE, channel_shuffle::prepare, channel_shuffle::execute); } // namespace nn } // namespace android