oneflow.nn.ModuleDict¶
-
class
oneflow.nn.
ModuleDict
(modules: Optional[Mapping[str, oneflow.nn.module.Module]] = None)¶ Holds submodules in a dictionary.
The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ModuleDict.html?#torch.nn.ModuleDict.
ModuleDict
can be indexed like a regular Python dictionary, but modules it contains are properly registered, and will be visible by allModule
methods.ModuleDict
is an ordered dictionary that respectsthe order of insertion, and
in
update()
, the order of the mergedOrderedDict
,dict
(started from Python 3.6) or anotherModuleDict
(the argument toupdate()
).
Note that
update()
with other unordered mapping types (e.g., Python’s plaindict
before Python version 3.6) does not preserve the order of the merged mapping.- Parameters
modules (iterable, optional) – a mapping (dictionary) of (string: module) or an iterable of key-value pairs of type (string, module)
>>> import oneflow.nn as nn >>> class MyModule(nn.Module): ... def __init__(self): ... super(MyModule, self).__init__() ... self.choices = nn.ModuleDict({ ... 'conv': nn.Conv2d(10, 10, 3), ... 'pool': nn.MaxPool2d(3) ... }) ... self.activations = nn.ModuleDict([ ... ['lrelu', nn.LeakyReLU()], ... ['prelu', nn.PReLU()] ... ]) ... def forward(self, x, choice, act): ... x = self.choices[choice](x) ... x = self.activations[act](x) ... return x >>> model = MyModule() >>> model.choices ModuleDict( (conv): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1)) (pool): MaxPool2d(kernel_size=(3, 3), stride=(3, 3), padding=(0, 0), dilation=(1, 1)) )
-
__init__
(modules: Optional[Mapping[str, oneflow.nn.module.Module]] = None) → None¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__call__
(*args, **kwargs)Call self as a function.
__contains__
(key)__delattr__
(name, /)Implement delattr(self, name).
__delitem__
(key)__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__
(key)__gt__
(value, /)Return self>value.
__hash__
()Return hash(self).
__init__
([modules])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__
(key, module)__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_name
()_load_from_state_dict
(state_dict, prefix, …)_named_members
(get_members_fn[, prefix, recurse])_save_to_state_dict
(destination, prefix, …)_shallow_repr
()add_module
(name, module)Adds a child module to the current module.
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.
clear
()Remove all items from the ModuleDict.
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.
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.items
()Return an iterable of the ModuleDict key/value pairs.
keys
()Return an iterable of the ModuleDict keys.
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.
pop
(key)Remove key from the ModuleDict and return its module.
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.
update
(modules)values
()Return an iterable of the ModuleDict values.
zero_grad
([set_to_none])Sets gradients of all model parameters to zero.