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] = value
elif 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] = value
else:
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] = value
else:
object.__setattr__(self, name, value)
import torch
from torch import nn
class 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 x
mymodel = 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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