/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ // Generate a list of skip grams from an input. // // Options: // ngram_size: num of words for each output item. // max_skip_size: max num of words to skip. // The op generates ngrams when it is 0. // include_all_ngrams: include all ngrams with size up to ngram_size. // // Input: // A string tensor to generate n-grams. // Dim = {1} // // Output: // A list of strings, each of which contains ngram_size words. // Dim = {num_ngram} #include <ctype.h> #include <vector> #include "tensorflow/lite/core/c/builtin_op_data.h" #include "tensorflow/lite/core/c/common.h" #include "tensorflow/lite/kernels/internal/compatibility.h" #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/string_util.h" namespace tflite { namespace ops { namespace builtin { namespace { TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { … } bool ShouldIncludeCurrentNgram(const TfLiteSkipGramParams* params, int size) { … } bool ShouldStepInRecursion(const TfLiteSkipGramParams* params, const std::vector<int>& stack, int stack_idx, int num_words) { … } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { … } } // namespace TfLiteRegistration* Register_SKIP_GRAM() { … } } // namespace builtin } // namespace ops } // namespace tflite