LibtorchUtils Namespace Reference

Functions
template<typename DataType >
void	vectorToTensor (const std::vector< DataType > &vector, torch::Tensor &tensor, const bool detach=false)
	Utility function that converts a standard vector to a torch::Tensor.
template<typename DataType >
torch::Tensor	vectorToTensorCopy (const std::vector< DataType > &vector, c10::IntArrayRef sizes)
	Utility function that creates an owning tensor copy of a standard vector.
template<typename DataType >
torch::Tensor	vectorToTensorView (std::vector< DataType > &vector, c10::IntArrayRef sizes)
	Utility function that creates a non-owning tensor view of a standard vector.
void	moveToLibtorchDevice (torch::Tensor &tensor, const torch::DeviceType device_type)
	Move a tensor to the configured libtorch device.
torch::Tensor	toCPUContiguous (const torch::Tensor &tensor)
	Return a detached contiguous CPU copy of a tensor.
template<typename DataType >
void	tensorToVector (torch::Tensor &tensor, std::vector< DataType > &vector)
	Utility function that converts a torch::Tensor to a standard vector.
template torch::Tensor	vectorToTensorCopy< Real > (const std::vector< Real > &vector, c10::IntArrayRef sizes)
template void	vectorToTensor< Real > (const std::vector< Real > &vector, torch::Tensor &tensor, const bool detach)
template torch::Tensor	vectorToTensorView< Real > (std::vector< Real > &vector, c10::IntArrayRef sizes)
template void	tensorToVector< Real > (torch::Tensor &tensor, std::vector< Real > &vector)

Function Documentation

moveToLibtorchDevice()

void LibtorchUtils::moveToLibtorchDevice	(	torch::Tensor &	tensor,
		const torch::DeviceType	device_type
	)

Move a tensor to the configured libtorch device.

Parameters

tensor	The tensor to move
device_type	The target torch device type

Definition at line 100 of file LibtorchUtils.C.

{
  tensor = tensor.to(device_type);
}

tensorToVector()

template<typename DataType >

void LibtorchUtils::tensorToVector	(	torch::Tensor &	tensor,
		std::vector< DataType > &	vector
	)

Utility function that converts a torch::Tensor to a standard vector.

Template Parameters

DataType

The type of data (float,double, etc.) which the vector is filled with

Parameters

tensor	The tensor which needs to be converted
vector	The output vector

Definition at line 114 of file LibtorchUtils.C.

{
  try
  {
    tensor.data_ptr<DataType>();
  }
  catch (const c10::Error & e)
  {
    mooseError(
        "Cannot cast tensor values to", MooseUtils::prettyCppType<DataType>(), "!\n", e.msg());
  }
 
  const auto & sizes = tensor.sizes();
 
  long int max_size = 0;
  for (const auto & dim_size : sizes)
    // We do this comparison because XCode complains if we use std::max
    max_size = dim_size > max_size ? dim_size : max_size;
 
  mooseAssert(max_size == tensor.numel(), "The given tensor should be one-dimensional!");
  vector = {tensor.data_ptr<DataType>(), tensor.data_ptr<DataType>() + tensor.numel()};
}

tensorToVector< Real >()

template void LibtorchUtils::tensorToVector< Real >	(	torch::Tensor &	tensor,
		std::vector< Real > &	vector
	)

toCPUContiguous()

torch::Tensor LibtorchUtils::toCPUContiguous

(

const torch::Tensor &

tensor

)

Return a detached contiguous CPU copy of a tensor.

This is for call sites that read tensor storage through CPU accessors or data_ptr(). Moving a tensor to CPU does not guarantee that logical tensor order is backed by a dense linear memory layout; contiguous() makes that invariant explicit.

Parameters

tensor

The tensor to copy to CPU

Definition at line 106 of file LibtorchUtils.C.

{
  // CPU accessors can handle strides, but data_ptr()-based reads require dense logical order.
  return tensor.detach().to(tensor.options().device(at::kCPU)).contiguous();
}

Referenced by dataStore().

vectorToTensor()

template<typename DataType >

void LibtorchUtils::vectorToTensor	(	const std::vector< DataType > &	vector,
		torch::Tensor &	tensor,
		const bool	detach = false
	)

Utility function that converts a standard vector to a torch::Tensor.

Template Parameters

DataType

The type of data (float,double, etc.) which the vector is filled with

Parameters

vector	The vector that needs to be converted
tensor	The output tensor
detach	If the gradient information needs to be detached during the conversion

Definition at line 71 of file LibtorchUtils.C.

{
  tensor = vectorToTensorCopy(vector, {long(vector.size()), 1});
 
  if (detach)
    tensor = tensor.detach();
}

Referenced by LibtorchNeuralNetControl::prepareInputTensor().

vectorToTensor< Real >()

template void LibtorchUtils::vectorToTensor< Real >	(	const std::vector< Real > &	vector,
		torch::Tensor &	tensor,
		const bool	detach
	)

vectorToTensorCopy()

template<typename DataType >

torch::Tensor LibtorchUtils::vectorToTensorCopy	(	const std::vector< DataType > &	vector,
		c10::IntArrayRef	sizes
	)

Utility function that creates an owning tensor copy of a standard vector.

Template Parameters

DataType

The vector element type

Parameters

vector	The vector that needs to be copied
sizes	The desired tensor shape

Definition at line 52 of file LibtorchUtils.C.

{
  checkTensorShape(vector, sizes);
 
  const auto options = tensorOptions<DataType>();
  auto tensor = torch::empty(sizes, options);
 
  if (!vector.empty())
    std::copy(vector.begin(), vector.end(), tensor.template data_ptr<DataType>());
 
  return tensor;
}

Referenced by vectorToTensor().

vectorToTensorCopy< Real >()

template torch::Tensor LibtorchUtils::vectorToTensorCopy< Real >	(	const std::vector< Real > &	vector,
		c10::IntArrayRef	sizes
	)

vectorToTensorView()

template<typename DataType >

torch::Tensor LibtorchUtils::vectorToTensorView	(	std::vector< DataType > &	vector,
		c10::IntArrayRef	sizes
	)

Utility function that creates a non-owning tensor view of a standard vector.

The returned tensor shares the mutable storage of the provided vector, so the vector must outlive the tensor and may be modified through the tensor.

Template Parameters

DataType

The vector element type

Parameters

vector	The vector that needs to be wrapped
sizes	The desired tensor shape

Definition at line 85 of file LibtorchUtils.C.

{
  checkTensorShape(vector, sizes);
 
  const auto options = tensorOptions<DataType>();
  if (vector.empty())
    return torch::empty(sizes, options);
 
  return torch::from_blob(vector.data(), sizes, options);
}

vectorToTensorView< Real >()

template torch::Tensor LibtorchUtils::vectorToTensorView< Real >	(	std::vector< Real > &	vector,
		c10::IntArrayRef	sizes
	)