在 Pytorch 中一种模型保存和加载的方式如下:
# save
torch.save(model.state_dict(), PATH)
# load
model = MyModel(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()model.state_dict()其实返回的是一个OrderDict，存储了网络结构的名字和对应的参数，下面看看源代码如何实现的。
state_dict
# torch.nn.modules.module.py
class Module(object):
def state_dict(self, destination=None, prefix='', keep_vars=False):
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version)
for name, param in self._parameters.items():
if param is not None:
destination[prefix + name] = param if keep_vars else param.data
for name, buf in self._buffers.items():
if buf is not None:
destination[prefix + name] = buf if keep_vars else buf.data
for name, module in self._modules.items():
if module is not None:
module.state_dict(destination, prefix + name + '.', keep_vars=keep_vars)
for hook in self._state_dict_hooks.values():
hook_result = hook(self, destination, prefix, local_metadata)
if hook_result is not None:
destination = hook_result
return destination可以看到state_dict函数中遍历了4中元素，分别是_paramters,_buffers,_modules和_state_dict_hooks,前面三者在之前的文章已经介绍区别，最后一种就是在读取state_dict时希望执行的操作，一般为空，所以不做考虑。另外有一点需要注意的是，在读取Module时采用的递归的读取方式，并且名字间使用.做分割，以方便后面load_state_dict读取参数。
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.my_tensor = torch.randn(1) # 参数直接作为模型类成员变量
self.register_buffer('my_buffer', torch.randn(1)) # 参数注册为 buffer
self.my_param = nn.Parameter(torch.randn(1))
self.fc = nn.Linear(2,2,bias=False)
self.conv = nn.Conv2d(2,1,1)
self.fc2 = nn.Linear(2,2,bias=False)
self.f3 = self.fc
def forward(self, x):
return x
model = MyModel()
print(model.state_dict())
>>>OrderedDict([('my_param', tensor([-0.3052])), ('my_buffer', tensor([0.5583])), ('fc.weight', tensor([[ 0.6322, -0.0255],
[-0.4747, -0.0530]])), ('conv.weight', tensor([[[[ 0.3346]],
[[-0.2962]]]])), ('conv.bias', tensor([0.5205])), ('fc2.weight', tensor([[-0.4949, 0.2815],
[ 0.3006, 0.0768]])), ('f3.weight', tensor([[ 0.6322, -0.0255],
[-0.4747, -0.0530]]))])可以看到最后的确输出了三种参数。
load_state_dict
下面的代码中我们可以分成两个部分看，
load(self)
这个函数会递归地对模型进行参数恢复，其中的_load_from_state_dict的源码附在文末。
首先我们需要明确state_dict这个变量表示你之前保存的模型参数序列，而_load_from_state_dict函数中的local_state表示你的代码中定义的模型的结构。
那么_load_from_state_dict的作用简单理解就是假如我们现在需要对一个名为conv.weight的子模块做参数恢复，那么就以递归的方式先判断conv是否在staet__dict和local_state中，如果不在就把conv添加到unexpected_keys中去，否则递归的判断conv.weight是否存在，如果都存在就执行param.copy_(input_param),这样就完成了conv.weight的参数拷贝。
if strict：
这个部分的作用是判断上面参数拷贝过程中是否有unexpected_keys或者missing_keys,如果有就报错，代码不能继续执行。当然，如果strict=False，则会忽略这些细节。
def load_state_dict(self, state_dict, strict=True):
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(self)
if strict:
error_msg = ''
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join('"{}"'.format(k) for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join('"{}"'.format(k) for k in missing_keys)))
if len(error_msgs) > 0:
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
self.__class__.__name__, "\n\t".join(error_msgs)))_load_from_state_dict
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
for hook in self._load_state_dict_pre_hooks.values():
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
local_name_params = itertools.chain(self._parameters.items(), self._buffers.items())
local_state = {k: v.data for k, v in local_name_params if v is not None}
for name, param in local_state.items():
key = prefix + name
if key in state_dict:
input_param = state_dict[key]
# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
if len(param.shape) == 0 and len(input_param.shape) == 1:
input_param = input_param[0]
if input_param.shape != param.shape:
# local shape should match the one in checkpoint
error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
'the shape in current model is {}.'
.format(key, input_param.shape, param.shape))
continue
if isinstance(input_param, Parameter):
# backwards compatibility for serialized parameters
input_param = input_param.data
try:
param.copy_(input_param)
except Exception:
error_msgs.append('While copying the parameter named "{}", '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}.'
.format(key, param.size(), input_param.size()))
elif strict:
missing_keys.append(key)
if strict:
for key, input_param in state_dict.items():
if key.startswith(prefix):
input_name = key[len(prefix):]
input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child
if input_name not in self._modules and input_name not in local_state:
unexpected_keys.append(key)