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
147 lines
5.0 KiB
C++
147 lines
5.0 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 <stddef.h>
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#include <stdint.h>
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#include "ruy/profiler/instrumentation.h" // from @ruy
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/binary_function.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 squared_difference {
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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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struct OpData {
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bool requires_broadcast;
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};
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template <typename T>
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T SquaredDifference(T input1, T input2) {
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const T difference = input1 - input2;
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return difference * difference;
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}
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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auto* data = new OpData;
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data->requires_broadcast = false;
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return data;
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}
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void Free(TfLiteContext* context, void* buffer) {
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delete reinterpret_cast<OpData*>(buffer);
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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OpData* data = reinterpret_cast<OpData*>(node->user_data);
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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* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor2, &input2));
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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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TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
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output->type = input2->type;
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data->requires_broadcast = !HaveSameShapes(input1, input2);
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TfLiteIntArray* output_size = nullptr;
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if (data->requires_broadcast) {
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TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
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context, input1, input2, &output_size));
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} else {
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output_size = TfLiteIntArrayCopy(input1->dims);
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}
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return context->ResizeTensor(context, output, output_size);
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}
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template <typename T>
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void EvalSquaredDifference(TfLiteContext* context, TfLiteNode* node,
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const OpData* data, const TfLiteTensor* input1,
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const TfLiteTensor* input2, TfLiteTensor* output) {
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if (data->requires_broadcast) {
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reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>(
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GetTensorShape(input1), GetTensorData<T>(input1),
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GetTensorShape(input2), GetTensorData<T>(input2),
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GetTensorShape(output), GetTensorData<T>(output), SquaredDifference<T>);
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} else {
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reference_ops::BinaryFunction<T, T, T>(
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GetTensorShape(input1), GetTensorData<T>(input1),
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GetTensorShape(input2), GetTensorData<T>(input2),
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GetTensorShape(output), GetTensorData<T>(output), SquaredDifference<T>);
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}
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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OpData* data = reinterpret_cast<OpData*>(node->user_data);
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ruy::profiler::ScopeLabel label("SquaredDifference");
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor2, &input2));
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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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if (output->type == kTfLiteFloat32) {
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EvalSquaredDifference<float>(context, node, data, input1, input2, output);
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} else if (output->type == kTfLiteInt32) {
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EvalSquaredDifference<int32_t>(context, node, data, input1, input2, output);
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} else {
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context->ReportError(
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context,
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"SquaredDifference only supports FLOAT32 and INT32 now, got %d.",
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output->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace squared_difference
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TfLiteRegistration* Register_SQUARED_DIFFERENCE() {
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static TfLiteRegistration r = {
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squared_difference::Init, squared_difference::Free,
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squared_difference::Prepare, squared_difference::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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