torch.optim.Optimizer.load_state_dict#

Optimizer.load_state_dict(state_dict)[source]#

Load the optimizer state.

Parameters

state_dict (dict) – optimizer state. Should be an object returnedfrom a call tostate_dict().

Warning

Make sure this method is called after initializingtorch.optim.lr_scheduler.LRScheduler,as calling it beforehand will overwrite the loaded learning rates.

Note

The names of the parameters (if they exist under the “param_names” key of each param groupinstate_dict()) will not affect the loading process.To use the parameters’ names for custom cases (such as when the parameters in the loaded state dictdiffer from those initialized in the optimizer),a customregister_load_state_dict_pre_hook should be implemented to adapt the loaded dictaccordingly.Ifparam_names exist in loaded state dictparam_groups they will be saved and overridethe current names, if present, in the optimizer state. If they do not exist in loaded state dict,the optimizerparam_names will remain unchanged.

Example

>>>model=torch.nn.Linear(10,10)>>>optim=torch.optim.SGD(model.parameters(),lr=3e-4)>>>scheduler1=torch.optim.lr_scheduler.LinearLR(...optim,...start_factor=0.1,...end_factor=1,...total_iters=20,...)>>>scheduler2=torch.optim.lr_scheduler.CosineAnnealingLR(...optim,...T_max=80,...eta_min=3e-5,...)>>>lr=torch.optim.lr_scheduler.SequentialLR(...optim,...schedulers=[scheduler1,scheduler2],...milestones=[20],...)>>>lr.load_state_dict(torch.load("./save_seq.pt"))>>># now load the optimizer checkpoint after loading the LRScheduler>>>optim.load_state_dict(torch.load("./save_optim.pt"))