wehub-resource-sync
115 lines
3.9 KiB
C++
115 lines
3.9 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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// Copied from tensorflow/core/util/ctc/ctc_decoder.h
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// TODO(b/111524997): Remove this file.
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#ifndef TENSORFLOW_LITE_KERNELS_CTC_CTC_DECODER_H_
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#define TENSORFLOW_LITE_KERNELS_CTC_CTC_DECODER_H_
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#include <memory>
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#include <vector>
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#include "Eigen/Core" // from @eigen_archive
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namespace tflite {
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namespace custom {
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namespace ctc {
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// The CTCDecoder is an abstract interface to be implemented when providing a
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// decoding method on the timestep output of a RNN trained with CTC loss.
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//
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// The two types of decoding available are:
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// - greedy path, through the CTCGreedyDecoder
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// - beam search, through the CTCBeamSearchDecoder
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class CTCDecoder {
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public:
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typedef Eigen::Map<const Eigen::ArrayXi> SequenceLength;
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typedef Eigen::Map<const Eigen::MatrixXf> Input;
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typedef std::vector<std::vector<int>> Output;
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typedef Eigen::Map<Eigen::MatrixXf> ScoreOutput;
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CTCDecoder(int num_classes, int batch_size, bool merge_repeated)
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: num_classes_(num_classes),
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blank_index_(num_classes - 1),
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batch_size_(batch_size),
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merge_repeated_(merge_repeated) {}
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virtual ~CTCDecoder() {}
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// Dimensionality of the input/output is expected to be:
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// - seq_len[b] - b = 0 to batch_size_
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// - input[t].rows(b) - t = 0 to timesteps; b = 0 t batch_size_
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// - output.size() specifies the number of beams to be returned.
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// - scores(b, i) - b = 0 to batch_size; i = 0 to output.size()
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virtual bool Decode(const SequenceLength& seq_len,
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const std::vector<Input>& input,
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std::vector<Output>* output, ScoreOutput* scores) = 0;
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int batch_size() { return batch_size_; }
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int num_classes() { return num_classes_; }
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protected:
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int num_classes_;
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int blank_index_;
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int batch_size_;
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bool merge_repeated_;
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};
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// CTCGreedyDecoder is an implementation of the simple best path decoding
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// algorithm, selecting at each timestep the most likely class at each timestep.
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class CTCGreedyDecoder : public CTCDecoder {
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public:
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CTCGreedyDecoder(int num_classes, int batch_size, bool merge_repeated)
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: CTCDecoder(num_classes, batch_size, merge_repeated) {}
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bool Decode(const CTCDecoder::SequenceLength& seq_len,
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const std::vector<CTCDecoder::Input>& input,
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std::vector<CTCDecoder::Output>* output,
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CTCDecoder::ScoreOutput* scores) override {
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if (output->empty() || (*output)[0].size() < batch_size_) {
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return false;
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}
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if (scores->rows() < batch_size_ || scores->cols() == 0) {
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return false;
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}
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// For each batch entry, identify the transitions
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for (int b = 0; b < batch_size_; ++b) {
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int seq_len_b = seq_len[b];
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// Only writing to beam 0
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std::vector<int>& output_b = (*output)[0][b];
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int prev_class_ix = -1;
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(*scores)(b, 0) = 0;
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for (int t = 0; t < seq_len_b; ++t) {
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auto row = input[t].row(b);
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int max_class_ix;
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(*scores)(b, 0) += -row.maxCoeff(&max_class_ix);
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if (max_class_ix != blank_index_ &&
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!(merge_repeated_ && max_class_ix == prev_class_ix)) {
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output_b.push_back(max_class_ix);
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}
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prev_class_ix = max_class_ix;
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}
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}
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return true;
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}
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};
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} // namespace ctc
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} // namespace custom
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_CTC_CTC_DECODER_H_
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