MRINufftAutoGrad#
- class mrinufft.operators.autodiff.MRINufftAutoGrad(nufft_op, wrt_data=True, wrt_traj=False)[source]#
Bases:
Module
Wraps the NUFFT operator to support torch autodiff.
- Parameters:
nufft_op (Classic Non differentiable MRI-NUFFT operator.)
Methods
__init__
Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module
Add a child module to the current module.
Compute the adjoint k-space -> image.
apply
Apply
fn
recursively to every submodule (as returned by.children()
) as well as self.bfloat16
Casts all floating point parameters and buffers to
bfloat16
datatype.buffers
Return an iterator over module buffers.
children
Return an iterator over immediate children modules.
compile
Compile this Module's forward using
torch.compile()
.cpu
Move all model parameters and buffers to the CPU.
cuda
Move all model parameters and buffers to the GPU.
double
Casts all floating point parameters and buffers to
double
datatype.eval
Set the module in evaluation mode.
extra_repr
Set the extra representation of the module.
float
Casts all floating point parameters and buffers to
float
datatype.forward
Define the computation performed at every call.
get_buffer
Return the buffer given by
target
if it exists, otherwise throw an error.get_extra_state
Return any extra state to include in the module's state_dict.
get_parameter
Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
Return the submodule given by
target
if it exists, otherwise throw an error.half
Casts all floating point parameters and buffers to
half
datatype.ipu
Move all model parameters and buffers to the IPU.
load_state_dict
Copy parameters and buffers from
state_dict
into this module and its descendants.modules
Return an iterator over all modules in the network.
mtia
Move all model parameters and buffers to the MTIA.
named_buffers
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Compute the forward image -> k-space.
parameters
Return an iterator over module parameters.
register_backward_hook
Register a backward hook on the module.
register_buffer
Add a buffer to the module.
register_forward_hook
Register a forward hook on the module.
register_forward_pre_hook
Register a forward pre-hook on the module.
register_full_backward_hook
Register a backward hook on the module.
register_full_backward_pre_hook
Register a backward pre-hook on the module.
register_load_state_dict_post_hook
Register a post-hook to be run after module's
load_state_dict()
is called.register_load_state_dict_pre_hook
Register a pre-hook to be run before module's
load_state_dict()
is called.register_module
Alias for
add_module()
.register_parameter
Add a parameter to the module.
register_state_dict_post_hook
Register a post-hook for the
state_dict()
method.register_state_dict_pre_hook
Register a pre-hook for the
state_dict()
method.requires_grad_
Change if autograd should record operations on parameters in this module.
set_extra_state
Set extra state contained in the loaded state_dict.
set_submodule
Set the submodule given by
target
if it exists, otherwise throw an error.share_memory
See
torch.Tensor.share_memory_()
.state_dict
Return a dictionary containing references to the whole state of the module.
to
Move and/or cast the parameters and buffers.
to_empty
Move the parameters and buffers to the specified device without copying storage.
train
Set the module in training mode.
type
Casts all parameters and buffers to
dst_type
.xpu
Move all model parameters and buffers to the XPU.
zero_grad
Reset gradients of all model parameters.
Attributes
T_destination
call_super_init
dump_patches
Get the samples.
training
- property samples#
Get the samples.