You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
125 lines
5.3 KiB
125 lines
5.3 KiB
/*
|
|
* 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 "OperationResolver.h"
|
|
#include "OperationsUtils.h"
|
|
#include "Tracing.h"
|
|
|
|
namespace android {
|
|
namespace nn {
|
|
namespace channel_shuffle {
|
|
|
|
constexpr char kOperationName[] = "CHANNEL_SHUFFLE";
|
|
|
|
constexpr uint32_t kNumInputs = 3;
|
|
constexpr uint32_t kInputTensor = 0;
|
|
constexpr uint32_t kNumGroups = 1;
|
|
constexpr uint32_t kInputAxis = 2;
|
|
|
|
constexpr uint32_t kNumOutputs = 1;
|
|
constexpr uint32_t kOutputTensor = 0;
|
|
|
|
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;
|
|
}
|
|
|
|
Result<Version> validate(const IOperationValidationContext* context) {
|
|
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
|
|
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
|
|
auto inputType = context->getInputType(kInputTensor);
|
|
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
|
|
inputType == OperandType::TENSOR_FLOAT32 ||
|
|
inputType == OperandType::TENSOR_QUANT8_ASYMM ||
|
|
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
|
|
<< "Unsupported tensor type for operation " << kOperationName;
|
|
const Shape& inputShape = context->getInputShape(kInputTensor);
|
|
if (hasKnownRank(inputShape)) {
|
|
NN_RET_CHECK_LE(getNumberOfDimensions(inputShape), 4);
|
|
}
|
|
NN_RET_CHECK(validateInputTypes(context, {inputType, OperandType::INT32, OperandType::INT32}));
|
|
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
|
|
if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
|
return Version::ANDROID_R;
|
|
} else {
|
|
return Version::ANDROID_Q;
|
|
}
|
|
}
|
|
|
|
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(CHANNEL_SHUFFLE, channel_shuffle::kOperationName, channel_shuffle::validate,
|
|
channel_shuffle::prepare, channel_shuffle::execute);
|
|
|
|
} // namespace nn
|
|
} // namespace android
|