95 lines
4.0 KiB
C++
95 lines
4.0 KiB
C++
/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "Operations"
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#include "ChannelShuffle.h"
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#include "OperationResolver.h"
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#include "OperationsExecutionUtils.h"
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#include "Tracing.h"
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namespace android {
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namespace nn {
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namespace channel_shuffle {
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template <typename T>
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inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis,
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T* outputData) {
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const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
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const uint32_t innerSize =
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getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
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const uint32_t groupSize = axisSize / numGroups;
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for (uint32_t outer = 0; outer < outerSize; ++outer) {
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for (uint32_t inner = 0; inner < innerSize; ++inner) {
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const T* inputBase = inputData + outer * axisSize * innerSize + inner;
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T* outputBase = outputData + outer * axisSize * innerSize + inner;
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for (uint32_t i = 0; i < groupSize; i++) {
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for (uint32_t j = 0; j < static_cast<uint32_t>(numGroups);
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j++, outputBase += innerSize) {
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*outputBase = inputBase[innerSize * (i + j * groupSize)];
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}
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}
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}
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}
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return true;
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape input = context->getInputShape(kInputTensor);
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int32_t numGroups = context->getInputValue<int32_t>(kNumGroups);
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int32_t axis = context->getInputValue<int32_t>(kInputAxis);
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NN_RET_CHECK(handleNegativeAxis(input, &axis));
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NN_RET_CHECK(numGroups > 0);
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NN_RET_CHECK(getSizeOfDimension(input, axis) % numGroups == 0);
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return context->setOutputShape(kOutputTensor, input);
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}
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bool execute(IOperationExecutionContext* context) {
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int32_t numGroups = context->getInputValue<int32_t>(kNumGroups);
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int32_t axis = context->getInputValue<int32_t>(kInputAxis);
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NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return eval(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor), numGroups, axis,
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context->getOutputBuffer<_Float16>(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return eval(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor), numGroups, axis,
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context->getOutputBuffer<float>(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return eval(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor), numGroups, axis,
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context->getOutputBuffer<uint8_t>(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return eval(context->getInputBuffer<int8_t>(kInputTensor),
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context->getInputShape(kInputTensor), numGroups, axis,
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context->getOutputBuffer<int8_t>(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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} // namespace channel_shuffle
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NN_REGISTER_OPERATION_DEFAULT_VALIDATION(CHANNEL_SHUFFLE, channel_shuffle::prepare,
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channel_shuffle::execute);
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} // namespace nn
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} // namespace android
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