ModelIO

class pytorch_lightning.core.saving.ModelIO

Bases: object

classmethod load_from_checkpoint(checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs)

Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to __init__ in the checkpoint under "hyper_parameters".

Any arguments specified through **kwargs will override args stored in "hyper_parameters".

Parameters:

• checkpoint_path (Union[str, IO]) – Path to checkpoint. This can also be a URL, or file-like object

• map_location (Union[Dict[str, str], str, device, int, Callable, None]) – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().

• hparams_file (Optional[str]) –

  Optional path to a .yaml or .csv file with hierarchical structure as in this example:

  drop_prob: 0.2
  dataloader:
      batch_size: 32
  

  You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningModule for use.

  If your model’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your model to treat hparams as dict.

• strict (bool) – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict.

• **kwargs – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Returns:

LightningModule instance with loaded weights and hyperparameters (if available).

Note

load_from_checkpoint is a class method. You should use your LightningModule class to call it instead of the LightningModule instance.

Example:

# load weights without mapping ...
model = MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')

# or load weights mapping all weights from GPU 1 to GPU 0 ...
map_location = {'cuda:1':'cuda:0'}
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    map_location=map_location
)

# or load weights and hyperparameters from separate files.
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    hparams_file='/path/to/hparams_file.yaml'
)

# override some of the params with new values
model = MyLightningModule.load_from_checkpoint(
    PATH,
    num_layers=128,
    pretrained_ckpt_path=NEW_PATH,
)

# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)

on_hpc_load(checkpoint)

Hook to do whatever you need right before Slurm manager loads the model.

Parameters:

checkpoint (Dict[str, Any]) – A dictionary with variables from the checkpoint.

Deprecated since version v1.6: This method is deprecated in v1.6 and will be removed in v1.8. Please use LightningModule.on_load_checkpoint instead.

Return type:

None

on_hpc_save(checkpoint)

Hook to do whatever you need right before Slurm manager saves the model.

Parameters:

checkpoint (Dict[str, Any]) – A dictionary in which you can save variables to save in a checkpoint. Contents need to be pickleable.

Deprecated since version v1.6: This method is deprecated in v1.6 and will be removed in v1.8. Please use LightningModule.on_save_checkpoint instead.

Return type:

None