Struct tch::TrainableCModule

pub struct TrainableCModule { /* private fields */ }

The trainable version of a jit PyTorch module.

These modules can be created via the TorchScript python api.

Implementations

impl TrainableCModule

pub fn load<T: AsRef<Path>>( module_path: T, path: Path<'_> ) -> Result<Self, TchError>

Loads a PyTorch saved JIT module from a file.

This function also adds the tensors from the JIT module to the VarStore path passed as argument so that the module can be trained.

pub fn load_data<T: Read>( data: &mut T, path: Path<'_> ) -> Result<Self, TchError>

Loads a PyTorch saved JIT model from a read instance.

This function also adds the tensors from the JIT module to the VarStore path passed as argument so that the module can be trained.

pub fn save<T: AsRef<Path>>(&self, module_path: T) -> Result<(), TchError>

pub fn f_set_train(&mut self) -> Result<(), TchError>

Switches the module to training mode.

pub fn set_train(&mut self)

Switches the module to training mode.

pub fn f_set_eval(&mut self) -> Result<(), TchError>

Switches the module to evaluation mode.

pub fn set_eval(&mut self)

Switches the module to evaluation mode.

pub fn forward_ts<T: Borrow<Tensor>>( &self, ts: &[T] ) -> Result<Tensor, TchError>

Performs the forward pass for a model on some specified tensor inputs.

pub fn forward_is<T: Borrow<IValue>>( &self, ts: &[T] ) -> Result<IValue, TchError>

Performs the forward pass for a model on some specified ivalue inputs.

pub fn method_ts<T: Borrow<Tensor>>( &self, method_name: &str, ts: &[T] ) -> Result<Tensor, TchError>

Runs a specified entry point for a model on some given tensor inputs.

pub fn method_is<T: Borrow<IValue>>( &self, method_name: &str, ts: &[T] ) -> Result<IValue, TchError>

Runs a specified entry point for a model on some given ivalue inputs.

Trait Implementations

impl Debug for TrainableCModule

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl ModuleT for TrainableCModule

fn forward_t(&self, xs: &Tensor, _train: bool) -> Tensor

fn batch_accuracy_for_logits( &self, xs: &Tensor, ys: &Tensor, d: Device, batch_size: i64 ) -> f64