Class ModuleList

python/oneflow/nn/modules/container.py:60–105

Holds submodules in a list. The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ModuleList.html?#torch.nn.ModuleList. :class:`~oneflow.nn.ModuleList` can be indexed like a regular Python list, but

class ModuleList(get_list(Module)):
    """Holds submodules in a list.

    The interface is consistent with PyTorch.    
    The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ModuleList.html?#torch.nn.ModuleList.
    
    :class:`~oneflow.nn.ModuleList` can be indexed like a regular Python list, but
    modules it contains are properly registered, and will be visible by all
    :class:`~oneflow.nn.Module` methods.
    
    Args:
        modules (iterable, optional): an iterable of modules to add
    
    .. code-block:: python

        >>> import oneflow.nn as nn

        >>> class MyModule(nn.Module):
        ...    def __init__(self):
        ...        super(MyModule, self).__init__()
        ...        self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
        ...    def forward(self, x):
        ...        # ModuleList can act as an iterable, or be indexed using ints
        ...        for i, l in enumerate(self.linears):
        ...            x = self.linears[i // 2](x) + l(x)
        ...        return x

        >>> model = MyModule()
        >>> model.linears
        ModuleList(
          (0): Linear(in_features=10, out_features=10, bias=True)
          (1): Linear(in_features=10, out_features=10, bias=True)
          (2): Linear(in_features=10, out_features=10, bias=True)
          (3): Linear(in_features=10, out_features=10, bias=True)
          (4): Linear(in_features=10, out_features=10, bias=True)
          (5): Linear(in_features=10, out_features=10, bias=True)
          (6): Linear(in_features=10, out_features=10, bias=True)
          (7): Linear(in_features=10, out_features=10, bias=True)
          (8): Linear(in_features=10, out_features=10, bias=True)
          (9): Linear(in_features=10, out_features=10, bias=True)
        )
        

    """

    pass


class ModuleDict(get_dict(Module)):