61c6c84964
This change moves //tensorflow/contrib/lite to //tensorflow/lite in preparation for TensorFlow 2.0's deprecation of contrib/. If you refer to TF Lite build targets or headers, you will need to update them manually. If you use TF Lite from the TensorFlow python package, "tf.contrib.lite" now points to "tf.lite". Please update your imports as soon as possible. For more details, see https://groups.google.com/a/tensorflow.org/forum/#!topic/tflite/iIIXOTOFvwQ @angersson and @aselle are conducting this migration. Please contact them if you have any further questions. PiperOrigin-RevId: 219536476
81 lines
2.5 KiB
C++
81 lines
2.5 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 "tensorflow/lite/c/c_api_internal.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 neg {
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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 = GetInput(context, node, kInputTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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output->type = input->type;
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return context->ResizeTensor(context, output,
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TfLiteIntArrayCopy(input->dims));
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}
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template <typename T>
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void Negate(const T* in_data, int num_elements, T* out_data) {
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// TODO(alanchiao): add vectorized version.
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for (int i = 0; i < num_elements; ++i) {
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out_data[i] = -in_data[i];
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}
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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const int num_elements = NumElements(input);
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switch (input->type) {
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case kTfLiteInt64:
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Negate(input->data.i64, num_elements, output->data.i64);
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break;
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case kTfLiteInt32:
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Negate(input->data.i32, num_elements, output->data.i32);
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break;
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case kTfLiteFloat32:
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Negate(input->data.f, num_elements, output->data.f);
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break;
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default:
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context->ReportError(
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context,
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"Neg only currently supports int64, int32, and float32, got %d.",
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input->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 neg
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TfLiteRegistration* Register_NEG() {
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static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
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neg::Prepare, neg::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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