mirror of
https://github.com/vee1e/tflite-micro.git
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5f5fcc8af6
refactor init, prepare , and eval functions to be unique names for kernels who's name starts with the Letters A-M BUG=[b/313963581](https://b.corp.google.com/issues/313963581)
128 lines
4.6 KiB
C++
128 lines
4.6 KiB
C++
/* Copyright 2020 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/kernels/internal/reference/floor_mod.h"
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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/reference/process_broadcast_shapes.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/micro_log.h"
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#include "tensorflow/lite/micro/micro_utils.h"
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// OLD-TODO(b/117523611): We should factor out a binary_op and put binary ops
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// there.
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namespace tflite {
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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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// OLD-TODO(b/117912880): Support quantization.
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TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
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MicroContext* micro_context = GetMicroContext(context);
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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TfLiteTensor* input1 =
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micro_context->AllocateTempInputTensor(node, kInputTensor1);
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TF_LITE_ENSURE(context, input1 != nullptr);
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TfLiteTensor* input2 =
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micro_context->AllocateTempInputTensor(node, kInputTensor2);
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TF_LITE_ENSURE(context, input2 != nullptr);
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TfLiteTensor* output =
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micro_context->AllocateTempOutputTensor(node, kOutputTensor);
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TF_LITE_ENSURE(context, output != nullptr);
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TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
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TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type);
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micro_context->DeallocateTempTfLiteTensor(input1);
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micro_context->DeallocateTempTfLiteTensor(input2);
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micro_context->DeallocateTempTfLiteTensor(output);
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return kTfLiteOk;
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}
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void* FloorModInit(TfLiteContext* context, const char* buffer, size_t length) {
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return nullptr;
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}
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TfLiteStatus FloorModPrepare(TfLiteContext* context, TfLiteNode* node) {
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return CalculateOpData(context, node);
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}
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template <typename T>
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TfLiteStatus EvalFloorMod(TfLiteContext* context, bool requires_broadcast,
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const TfLiteEvalTensor* input1,
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const TfLiteEvalTensor* input2,
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TfLiteEvalTensor* output) {
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const T* denominator_data = tflite::micro::GetTensorData<T>(input2);
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if (requires_broadcast) {
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reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>(
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tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<T>(input1),
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tflite::micro::GetTensorShape(input2), denominator_data,
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>);
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} else {
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reference_ops::BinaryFunction<T, T, T>(
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tflite::micro::GetTensorShape(input1),
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tflite::micro::GetTensorData<T>(input1),
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tflite::micro::GetTensorShape(input2), denominator_data,
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>);
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}
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return kTfLiteOk;
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}
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TfLiteStatus FloorModEval(TfLiteContext* context, TfLiteNode* node) {
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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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bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteFloat32: {
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return EvalFloorMod<float>(context, requires_broadcast, input1, input2,
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output);
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}
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default: {
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MicroPrintf("Type '%s' is not supported by FLOOR_MOD.",
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TfLiteTypeGetName(input1->type));
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return kTfLiteError;
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}
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}
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}
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} // namespace
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TFLMRegistration Register_FLOOR_MOD() {
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return tflite::micro::RegisterOp(FloorModInit, FloorModPrepare, FloorModEval);
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}
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} // namespace tflite
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