| /* |
| * 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 <typename T> |
| 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<uint32_t>(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<int32_t>(kNumGroups); |
| int32_t axis = context->getInputValue<int32_t>(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<int32_t>(kNumGroups); |
| int32_t axis = context->getInputValue<int32_t>(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<float>(kInputTensor), |
| context->getInputShape(kInputTensor), numGroups, axis, |
| context->getOutputBuffer<float>(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return eval(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), numGroups, axis, |
| context->getOutputBuffer<uint8_t>(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return eval(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), numGroups, axis, |
| context->getOutputBuffer<int8_t>(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 |