106 lines
3.6 KiB
C++
106 lines
3.6 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/binary_function.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/op_macros.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace logical {
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namespace {
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// Input/output tensor index.
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constexpr int kInputTensor1 = 0;
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constexpr int kInputTensor2 = 1;
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constexpr int kOutputTensor = 0;
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TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node,
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bool (*func)(bool, bool)) {
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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if (tflite::micro::HaveSameShapes(input1, input2)) {
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reference_ops::BinaryFunction<bool, bool, bool>(
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tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<bool>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<bool>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<bool>(output), func);
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} else {
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reference_ops::BroadcastBinaryFunction4DSlow<bool, bool, bool>(
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tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<bool>(input1),
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tflite::micro::GetTensorShape(input2),
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tflite::micro::GetTensorData<bool>(input2),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<bool>(output), func);
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}
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return kTfLiteOk;
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}
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bool LogicalOr(bool x, bool y) { return x || y; }
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TfLiteStatus LogicalOrEval(TfLiteContext* context, TfLiteNode* node) {
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return LogicalImpl(context, node, LogicalOr);
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}
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bool LogicalAnd(bool x, bool y) { return x && y; }
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TfLiteStatus LogicalAndEval(TfLiteContext* context, TfLiteNode* node) {
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return LogicalImpl(context, node, LogicalAnd);
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}
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} // namespace
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} // namespace logical
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TfLiteRegistration Register_LOGICAL_OR() {
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// Init, Free, Prepare, Eval are satisfying the Interface required by
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// TfLiteRegistration.
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return {/*init=*/nullptr,
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/*free=*/nullptr,
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/*prepare=*/nullptr,
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/*invoke=*/logical::LogicalOrEval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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TfLiteRegistration Register_LOGICAL_AND() {
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// Init, Free, Prepare, Eval are satisfying the Interface required by
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// TfLiteRegistration.
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return {/*init=*/nullptr,
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/*free=*/nullptr,
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/*prepare=*/nullptr,
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/*invoke=*/logical::LogicalAndEval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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