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Saving and loading weights

Lightning automates saving and loading checkpoints. Checkpoints capture the exact value of all parameters used by a model.

Checkpointing your training allows you to resume a training process in case it was interrupted, fine-tune a model or use a pre-trained model for inference without having to retrain the model.

Checkpoint saving

A Lightning checkpoint has everything needed to restore a training session including:

  • 16-bit scaling factor (apex)

  • Current epoch

  • Global step

  • Model state_dict

  • State of all optimizers

  • State of all learningRate schedulers

  • State of all callbacks

  • The hyperparameters used for that model if passed in as hparams (Argparse.Namespace)

Automatic saving

Lightning automatically saves a checkpoint for you in your current working directory, with the state of your last training epoch. This makes sure you can resume training in case it was interrupted.

To change the checkpoint path pass in:

# saves checkpoints to '/your/path/to/save/checkpoints' at every epoch end
trainer = Trainer(default_root_dir='/your/path/to/save/checkpoints')

You can customize the checkpointing behavior to monitor any quantity of your training or validation steps. For example, if you want to update your checkpoints based on your validation loss:

  1. Calculate any metric or other quantity you wish to monitor, such as validation loss.

  2. Log the quantity using log() method, with a key such as val_loss.

  3. Initializing the ModelCheckpoint callback, and set monitor to be the key of your quantity.

  4. Pass the callback to checkpoint_callback Trainer flag.

from pytorch_lightning.callbacks import ModelCheckpoint

class LitAutoEncoder(pl.LightningModule):
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.backbone(x)

        # 1. calculate loss
        loss = F.cross_entropy(y_hat, y)

        # 2. log `val_loss`
        self.log('val_loss', loss)

# 3. Init ModelCheckpoint callback, monitoring 'val_loss'
checkpoint_callback = ModelCheckpoint(monitor='val_loss')

# 4. Pass your callback to checkpoint_callback trainer flag
trainer = Trainer(checkpoint_callback=checkpoint_callback)

You can also control more advanced options, like save_top_k, to save the best k models and the mode of the monitored quantity (min/max/auto, where the mode is automatically inferred from the name of the monitored quantity), save_weights_only or period to set the interval of epochs between checkpoints, to avoid slowdowns.

from pytorch_lightning.callbacks import ModelCheckpoint

class LitAutoEncoder(pl.LightningModule):
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.backbone(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('val_loss', loss)

# saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
checkpoint_callback = ModelCheckpoint(
    monitor='val_loss',
    filepath='my/path/sample-mnist-{epoch:02d}-{val_loss:.2f}' ,
    save_top_k=3,
    mode='min')

trainer = Trainer(checkpoint_callback=checkpoint_callback)

You can retrieve the checkpoint after training by calling

checkpoint_callback = ModelCheckpoint(filepath='my/path/')
trainer = Trainer(checkpoint_callback=checkpoint_callback)
trainer.fit(model)
checkpoint_callback.best_model_path

Disabling checkpoints

You can disable checkpointing by passing

trainer = Trainer(checkpoint_callback=False)

The Lightning checkpoint also saves the arguments passed into the LightningModule init under the module_arguments key in the checkpoint.

class MyLightningModule(LightningModule):

   def __init__(self, learning_rate, *args, **kwargs):
        super().__init__()

# all init args were saved to the checkpoint
checkpoint = torch.load(CKPT_PATH)
print(checkpoint['module_arguments'])
# {'learning_rate': the_value}

Manual saving

You can manually save checkpoints and restore your model from the checkpointed state.

model = MyLightningModule(hparams)
trainer.fit(model)
trainer.save_checkpoint("example.ckpt")
new_model = MyModel.load_from_checkpoint(checkpoint_path="example.ckpt")

Checkpoint loading

To load a model along with its weights, biases and module_arguments use the following method:

model = MyLightingModule.load_from_checkpoint(PATH)

print(model.learning_rate)
# prints the learning_rate you used in this checkpoint

model.eval()
y_hat = model(x)

But if you don’t want to use the values saved in the checkpoint, pass in your own here

class LitModel(LightningModule):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.save_hyperparameters()
        self.l1 = nn.Linear(self.hparams.in_dim, self.hparams.out_dim)

you can restore the model like this

# if you train and save the model like this it will use these values when loading
# the weights. But you can overwrite this
LitModel(in_dim=32, out_dim=10)

# uses in_dim=32, out_dim=10
model = LitModel.load_from_checkpoint(PATH)

# uses in_dim=128, out_dim=10
model = LitModel.load_from_checkpoint(PATH, in_dim=128, out_dim=10)
classmethod LightningModule.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 module_arguments

Any arguments specified through *args and **kwargs will override args stored in hparams.

Parameters
  • checkpoint_path (str) – Path to checkpoint. This can also be a URL.

  • 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 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. Default: True.

  • hparam_overrides – A dictionary with keys to override in the hparams

  • kwargs – Any keyword args needed to init the model.

Returns

LightningModule with loaded weights and hyperparameters (if available).

Example

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

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

# override some of the params with new values
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)

Restoring Training State

If you don’t just want to load weights, but instead restore the full training, do the following:

model = LitModel()
trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')

# automatically restores model, epoch, step, LR schedulers, apex, etc...
trainer.fit(model)
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