Source code for torch.nn.modules.container

from collections import OrderedDict, Iterable
import string
import torch
import warnings
from .module import Module


class Container(Module):

    def __init__(self, **kwargs):
        super(Container, self).__init__()
        # DeprecationWarning is ignored by default <sigh>
        warnings.warn("nn.Container is deprecated. All of it's functionality "
                      "is now implemented in nn.Module. Subclass that instead.")
        for key, value in kwargs.items():
            self.add_module(key, value)


[docs]class Sequential(Module): r"""A sequential container. Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in. To make it easier to understand, given is a small example:: # Example of using Sequential model = nn.Sequential( nn.Conv2d(1,20,5), nn.ReLU(), nn.Conv2d(20,64,5), nn.ReLU() ) # Example of using Sequential with OrderedDict model = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2', nn.ReLU()) ])) """ def __init__(self, *args): super(Sequential, self).__init__() if len(args) == 1 and isinstance(args[0], OrderedDict): for key, module in args[0].items(): self.add_module(key, module) else: for idx, module in enumerate(args): self.add_module(str(idx), module) def __getitem__(self, idx): if isinstance(idx, slice): return Sequential(OrderedDict(list(self._modules.items())[idx])) else: if not (-len(self) <= idx < len(self)): raise IndexError('index {} is out of range'.format(idx)) if idx < 0: idx += len(self) it = iter(self._modules.values()) for i in range(idx): next(it) return next(it) def __len__(self): return len(self._modules) def __dir__(self): keys = super(Sequential, self).__dir__() keys = [key for key in keys if not key.isdigit()] return keys def forward(self, input): for module in self._modules.values(): input = module(input) return input
[docs]class ModuleList(Module): r"""Holds submodules in a list. ModuleList can be indexed like a regular Python list, but modules it contains are properly registered, and will be visible by all Module methods. Arguments: modules (iterable, optional): an iterable of modules to add Example:: 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 """ def __init__(self, modules=None): super(ModuleList, self).__init__() if modules is not None: self += modules def __getitem__(self, idx): if isinstance(idx, slice): return ModuleList(list(self._modules.values())[idx]) else: if not (-len(self) <= idx < len(self)): raise IndexError('index {} is out of range'.format(idx)) if idx < 0: idx += len(self) return self._modules[str(idx)] def __setitem__(self, idx, module): return setattr(self, str(idx), module) def __len__(self): return len(self._modules) def __iter__(self): return iter(self._modules.values()) def __iadd__(self, modules): return self.extend(modules) def __dir__(self): keys = super(ModuleList, self).__dir__() keys = [key for key in keys if not key.isdigit()] return keys
[docs] def append(self, module): r"""Appends a given module to the end of the list. Arguments: module (nn.Module): module to append """ self.add_module(str(len(self)), module) return self
[docs] def extend(self, modules): r"""Appends modules from a Python iterable to the end of the list. Arguments: modules (iterable): iterable of modules to append """ if not isinstance(modules, Iterable): raise TypeError("ModuleList.extend should be called with an " "iterable, but got " + type(modules).__name__) offset = len(self) for i, module in enumerate(modules): self.add_module(str(offset + i), module) return self
[docs]class ParameterList(Module): r"""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. Arguments: parameters (iterable, optional): an iterable of :class:`~torch.nn.Parameter`` to add Example:: class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.params = nn.ParameterList([nn.Parameter(torch.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 """ def __init__(self, parameters=None): super(ParameterList, self).__init__() if parameters is not None: self += parameters def __getitem__(self, idx): if isinstance(idx, slice): return ParameterList(list(self._parameters.values())[idx]) else: if not (-len(self) <= idx < len(self)): raise IndexError('index {} is out of range'.format(idx)) if idx < 0: idx += len(self) return self._parameters[str(idx)] def __setitem__(self, idx, param): return self.register_parameter(str(idx), param) def __len__(self): return len(self._parameters) def __iter__(self): return iter(self._parameters.values()) def __iadd__(self, parameters): return self.extend(parameters) def __dir__(self): keys = super(ParameterList, self).__dir__() keys = [key for key in keys if not key.isdigit()] return keys
[docs] def append(self, parameter): """Appends a given parameter at the end of the list. Arguments: parameter (nn.Parameter): parameter to append """ self.register_parameter(str(len(self)), parameter) return self
[docs] def extend(self, parameters): """Appends parameters from a Python iterable to the end of the list. Arguments: parameters (iterable): iterable of parameters to append """ if not isinstance(parameters, Iterable): raise TypeError("ParameterList.extend should be called with an " "iterable, but got " + type(parameters).__name__) offset = len(self) for i, param in enumerate(parameters): self.register_parameter(str(offset + i), param) return self
def __repr__(self): tmpstr = self.__class__.__name__ + '(\n' for k, p in self._parameters.items(): size_str = 'x'.join(str(size) for size in p.size()) device_str = '' if not p.is_cuda else ' (GPU {})'.format(p.get_device()) parastr = 'Parameter containing: [{} of size {}{}]'.format( torch.typename(p.data), size_str, device_str) tmpstr = tmpstr + ' (' + k + '): ' + parastr + '\n' tmpstr = tmpstr + ')' return tmpstr