1970c2158b
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages. We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`). PiperOrigin-RevId: 332521299 Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56
78 lines
2.6 KiB
C++
78 lines
2.6 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 <stdint.h>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor.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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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace rank {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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TfLiteStatus Prepare(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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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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output->type = kTfLiteInt32;
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// By design, the input shape is always known at the time of Prepare, even
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// if the preceding op that generates |input| is dynamic. Thus, we can
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// always compute the rank immediately, without waiting for Eval.
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SetTensorToPersistentRo(output);
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// Rank produces a 0-D int32 Tensor representing the rank of input.
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TfLiteIntArray* output_size = TfLiteIntArrayCreate(0);
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TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
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TF_LITE_ENSURE_EQ(context, NumDimensions(output), 0);
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// Immediately propagate the known rank to the output tensor. This allows
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// downstream ops that rely on the value to use it during prepare.
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if (output->type == kTfLiteInt32) {
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int32_t* output_data = GetTensorData<int32_t>(output);
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*output_data = NumDimensions(input);
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} else {
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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return kTfLiteOk;
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}
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} // namespace rank
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TfLiteRegistration* Register_RANK() {
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static TfLiteRegistration r = {nullptr, nullptr, rank::Prepare, rank::Eval};
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return &r;
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}
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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