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 all Module methods.

ModuleDict is an ordered dictionary that respects

  • the order of insertion, and

  • in update(), the order of the merged OrderedDict, dict (started from Python 3.6) or another ModuleDict (the argument to update()).

Note that update() with other unordered mapping types (e.g., Python’s plain dict 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.