pytorch_pfn_extras.nn.ExtendedSequential#
- class pytorch_pfn_extras.nn.ExtendedSequential(*args: Module)#
- class pytorch_pfn_extras.nn.ExtendedSequential(arg: OrderedDict[str, Module])
Bases:
Sequential
Sequential module with extended features from chainer.
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Methods
__init__
(*args)Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module
(name, module)Add a child module to the current module.
append
(module)Append a given module to the end.
apply
(fn)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
([recurse])Return an iterator over module buffers.
children
()Return an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module's forward using
torch.compile()
.cpu
()Move all model parameters and buffers to the CPU.
cuda
([device])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.
extend
(sequential)extra_repr
()Set the extra representation of the module.
float
()Casts all floating point parameters and buffers to
float
datatype.forward
(input)Define the computation performed at every call.
get_buffer
(target)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
(target)Return the parameter given by
target
if it exists, otherwise throw an error.get_submodule
(target)Return the submodule given by
target
if it exists, otherwise throw an error.half
()Casts all floating point parameters and buffers to
half
datatype.insert
(index, module)ipu
([device])Move all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copy parameters and buffers from
state_dict
into this module and its descendants.modules
()Return an iterator over all modules in the network.
named_buffers
([prefix, recurse, ...])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
([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters
([prefix, recurse, ...])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Return an iterator over module parameters.
pop
(key)register_backward_hook
(hook)Register a backward hook on the module.
register_buffer
(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook
(hook, *[, prepend, ...])Register a forward hook on the module.
register_forward_pre_hook
(hook, *[, ...])Register a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Register a backward pre-hook on the module.
register_load_state_dict_post_hook
(hook)Register a post hook to be run after module's
load_state_dict
is called.register_module
(name, module)Alias for
add_module()
.register_parameter
(name, param)Add a parameter to the module.
register_state_dict_pre_hook
(hook)Register a pre-hook for the
load_state_dict()
method.repeat
(n_repeat[, mode])Repeats this Sequential multiple times.
requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)Set extra state contained in the loaded state_dict.
share_memory
()See
torch.Tensor.share_memory_()
.state_dict
(*args[, destination, prefix, ...])Return a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Move and/or cast the parameters and buffers.
to_empty
(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
train
([mode])Set the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Move all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Reset gradients of all model parameters.
Attributes
T_destination
alias of TypeVar('T_destination', bound=
Dict
[str
,Any
])call_super_init
dump_patches
- repeat(n_repeat, mode='init')#
Repeats this Sequential multiple times.
This method returns a
Sequential
object which has original Sequential multiple times repeatedly. Themode
argument means how to copy this sequential to repeat.The functions is supposed to behave the same way as repeat in chainer.
When the mode is set to
init
, the default value, modules will be copied and reinitialized by callingreset_parameters
(or_reset_parameters
) method.To repeat user-defined modules, which have parameters or buffers, with mode=``init`` in this Sequential, you need to implement the
reset_parameters
or_reset_parameters
method to the module to reinitialize parameters and (if necessary) buffers; otherwise the initialization cannot be performed and a warning message will be shown.- Parameters:
n_repeat (int) – Number of times to repeat.
mode (str) – It should be either
init
,copy
, orshare
.init
means parameters of each repeated element in the returnedSequential
will be re-initialized, so that all elements have different initial parameters.copy
means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initial parameters but can be changed independently.share
means all the elements which consist the resultingSequential
object are same object because they are shallow-copied, so that all parameters of elements are shared with each other.
- Return type: