register_parameter

nn.Parameters 与 register_parameter 都会向 _parameters写入参数，但是后者可以支持字符串命名。
从源码中可以看到，nn.Parameters为Module添加属性的方式也是通过register_parameter向 _parameters写入参数。

    def __setattr__(self, name, value):def remove_from(*dicts):for d in dicts:if name in d:del d[name]params = self.__dict__.get('_parameters')if isinstance(value, Parameter):if params is None:raise AttributeError("cannot assign parameters before Module.__init__() call")remove_from(self.__dict__, self._buffers, self._modules)self.register_parameter(name, value)elif params is not None and name in params:if value is not None:raise TypeError("cannot assign '{}' as parameter '{}' ""(torch.nn.Parameter or None expected)".format(torch.typename(value), name))self.register_parameter(name, value)else:modules = self.__dict__.get('_modules')if isinstance(value, Module):if modules is None:raise AttributeError("cannot assign module before Module.__init__() call")remove_from(self.__dict__, self._parameters, self._buffers)modules[name] = valueelif modules is not None and name in modules:if value is not None:raise TypeError("cannot assign '{}' as child module '{}' ""(torch.nn.Module or None expected)".format(torch.typename(value), name))modules[name] = valueelse:buffers = self.__dict__.get('_buffers')if buffers is not None and name in buffers:if value is not None and not isinstance(value, torch.Tensor):raise TypeError("cannot assign '{}' as buffer '{}' ""(torch.Tensor or None expected)".format(torch.typename(value), name))buffers[name] = valueelse:object.__setattr__(self, name, value)

import torch
from torch import nnclass MyModel(nn.Module):def __init__(self):super(MyModel, self).__init__()print('before register:\n', self._parameters, end='\n\n')self.register_parameter('my_param1', nn.Parameter(torch.randn(3, 3)))print('after register and before nn.Parameter:\n', self._parameters, end='\n\n')self.my_param2 = nn.Parameter(torch.randn(2, 2))print('after register and nn.Parameter:\n', self._parameters, end='\n\n')def forward(self, x):return xmymodel = MyModel()for k, v in mymodel.named_parameters():print(k, v)

程序返回为:

before register:OrderedDict()after register and before nn.Parameter:OrderedDict([('my_param1', Parameter containing:
tensor([[-1.3542, -0.4591, -2.0968],[-0.4345, -0.9904, -0.9329],[ 1.4990, -1.7540, -0.4479]], requires_grad=True))])after register and nn.Parameter:OrderedDict([('my_param1', Parameter containing:
tensor([[-1.3542, -0.4591, -2.0968],[-0.4345, -0.9904, -0.9329],[ 1.4990, -1.7540, -0.4479]], requires_grad=True)), ('my_param2', Parameter containing:
tensor([[ 1.0205, -1.3145],[-1.1108,  0.4288]], requires_grad=True))])my_param1 Parameter containing:
tensor([[-1.3542, -0.4591, -2.0968],[-0.4345, -0.9904, -0.9329],[ 1.4990, -1.7540, -0.4479]], requires_grad=True)
my_param2 Parameter containing:
tensor([[ 1.0205, -1.3145],[-1.1108,  0.4288]], requires_grad=True)

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register_parameter

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