load_state_dict(self.model, checkpoint['model_state_dict'])

This line of code loads the saved state dictionary of the model from a previously saved checkpoint into the current model. The `load_state_dict` function is used to load the saved state dictionary into the model, and the `checkpoint` variable is a dictionary that contains the saved state dictionary of the model.

The `self.model` is the current model object to which we want to load the saved state dictionary. The `checkpoint['model_state_dict']` refers to the saved state dictionary of the model, which is stored in the `checkpoint` variable.

Overall, this line of code is used in deep learning frameworks like PyTorch to resume training or inference from a previously saved checkpoint.

相关问题

def restore(self, save_path, model=None): if model is None: model = self.alg.model checkpoint = torch.load(save_path,map_location=torch.device('cpu')) # import pdb # pdb.set_trace() # a = torch.load_state_dict(checkpoint) model.load_state_dict(checkpoint)

这段代码是用来从指定路径 `save_path` 中加载模型参数到 `model` 中的。如果没有指定 `model`，则默认使用 `self.alg.model`。这个方法使用了 PyTorch 的 `load()` 方法来加载保存的模型参数。`map_location` 参数指定了将模型参数加载到 CPU 上，因为有些模型参数可能是在 GPU 上保存的，这样加载到 CPU 上可以避免 GPU 内存不足的问题。加载完成后，模型参数就被成功恢复了。

if isinstance(self.pretrained, str): self.apply(_init_weights) logger = get_root_logger() logger.info(f'load model from: {self.pretrained}') checkpoint = torch.load(self.pretrained, map_location='cpu') state_dict = checkpoint['model'] state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).repeat(1,1,self.patch_size[0],1,1) / self.patch_size[0] ## Duplicate weights for temporal attention and temporal norm # new_state_dict = state_dict.copy() # for key in state_dict: # if 'blocks' in key and 'attn' in key and 'relative' not in key and 'mask' not in key: # new_key = key.replace('attn','temporal_attn') # if not new_key in state_dict: # new_state_dict[new_key] = state_dict[key] # else: # new_state_dict[new_key] = state_dict[new_key] # if 'blocks' in key and 'norm1' in key and 'relative' not in key and 'mask' not in key: # new_key = key.replace('norm1','temporal_norm') # if not new_key in state_dict: # new_state_dict[new_key] = state_dict[key] # else: # new_state_dict[new_key] = state_dict[new_key] # state_dict = new_state_dict msg = self.load_state_dict(state_dict, strict=False) logger.info('Missing keys: {}'.format(msg.missing_keys)) logger.info('Unexpected keys: {}'.format(msg.unexpected_keys)) logger.info(f"=> loaded successfully '{self.pretrained}'") del checkpoint torch.cuda.empty_cache() elif self.pretrained is None: self.apply(_init_weights) else: raise TypeError('pretrained must be a str or None')

这段代码是初始化模型的代码。首先判断是否需要加载预训练模型，如果需要，则从指定的路径加载预训练模型的参数，并将模型的 patch_embed.proj.weight 层的权重进行重新计算，以适应输入的 patch 大小。然后将加载的参数应用到当前模型中。如果没有指定预训练模型，则直接进行权重初始化。如果预训练参数既不是字符串也不是 None，则会报错。