oneflow.nn.ParameterList

class oneflow.nn.ParameterList(parameters=None)

Holds parameters in a list.

ParameterList can be indexed like a regular Python list, but parameters it contains are properly registered, and will be visible by all Module methods.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ParameterList.html?#torch.nn.ParameterList.

Parameters

parameters (iterable, optional) – an iterable of Parameter to add

>>> import oneflow as flow
>>> import oneflow.nn as nn

>>> class MyModule(nn.Module):
...    def __init__(self):
...        super(MyModule, self).__init__()
...        self.params = nn.ParameterList([nn.Parameter(flow.randn(10, 10)) for i in range(10)])
...
...    def forward(self, x):
...        # ParameterList can act as an iterable, or be indexed using ints
...        for i, p in enumerate(self.params):
...            x = self.params[i // 2].mm(x) + p.mm(x)
...        return x

>>> model = MyModule()
>>> model.params
ParameterList(
    (0): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (1): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (2): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (3): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (4): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (5): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (6): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (7): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (8): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
    (9): Parameter containing: [<class 'oneflow.nn.Parameter'> of size 10x10]
)

__init__(parameters=None) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__(input)	Call self as a function.
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattr__(name)
__getattribute__(name, /)	Return getattr(self, name).
__getitem__(idx)
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__iadd__(parameters)
__init__([parameters])	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__iter__()
__le__(value, /)	Return self<=value.
__len__()
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value)	Implement setattr(self, name, value).
__setitem__(idx, param)
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_apply(fn[, applied_dict])
_get_abs_string_index(idx)	Get the absolute index for the list of modules
_get_name()
_load_from_state_dict(state_dict, prefix, …)
_named_members(get_members_fn[, prefix, recurse])
_replicate_for_data_parallel()
_save_to_state_dict(destination, prefix, …)
_shallow_repr()
add_module(name, module)	Adds a child module to the current module.
append(parameter)	Appends a given parameter at the end of the list.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extend(parameters)	Appends parameters from a Python iterable to the end of the list.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(*args, **kwargs)
half()	Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns 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])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
to([device])	Moves the parameters and buffers.
to_consistent(*args, **kwargs)	This interface is no longer available, please use oneflow.nn.Module.to_global() instead.
to_global([placement, sbp])	Convert the parameters and buffers to global.
train([mode])	Sets the module in training mode.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.