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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
#include "caffe2/operators/ctc_greedy_decoder_op.h"
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namespace caffe2 {
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namespace {
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const float* getTensorDataPtr(const Tensor& tensor, int t, int n) {
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const auto dims = tensor.sizes();
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CAFFE_ENFORCE_EQ(dims.size(), 3);
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int64_t offset = (t * dims[1] + n) * dims[2];
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CAFFE_ENFORCE_LT(offset, tensor.numel());
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return tensor.template data<float>() + offset;
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}
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} // namespace
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template <>
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bool CTCGreedyDecoderOp<CPUContext>::RunOnDevice() {
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// [max_time_step, batch_size, num_classes]
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auto& inputs = Input(INPUTS);
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// [batch_size]
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// [total_decoded_output]
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const auto inputs_dims = inputs.sizes();
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int32_t max_time_step = inputs_dims[0];
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int32_t batch_size = inputs_dims[1];
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int32_t num_classes = inputs_dims[2];
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// [batch_size]
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const int* seq_len_data =
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(InputSize() == 2) ? Input(SEQ_LEN).data<int>() : nullptr;
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vector<int> values_cach;
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auto* output_len =
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Output(OUTPUT_LEN, vector<int64_t>{batch_size}, at::dtype<int>());
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int* output_len_data = output_len->template mutable_data<int>();
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for (int32_t i = 0; i < batch_size; ++i) {
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int previous_label = 0, t_dec = 0;
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int32_t seq_len_i = (seq_len_data) ? seq_len_data[i] : max_time_step;
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CAFFE_ENFORCE_LE(seq_len_i, max_time_step);
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for (int32_t t = 0; t < seq_len_i; ++t) {
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auto* prob_data = getTensorDataPtr(inputs, t, i);
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int curr_label =
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// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
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std::max_element(prob_data, prob_data + num_classes) - prob_data;
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if (curr_label != 0 &&
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(!merge_repeated_ || (previous_label != curr_label))) {
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t_dec++;
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values_cach.push_back(curr_label);
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}
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previous_label = curr_label;
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}
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output_len_data[i] = t_dec;
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}
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int32_t values_cach_size = values_cach.size();
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auto* values =
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Output(VALUES, vector<int64_t>{values_cach_size}, at::dtype<int>());
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int* values_data = values->mutable_data<int>();
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for (size_t i = 0; i < values_cach.size(); ++i) {
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values_data[i] = values_cach.at(i);
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}
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values_cach.clear();
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return true;
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}
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REGISTER_CPU_OPERATOR(CTCGreedyDecoder, CTCGreedyDecoderOp<CPUContext>);
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OPERATOR_SCHEMA(CTCGreedyDecoder)
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.NumInputs(1, 2)
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.NumOutputs(2)
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.Arg(
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"merge_repeated",
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"When merge_repeated is true, merge repeated classes in output.")
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.SetDoc("Greedy decoder for connectionist temporal classification.")
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.Input(
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0,
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"INPUTS",
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"3D float Tensor sized [max_time, batch_size, num_classes]")
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.Input(
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1,
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"SEQ_LEN",
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"(optional) 1D int vector containing sequence lengths, "
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"having size [batch_size]"
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"seq_len will be set to max_time if not provided")
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.Output(
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0,
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"OUTPUT_LEN",
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"Output_len matrix size (batch). "
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"The row store: [decoded_length]")
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.Output(
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1,
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"VALUES",
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"Values vector, size (total_decoded_outputs). "
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"The vector stores the decoded classes")
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.InheritOnnxSchema();
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SHOULD_NOT_DO_GRADIENT(CTCGreedyDecoder);
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} // namespace caffe2
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