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69 lines
2.5 KiB
69 lines
2.5 KiB
/*
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* Copyright (C) 2019 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 "LegacyUtils.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 rank_op {
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constexpr uint32_t kNumInputs = 1;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputScalar = 0;
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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(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32 ||
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inputType == OperandType::TENSOR_INT32 ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM ||
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inputType == OperandType::TENSOR_QUANT16_SYMM ||
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inputType == OperandType::TENSOR_BOOL8 ||
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inputType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
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inputType == OperandType::TENSOR_QUANT16_ASYMM ||
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inputType == OperandType::TENSOR_QUANT8_SYMM ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
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<< "Incorrect input type for a RANK op: " << inputType;
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NN_RET_CHECK(validateOutputTypes(context, {OperandType::INT32}));
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return Version::ANDROID_R;
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape output = context->getOutputShape(kOutputScalar);
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return context->setOutputShape(kOutputScalar, output);
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}
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bool execute(IOperationExecutionContext* context) {
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*context->getOutputBuffer<int32_t>(kOutputScalar) =
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getNumberOfDimensions(context->getInputShape(kInputTensor));
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return true;
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}
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} // namespace rank_op
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NN_REGISTER_OPERATION(RANK, "RANK", rank_op::validate, rank_op::prepare, rank_op::execute);
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} // namespace nn
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} // namespace android
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