mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-05 11:47:56 +00:00
93d4b1ccc6
efactor init, prepare , and eval functions to be unique names for kernels who's name starts with the Letters N-Z BUG=[b/313963581](https://b.corp.google.com/issues/313963581)
67 lines
2.2 KiB
C++
67 lines
2.2 KiB
C++
/* Copyright 2017 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/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.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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#include "tensorflow/lite/micro/memory_helpers.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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namespace tflite {
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namespace {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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void ExtractShape(const TfLiteEvalTensor* input, int32_t* output_data) {
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for (int i = 0; i < input->dims->size; ++i) {
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output_data[i] = input->dims->data[i];
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}
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}
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TfLiteStatus ShapePrepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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return kTfLiteOk;
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}
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TfLiteStatus ShapeEval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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if (output->type != kTfLiteInt32) {
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MicroPrintf("Output type %s (%d) not supported.",
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TfLiteTypeGetName(output->type), output->type);
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return kTfLiteError;
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} else {
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ExtractShape(input, tflite::micro::GetTensorData<int32_t>(output));
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}
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return kTfLiteOk;
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}
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} // namespace
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TFLMRegistration Register_SHAPE() {
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return tflite::micro::RegisterOp(nullptr, ShapePrepare, ShapeEval);
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}
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} // namespace tflite
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