| /* Copyright 2020 The TensorFlow Authors. All Rights Reserved. |
| |
| 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. |
| ==============================================================================*/ |
| |
| #include "tensorflow/lite/kernels/internal/reference/floor_mod.h" |
| |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/reference/binary_function.h" |
| #include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" |
| #include "tensorflow/lite/kernels/internal/types.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| #include "tensorflow/lite/micro/micro_utils.h" |
| |
| // OLD-TODO(b/117523611): We should factor out a binary_op and put binary ops |
| // there. |
| namespace tflite { |
| namespace { |
| |
| // Input/output tensor index. |
| constexpr int kInputTensor1 = 0; |
| constexpr int kInputTensor2 = 1; |
| constexpr int kOutputTensor = 0; |
| |
| // OLD-TODO(b/117912880): Support quantization. |
| |
| TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) { |
| MicroContext* micro_context = GetMicroContext(context); |
| |
| TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
| TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
| |
| TfLiteTensor* input1 = |
| micro_context->AllocateTempInputTensor(node, kInputTensor1); |
| TF_LITE_ENSURE(context, input1 != nullptr); |
| TfLiteTensor* input2 = |
| micro_context->AllocateTempInputTensor(node, kInputTensor2); |
| TF_LITE_ENSURE(context, input2 != nullptr); |
| TfLiteTensor* output = |
| micro_context->AllocateTempOutputTensor(node, kOutputTensor); |
| TF_LITE_ENSURE(context, output != nullptr); |
| |
| TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); |
| TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type); |
| |
| micro_context->DeallocateTempTfLiteTensor(input1); |
| micro_context->DeallocateTempTfLiteTensor(input2); |
| micro_context->DeallocateTempTfLiteTensor(output); |
| |
| return kTfLiteOk; |
| } |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| return nullptr; |
| } |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| return CalculateOpData(context, node); |
| } |
| |
| template <typename T> |
| TfLiteStatus EvalFloorMod(TfLiteContext* context, bool requires_broadcast, |
| const TfLiteEvalTensor* input1, |
| const TfLiteEvalTensor* input2, |
| TfLiteEvalTensor* output) { |
| const T* denominator_data = tflite::micro::GetTensorData<T>(input2); |
| |
| if (requires_broadcast) { |
| reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>( |
| tflite::micro::GetTensorShape(input1), |
| tflite::micro::GetTensorData<T>(input1), |
| tflite::micro::GetTensorShape(input2), denominator_data, |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>); |
| } else { |
| reference_ops::BinaryFunction<T, T, T>( |
| tflite::micro::GetTensorShape(input1), |
| tflite::micro::GetTensorData<T>(input1), |
| tflite::micro::GetTensorShape(input2), denominator_data, |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>); |
| } |
| |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| |
| switch (input1->type) { |
| case kTfLiteFloat32: { |
| return EvalFloorMod<float>(context, requires_broadcast, input1, input2, |
| output); |
| } |
| default: { |
| MicroPrintf("Type '%s' is not supported by FLOOR_MOD.", |
| TfLiteTypeGetName(input1->type)); |
| return kTfLiteError; |
| } |
| } |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_FLOOR_MOD() { |
| return tflite::micro::RegisterOp(Init, Prepare, Eval); |
| } |
| |
| } // namespace tflite |