MLPY/Lib/site-packages/onnx/common/tensor.h

// Copyright (c) ONNX Project Contributors

/*
 * SPDX-License-Identifier: Apache-2.0
 */

// ATTENTION: The code in this file is highly EXPERIMENTAL.
// Adventurous users should note that the APIs will probably change.

#pragma once

#include <cmath>
#include <functional>
#include <numeric>
#include <string>
#include <utility>
#include <vector>

#include "onnx/common/assertions.h"
#include "onnx/onnx_pb.h"
#include "onnx/string_utils.h"

namespace ONNX_NAMESPACE {

struct Tensor final {
 private:
  bool is_segment_;
  int64_t segment_begin_;
  int64_t segment_end_;
  bool has_name_;
  std::string name_;
  int32_t elem_type_;
  std::vector<int64_t> sizes_;

  std::vector<float> float_data_;
  std::vector<double> double_data_;
  std::vector<int32_t> int32_data_;
  std::vector<int64_t> int64_data_;
  std::vector<uint64_t> uint64_data_;
  std::vector<std::string> string_data_;

  bool is_raw_data_;
  std::string raw_data_;

 public:
  Tensor()
      : is_segment_(false),
        segment_begin_(0),
        segment_end_(0),
        has_name_(false),
        elem_type_(ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED),
        is_raw_data_(false) {}

  Tensor(const Tensor& other)
      : is_segment_(other.is_segment_),
        segment_begin_(other.segment_begin_),
        segment_end_(other.segment_end_),
        has_name_(other.has_name_),
        elem_type_(other.elem_type_),
        sizes_(other.sizes_),
        float_data_(other.float_data_),
        double_data_(other.double_data_),
        int32_data_(other.int32_data_),
        int64_data_(other.int64_data_),
        uint64_data_(other.uint64_data_),
        is_raw_data_(other.is_raw_data_) {
    // Deep copy. Avoid copy on write when using gcc<5.0
    string_data_.resize(other.string_data_.size());
    for (unsigned int i = 0; i < other.string_data_.size(); ++i) {
      string_data_[i] = std::string(other.string_data_[i].data(), other.string_data_[i].size());
    }
    name_ = std::string(other.name_.data(), other.name_.size());
    raw_data_ = std::string(other.raw_data_.data(), other.raw_data_.size());
  }
  Tensor(Tensor&&) = default;
  ~Tensor() = default;

  friend void swap(Tensor& first, Tensor& second) {
    using std::swap;
    swap(first.is_segment_, second.is_segment_);
    swap(first.segment_begin_, second.segment_begin_);
    swap(first.segment_end_, second.segment_end_);
    swap(first.has_name_, second.has_name_);
    swap(first.name_, second.name_);
    swap(first.elem_type_, second.elem_type_);
    swap(first.sizes_, second.sizes_);
    swap(first.float_data_, second.float_data_);
    swap(first.double_data_, second.double_data_);
    swap(first.int32_data_, second.int32_data_);
    swap(first.int64_data_, second.int64_data_);
    swap(first.uint64_data_, second.uint64_data_);
    swap(first.is_raw_data_, second.is_raw_data_);
    swap(first.string_data_, second.string_data_);
    swap(first.raw_data_, second.raw_data_);
  }

  Tensor& operator=(Tensor other) noexcept {
    swap(*this, other);
    return *this;
  }

  const std::vector<int64_t>& sizes() const {
    return sizes_;
  }
  std::vector<int64_t>& sizes() {
    return sizes_;
  }
  /// if tensor is a scalar, the sizes is empty, but the element number is actually 1.
  /// size_from_dim() cannot handle this case, while elem_num() handles it correctly
  int64_t elem_num() const {
    return std::accumulate(sizes_.begin(), sizes_.end(), (int64_t)1, std::multiplies<int64_t>{});
  }
  int64_t size_from_dim(int dim) const {
    if (dim < 0) {
      dim += (int)sizes_.size();
    }
    ONNX_ASSERT(dim >= 0 && (size_t)dim < sizes_.size());
    return std::accumulate(sizes_.begin() + dim, sizes_.end(), (int64_t)1, std::multiplies<int64_t>{});
  }

  int32_t elem_type() const {
    return elem_type_;
  }

  int32_t& elem_type() {
    return elem_type_;
  }

  std::vector<std::string>& strings() {
    return string_data_;
  }

  const std::vector<std::string>& strings() const {
    return string_data_;
  }

  std::vector<float>& floats() {
    return float_data_;
  }

  const std::vector<float>& floats() const {
    return float_data_;
  }

  std::vector<double>& doubles() {
    return double_data_;
  }

  const std::vector<double>& doubles() const {
    return double_data_;
  }

  std::vector<int32_t>& int32s() {
    return int32_data_;
  }

  const std::vector<int32_t>& int32s() const {
    return int32_data_;
  }

  std::vector<int64_t>& int64s() {
    return int64_data_;
  }

  const std::vector<int64_t>& int64s() const {
    return int64_data_;
  }

  std::vector<uint64_t>& uint64s() {
    return uint64_data_;
  }

  const std::vector<uint64_t>& uint64s() const {
    return uint64_data_;
  }

  const std::string& raw() const {
    return raw_data_;
  }

  void set_raw_data(std::string raw_data) {
    is_raw_data_ = true;
    raw_data_ = std::move(raw_data);
  }

  template <typename T>
  T* data();

  template <typename T>
  const T* data() const;

  bool is_segment() const {
    return is_segment_;
  }

  int64_t segment_begin() const {
    return segment_begin_;
  }

  int64_t segment_end() const {
    return segment_end_;
  }

  void set_segment_begin_and_end(int64_t begin, int64_t end) {
    is_segment_ = true;
    segment_begin_ = begin;
    segment_end_ = end;
  }

  bool hasName() const {
    return has_name_;
  }

  const std::string& name() const {
    return name_;
  }

  void setName(std::string name) {
    has_name_ = true;
    name_ = std::move(name);
  }

  bool is_raw_data() const {
    return is_raw_data_;
  }
};

template <>
inline std::string* Tensor::data<std::string>() {
  ONNX_ASSERTM(
      !is_raw_data(),
      "data type is string. string content is required to be stored in repeated bytes string_data field."
      "raw_data type cannot be string.");
  return string_data_.data();
}
template <>
inline const std::string* Tensor::data<std::string>() const {
  ONNX_ASSERTM(
      !is_raw_data(),
      "data type is string. string content is required to be stored in repeated bytes string_data field."
      "raw_data type cannot be string.");
  return string_data_.data();
}

#define define_data(type, field)                             \
  template <>                                                \
  inline type* Tensor::data<type>() {                        \
    if (is_raw_data_) {                                      \
      return (type*)const_cast<char*>(&raw_data_.data()[0]); \
    } else {                                                 \
      return field.data();                                   \
    }                                                        \
  }                                                          \
                                                             \
  template <>                                                \
  inline const type* Tensor::data<type>() const {            \
    if (is_raw_data_) {                                      \
      return (const type*)(raw_data_.data());                \
    } else {                                                 \
      return field.data();                                   \
    }                                                        \
  }

define_data(float, float_data_);
define_data(double, double_data_);
define_data(int32_t, int32_data_);
define_data(int64_t, int64_data_);
define_data(uint64_t, uint64_data_);
#undef define_data

} // namespace ONNX_NAMESPACE