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
174 lines
6.0 KiB
C++
174 lines
6.0 KiB
C++
/* Copyright 2018 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/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/reference_ops.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 reverse_sequence {
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namespace {
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constexpr int kInputTensor = 0;
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constexpr int kSeqLengthsTensor = 1;
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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), 2);
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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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const TfLiteTensor* seq_lengths;
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TF_LITE_ENSURE_OK(
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context, GetInputSafe(context, node, kSeqLengthsTensor, &seq_lengths));
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TF_LITE_ENSURE_EQ(context, NumDimensions(seq_lengths), 1);
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if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
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input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
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input->type != kTfLiteInt64) {
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context->ReportError(context,
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"Type '%s' is not supported by reverse_sequence.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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if (seq_lengths->type != kTfLiteInt32 && seq_lengths->type != kTfLiteInt64) {
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context->ReportError(
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context, "Seq_lengths type '%s' is not supported by reverse_sequence.",
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TfLiteTypeGetName(seq_lengths->type));
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return kTfLiteError;
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}
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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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TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
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TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
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return context->ResizeTensor(context, output, output_shape);
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}
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template <typename T, typename TS>
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TfLiteStatus ReverseSequenceImpl(TfLiteContext* context, TfLiteNode* node) {
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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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const TfLiteTensor* seq_lengths_tensor;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kSeqLengthsTensor,
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&seq_lengths_tensor));
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const TS* seq_lengths = GetTensorData<TS>(seq_lengths_tensor);
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auto* params =
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reinterpret_cast<TfLiteReverseSequenceParams*>(node->builtin_data);
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int seq_dim = params->seq_dim;
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int batch_dim = params->batch_dim;
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TF_LITE_ENSURE(context, seq_dim >= 0);
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TF_LITE_ENSURE(context, batch_dim >= 0);
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TF_LITE_ENSURE(context, seq_dim != batch_dim);
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TF_LITE_ENSURE(context, seq_dim < NumDimensions(input));
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TF_LITE_ENSURE(context, batch_dim < NumDimensions(input));
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TF_LITE_ENSURE_EQ(context, SizeOfDimension(seq_lengths_tensor, 0),
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SizeOfDimension(input, batch_dim));
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for (int i = 0; i < NumDimensions(seq_lengths_tensor); ++i) {
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TF_LITE_ENSURE(context, seq_lengths[i] <= SizeOfDimension(input, seq_dim));
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}
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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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reference_ops::ReverseSequence<T, TS>(
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seq_lengths, seq_dim, batch_dim, GetTensorShape(input),
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GetTensorData<T>(input), GetTensorShape(output),
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GetTensorData<T>(output));
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return kTfLiteOk;
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}
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template <typename T>
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TfLiteStatus ReverseSequenceHelper(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* seq_lengths_tensor;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kSeqLengthsTensor,
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&seq_lengths_tensor));
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switch (seq_lengths_tensor->type) {
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case kTfLiteInt32: {
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return ReverseSequenceImpl<T, int32_t>(context, node);
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}
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case kTfLiteInt64: {
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return ReverseSequenceImpl<T, int64_t>(context, node);
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}
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default: {
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context->ReportError(
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context,
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"Seq_lengths type '%s' is not supported by reverse_sequence.",
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TfLiteTypeGetName(seq_lengths_tensor->type));
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return kTfLiteError;
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}
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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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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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switch (output->type) {
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case kTfLiteFloat32: {
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return ReverseSequenceHelper<float>(context, node);
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}
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case kTfLiteUInt8: {
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return ReverseSequenceHelper<uint8_t>(context, node);
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}
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case kTfLiteInt16: {
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return ReverseSequenceHelper<int16_t>(context, node);
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}
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case kTfLiteInt32: {
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return ReverseSequenceHelper<int32_t>(context, node);
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}
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case kTfLiteInt64: {
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return ReverseSequenceHelper<int64_t>(context, node);
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}
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default: {
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context->ReportError(context,
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"Type '%s' is not supported by reverse_sequence.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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}
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return kTfLiteOk;
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} // namespace
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} // namespace
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} // namespace reverse_sequence
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TfLiteRegistration* Register_REVERSE_SEQUENCE() {
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static TfLiteRegistration r = {nullptr, nullptr, reverse_sequence::Prepare,
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reverse_sequence::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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