/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/serialize.h (5196B)
#pragma once
#include
#include
#include
#include
#include
namespace torch {
/// Serializes the given `value`.
/// There must be an overload of `operator<<` between `serialize::OutputArchive`
/// and `Value` for this method to be well-formed. Currently, such an overload
/// is provided for (subclasses of):
///
/// - `torch::nn::Module`,
/// - `torch::optim::Optimizer`
/// - `torch::Tensor`
///
/// To perform the serialization, a `serialize::OutputArchive` is constructed,
/// and all arguments after the `value` are forwarded to its `save_to` method.
/// For example, you can pass a filename, or an `ostream`.
///
/// \rst
/// .. code-block:: cpp
///
/// torch::nn::Linear model(3, 4);
/// torch::save(model, "model.pt");
///
/// torch::optim::SGD sgd(/*lr=*/0.9);
/// std::ostringstream stream;
/// // Note that the same stream cannot be used in multiple torch::save(...)
/// // invocations, otherwise the header will be corrupted.
/// torch::save(sgd, stream);
///
/// auto tensor = torch::ones({3, 4});
/// torch::save(tensor, "my_tensor.pt");
/// \endrst
template
void save(const Value& value, SaveToArgs&&... args) {
serialize::OutputArchive archive(
std::make_shared());
archive << value;
archive.save_to(std::forward(args)...);
}
/// Serializes the given `tensor_vec` of type `std::vector`.
///
/// To perform the serialization, a `serialize::OutputArchive` is constructed,
/// and all arguments after the `tensor_vec` are forwarded to its `save_to`
/// method. For example, you can pass a filename, or an `ostream`.
///
/// \rst
/// .. code-block:: cpp
///
/// std::vector tensor_vec = { torch::randn({1, 2}), torch::randn({3, 4}) };
/// torch::save(tensor_vec, "my_tensor_vec.pt");
///
/// std::vector tensor_vec = { torch::randn({5, 6}), torch::randn({7, 8}) };
/// std::ostringstream stream;
/// // Note that the same stream cannot be used in multiple torch::save(...)
/// // invocations, otherwise the header will be corrupted.
/// torch::save(tensor_vec, stream);
/// \endrst
template
void save(const std::vector& tensor_vec, SaveToArgs&&... args) {
serialize::OutputArchive archive(
std::make_shared());
for (const auto i : c10::irange(tensor_vec.size())) {
auto& value = tensor_vec[i];
archive.write(c10::to_string(i), value);
}
archive.save_to(std::forward(args)...);
}
TORCH_API std::vector pickle_save(const torch::IValue& ivalue);
TORCH_API torch::IValue pickle_load(const std::vector& data);
/// Deserializes the given `value`.
/// There must be an overload of `operator>>` between `serialize::InputArchive`
/// and `Value` for this method to be well-formed. Currently, such an overload
/// is provided for (subclasses of):
///
/// - `torch::nn::Module`,
/// - `torch::optim::Optimizer`
/// - `torch::Tensor`
///
/// To perform the serialization, a `serialize::InputArchive` is constructed,
/// and all arguments after the `value` are forwarded to its `load_from` method.
/// For example, you can pass a filename, or an `istream`.
///
/// \rst
/// .. code-block:: cpp
///
/// torch::nn::Linear model(3, 4);
/// torch::load(model, "model.pt");
///
/// torch::optim::SGD sgd(/*lr=*/0.9);
/// std::istringstream stream("...");
/// torch::load(sgd, stream);
///
/// auto tensor = torch::ones({3, 4});
/// torch::load(tensor, "my_tensor.pt");
/// \endrst
template
void load(Value& value, LoadFromArgs&&... args) {
serialize::InputArchive archive;
archive.load_from(std::forward(args)...);
archive >> value;
}
/// Deserializes the given `tensor_vec` of type `std::vector`.
///
/// To perform the serialization, a `serialize::InputArchive` is constructed,
/// and all arguments after the `value` are forwarded to its `load_from` method.
/// For example, you can pass a filename, or an `istream`.
///
/// \rst
/// .. code-block:: cpp
///
/// std::vector tensor_vec;
/// torch::load(tensor_vec, "my_tensor_vec.pt");
///
/// std::vector tensor_vec;
/// std::istringstream stream("...");
/// torch::load(tensor_vec, stream);
/// \endrst
template
void load(std::vector& tensor_vec, LoadFromArgs&&... args) {
serialize::InputArchive archive;
archive.load_from(std::forward(args)...);
// NOTE: The number of elements in the serialized `std::vector`
// is not known ahead of time, so we need a while-loop to increment the index,
// and use `archive.try_read(...)` to check whether we have reached the end of
// the serialized `std::vector`.
size_t index = 0;
torch::Tensor value;
while (archive.try_read(c10::to_string(index), value)) {
tensor_vec.push_back(std::move(value));
value = torch::Tensor();
index++;
}
}
} // namespace torch