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/*
* 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 "CpuOperationUtils.h"
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include <vector>
namespace android {
namespace nn {
namespace slice {
constexpr char kOperationName[] = "SLICE";
constexpr uint32_t kNumInputs = 3;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kBeginTensor = 1;
constexpr uint32_t kSizeTensor = 2;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
template <typename T>
void addVectors(const std::vector<T>& a, const std::vector<T>& b, std::vector<T>* res) {
for (int i = 0; i < res->size(); ++i) {
res->at(i) = a[i] + b[i];
}
}
template <typename T>
bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t* beginData,
const Shape& beginShape, const int32_t* sizeData, const Shape& sizeShape,
T* outputData, const Shape& outputShape) {
const int outputSize = getNumberOfElements(outputShape);
const IndexedShapeWrapper indexedOutput = IndexedShapeWrapper(outputShape);
const IndexedShapeWrapper indexedInput = IndexedShapeWrapper(inputShape);
std::vector<uint32_t> outputIndex(getNumberOfDimensions(outputShape), 0);
std::vector<uint32_t> beginIndex(getSizeOfDimension(beginShape, 0));
std::vector<uint32_t> inputIndex(getNumberOfDimensions(inputShape));
for (int i = 0; i < beginIndex.size(); ++i) {
beginIndex[i] = static_cast<uint32_t>(beginData[i]);
}
bool lastIndex = false;
uint32_t outputOffset;
uint32_t inputOffset;
do {
addVectors(outputIndex, beginIndex, &inputIndex);
NN_RET_CHECK(indexedOutput.indexToFlatIndex(outputIndex, &outputOffset));
NN_RET_CHECK(indexedInput.indexToFlatIndex(inputIndex, &inputOffset));
outputData[outputOffset] = inputData[inputOffset];
NN_RET_CHECK(indexedOutput.nextIndexInplace(&outputIndex, &lastIndex));
} while (!lastIndex);
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);
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
inputType == OperandType::TENSOR_FLOAT32 ||
inputType == OperandType::TENSOR_INT32 ||
inputType == OperandType::TENSOR_QUANT8_ASYMM ||
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
<< "Unsupported tensor type for operation " << kOperationName;
if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_3));
} else {
NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
}
return validateInputTypes(context,
{inputType, OperandType::TENSOR_INT32, OperandType::TENSOR_INT32}) &&
validateOutputTypes(context, {inputType});
}
bool prepare(IOperationExecutionContext* context) {
const Shape& inputShape = context->getInputShape(kInputTensor);
const int32_t n_dims = getNumberOfDimensions(inputShape);
NN_RET_CHECK(n_dims > 0);
const Shape& beginShape = context->getInputShape(kBeginTensor);
NN_RET_CHECK_EQ(getNumberOfDimensions(beginShape), 1);
NN_RET_CHECK_EQ(getSizeOfDimension(beginShape, 0), n_dims);
const Shape& sizeShape = context->getInputShape(kSizeTensor);
NN_RET_CHECK_EQ(getNumberOfDimensions(sizeShape), 1);
NN_RET_CHECK_EQ(getSizeOfDimension(sizeShape, 0), n_dims);
const int32_t* beginData = context->getInputBuffer<int32_t>(kBeginTensor);
const int32_t* sizeData = context->getInputBuffer<int32_t>(kSizeTensor);
Shape outputShape = context->getOutputShape(kOutputTensor);
outputShape.dimensions.resize(n_dims);
for (int i = 0; i < n_dims; ++i) {
const int32_t sliceBegin = beginData[i];
int32_t sliceSize = sizeData[i];
if (sliceSize == -1) {
sliceSize = getSizeOfDimension(inputShape, i) - sliceBegin;
}
NN_RET_CHECK_LE(beginData[i], getSizeOfDimension(inputShape, i));
NN_RET_CHECK_GE(sliceSize, 0);
NN_RET_CHECK_LE(sliceBegin + sliceSize, getSizeOfDimension(inputShape, i));
outputShape.dimensions[i] = sliceSize;
}
return context->setOutputShape(kOutputTensor, outputShape);
}
bool execute(IOperationExecutionContext* context) {
// Bypass execution in the case of zero-sized input.
if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return evalGeneric(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<int32_t>(kBeginTensor),
context->getInputShape(kBeginTensor),
context->getInputBuffer<int32_t>(kSizeTensor),
context->getInputShape(kSizeTensor),
context->getOutputBuffer<_Float16>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return evalGeneric(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<int32_t>(kBeginTensor),
context->getInputShape(kBeginTensor),
context->getInputBuffer<int32_t>(kSizeTensor),
context->getInputShape(kSizeTensor),
context->getOutputBuffer<float>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_INT32:
return evalGeneric(context->getInputBuffer<int32_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<int32_t>(kBeginTensor),
context->getInputShape(kBeginTensor),
context->getInputBuffer<int32_t>(kSizeTensor),
context->getInputShape(kSizeTensor),
context->getOutputBuffer<int32_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM:
return evalGeneric(context->getInputBuffer<uint8_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<int32_t>(kBeginTensor),
context->getInputShape(kBeginTensor),
context->getInputBuffer<int32_t>(kSizeTensor),
context->getInputShape(kSizeTensor),
context->getOutputBuffer<uint8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
return evalGeneric(context->getInputBuffer<int8_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<int32_t>(kBeginTensor),
context->getInputShape(kBeginTensor),
context->getInputBuffer<int32_t>(kSizeTensor),
context->getInputShape(kSizeTensor),
context->getOutputBuffer<int8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace slice
NN_REGISTER_OPERATION(SLICE, slice::kOperationName, slice::validate, slice::prepare, slice::execute,
.allowZeroSizedInput = true);
} // namespace nn
} // namespace android
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