missing_keys, unexpected_keys = model.load_state_dict(weights_dict, strict=False) missing_keys,unexpected_keys strict=False True 时,代表有什么要什me,每一个键都有。 False 时,有什么我要什么,没有的不勉强。 missing_keys, unexpected_keys 返回值:缺失的键,不期望的键。
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: {}. '...
defload_state_dict(self,state_dict,strict=True):missing_keys=[]unexpected_keys=[]error_msgs=[]# copy state_dict so _load_from_state_dict can modify itmetadata=getattr(state_dict,'_metadata',None)state_dict=state_dict.copy()ifmetadataisnotNone:state_dict._metadata=metadatadefload(module,pre...
{})module._load_from_state_dict(state_dict,prefix,local_metadata,strict,missing_keys,unexpected_keys,error_msgs)forname,childinmodule._modules.items():ifchild is not None:load(child,prefix+name+'.')load(self)ifstrict:error_msg=''iflen(unexpected_keys)>0:error_msgs.insert(0,'Unexpected...
state_dict(dict) – a dict containing parameters and persistent buffers. strict(bool,optional) – whether to strictly enforce that the keys instate_dictmatch the keys returned by this module’sstate_dict()function. Default:True Returns missing_keysis a list of str containing the missing keys ...
state_dict(dict) – a dict containing parameters and persistent buffers. strict(bool, optional) – whether to strictly enforce that the keys instate_dictmatch the keys returned by this module’sstate_dict()function. Default:True Returns
param)，完成参数拷贝。在if strict部分中，主要判断参数拷贝过程中是否有unexpected_keys或missing_keys，如有，则抛出错误，终止执行。当然，当strict=False时，会忽略这些细节。总结而言，state_dict和load_state_dict是Pytorch中用于保存和加载模型参数的关键函数，它们通过递归方式确保模型参数的准确恢复。
usingmodel.load_state_dict, an error occurs:RuntimeError: Error(s) in loading state_dict for GraphModule: Missing key(s), Unexpected key(s) in state_dict. 🐛 Describe the bug I'm reporting this issue due to errors related to capture_pre_autograd_graph and torch.compile in QAT. ...
Hi there, As I couldn't find a SpectralNorm class to add it in a nn.Sequential juste like nn.BatchNorm2d, I used the nn.utils.spectral_norm function and applied it to my conv layers. When I load a state_dict in a modified version of a nn...
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)forname, childinmodule._modules.items():ifchildisnotNone: load(child, prefix+ name +'.') load(model, prefix=''ifhasattr(model,'bert')else'bert.')#todo: 从这边,model.cls.predictions.bias,这个偏值项的权...