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107 lines
4.0 KiB
107 lines
4.0 KiB
/*
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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 <functional>
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#include <vector>
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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namespace android {
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namespace nn {
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namespace logical {
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constexpr uint32_t kNumInputs = 2;
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constexpr uint32_t kInputTensor1 = 0;
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constexpr uint32_t kInputTensor2 = 1;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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namespace {
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bool compute(const std::function<bool(bool, bool)>& func, const bool8* aData, const Shape& aShape,
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const bool8* bData, const Shape& bShape, bool8* outputData, const Shape& outputShape) {
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IndexedShapeWrapper aShapeIndexed(aShape);
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IndexedShapeWrapper bShapeIndexed(bShape);
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IndexedShapeWrapper outputShapeIndexed(outputShape);
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std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
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bool lastIndex = false;
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do {
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uint32_t outputFlatIndex;
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NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
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uint32_t aFlatIndex;
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NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
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uint32_t bFlatIndex;
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NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
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outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
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NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
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} while (!lastIndex);
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return true;
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}
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} // namespace
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Result<Version> validate(const IOperationValidationContext* context) {
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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OperandType inputType = context->getInputType(kInputTensor1);
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NN_RET_CHECK(inputType == OperandType::TENSOR_BOOL8)
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<< "Unsupported tensor type for a logical operation";
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NN_RET_CHECK(validateInputTypes(context, {inputType, inputType}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return Version::ANDROID_Q;
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape input1 = context->getInputShape(kInputTensor1);
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Shape input2 = context->getInputShape(kInputTensor2);
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Shape output = context->getOutputShape(kOutputTensor);
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NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output));
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return context->setOutputShape(kOutputTensor, output);
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}
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bool executeAnd(IOperationExecutionContext* context) {
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return compute(
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std::logical_and<bool>(), context->getInputBuffer<bool8>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<bool8>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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bool executeOr(IOperationExecutionContext* context) {
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return compute(
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std::logical_or<bool>(), context->getInputBuffer<bool8>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<bool8>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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} // namespace logical
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NN_REGISTER_OPERATION(LOGICAL_AND, "LOGICAL_AND", logical::validate, logical::prepare,
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logical::executeAnd);
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NN_REGISTER_OPERATION(LOGICAL_OR, "LOGICAL_OR", logical::validate, logical::prepare,
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logical::executeOr);
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} // namespace nn
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} // namespace android
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