torch::Tensor convert_to_tensor()

in torchaudio/csrc/sox/utils.cpp [153:193]

• 41 lines of code
• 10 McCabe index (conditional complexity)

torch::Tensor convert_to_tensor(
    sox_sample_t* buffer,
    const int32_t num_samples,
    const int32_t num_channels,
    const caffe2::TypeMeta dtype,
    const bool normalize,
    const bool channels_first) {
  torch::Tensor t;
  uint64_t dummy = 0;
  SOX_SAMPLE_LOCALS;
  if (normalize || dtype == torch::kFloat32) {
    t = torch::empty(
        {num_samples / num_channels, num_channels}, torch::kFloat32);
    auto ptr = t.data_ptr<float_t>();
    for (int32_t i = 0; i < num_samples; ++i) {
      ptr[i] = SOX_SAMPLE_TO_FLOAT_32BIT(buffer[i], dummy);
    }
  } else if (dtype == torch::kInt32) {
    t = torch::from_blob(
            buffer, {num_samples / num_channels, num_channels}, torch::kInt32)
            .clone();
  } else if (dtype == torch::kInt16) {
    t = torch::empty({num_samples / num_channels, num_channels}, torch::kInt16);
    auto ptr = t.data_ptr<int16_t>();
    for (int32_t i = 0; i < num_samples; ++i) {
      ptr[i] = SOX_SAMPLE_TO_SIGNED_16BIT(buffer[i], dummy);
    }
  } else if (dtype == torch::kUInt8) {
    t = torch::empty({num_samples / num_channels, num_channels}, torch::kUInt8);
    auto ptr = t.data_ptr<uint8_t>();
    for (int32_t i = 0; i < num_samples; ++i) {
      ptr[i] = SOX_SAMPLE_TO_UNSIGNED_8BIT(buffer[i], dummy);
    }
  } else {
    throw std::runtime_error("Unsupported dtype.");
  }
  if (channels_first) {
    t = t.transpose(1, 0);
  }
  return t.contiguous();
}