torch.lightning#
Module: torch.lightning#
Inheritance diagram for ISLP.torch.lightning:
Classes#
ErrorTracker#
- class ISLP.torch.lightning.ErrorTracker#
Bases:
Callback- Attributes:
state_keyIdentifier for the state of the callback.
Methods
load_state_dict(state_dict)Called when loading a checkpoint, implement to reload callback state given callback's
state_dict.on_after_backward(trainer, pl_module)Called after
loss.backward()and before optimizers are stepped.on_before_backward(trainer, pl_module, loss)Called before
loss.backward().on_before_optimizer_step(trainer, pl_module, ...)Called before
optimizer.step().on_before_zero_grad(trainer, pl_module, ...)Called before
optimizer.zero_grad().on_exception(trainer, pl_module, exception)Called when any trainer execution is interrupted by an exception.
on_fit_end(trainer, pl_module)Called when fit ends.
on_fit_start(trainer, pl_module)Called when fit begins.
on_load_checkpoint(trainer, pl_module, ...)Called when loading a model checkpoint, use to reload state.
on_predict_batch_end(trainer, pl_module, ...)Called when the predict batch ends.
on_predict_batch_start(trainer, pl_module, ...)Called when the predict batch begins.
on_predict_end(trainer, pl_module)Called when predict ends.
on_predict_epoch_end(trainer, pl_module)Called when the predict epoch ends.
on_predict_epoch_start(trainer, pl_module)Called when the predict epoch begins.
on_predict_start(trainer, pl_module)Called when the predict begins.
on_sanity_check_end(trainer, pl_module)Called when the validation sanity check ends.
on_sanity_check_start(trainer, pl_module)Called when the validation sanity check starts.
on_save_checkpoint(trainer, pl_module, ...)Called when saving a checkpoint to give you a chance to store anything else you might want to save.
on_test_batch_end(trainer, pl_module, ...[, ...])Called when the test batch ends.
on_test_batch_start(trainer, pl_module, ...)Called when the test batch begins.
on_test_end(trainer, pl_module)Called when the test ends.
on_test_epoch_end(trainer, pl_module)Called when the test epoch ends.
on_test_epoch_start(trainer, pl_module)Called when the test epoch begins.
on_test_start(trainer, pl_module)Called when the test begins.
on_train_batch_end(trainer, pl_module, ...)Called when the train batch ends.
on_train_batch_start(trainer, pl_module, ...)Called when the train batch begins.
on_train_end(trainer, pl_module)Called when the train ends.
on_train_epoch_end(trainer, pl_module)Called when the train epoch ends.
on_train_epoch_start(trainer, pl_module)Called when the train epoch begins.
on_train_start(trainer, pl_module)Called when the train begins.
on_validation_batch_end(trainer, pl_module, ...)Called when the validation batch ends.
on_validation_batch_start(trainer, ...[, ...])Called when the validation batch begins.
on_validation_end(trainer, pl_module)Called when the validation loop ends.
on_validation_epoch_end(trainer, pl_module)Called when the val epoch ends.
on_validation_epoch_start(trainer, pl_module)Called when the val epoch begins.
on_validation_start(trainer, pl_module)Called when the validation loop begins.
setup(trainer, pl_module, stage)Called when fit, validate, test, predict, or tune begins.
Called when saving a checkpoint, implement to generate callback's
state_dict.teardown(trainer, pl_module, stage)Called when fit, validate, test, predict, or tune ends.
- __init__(*args, **kwargs)#
- load_state_dict(state_dict: Dict[str, Any]) None#
Called when loading a checkpoint, implement to reload callback state given callback’s
state_dict.- Args:
state_dict: the callback state returned by
state_dict.
- on_after_backward(trainer: Trainer, pl_module: LightningModule) None#
Called after
loss.backward()and before optimizers are stepped.
- on_before_backward(trainer: Trainer, pl_module: LightningModule, loss: Tensor) None#
Called before
loss.backward().
- on_before_optimizer_step(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) None#
Called before
optimizer.step().
- on_before_zero_grad(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) None#
Called before
optimizer.zero_grad().
- on_exception(trainer: Trainer, pl_module: LightningModule, exception: BaseException) None#
Called when any trainer execution is interrupted by an exception.
- on_load_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]) None#
Called when loading a model checkpoint, use to reload state.
- Args:
trainer: the current
Trainerinstance. pl_module: the currentLightningModuleinstance. checkpoint: the full checkpoint dictionary that got loaded by the Trainer.
- on_predict_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called when the predict batch ends.
- on_predict_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called when the predict batch begins.
- on_predict_epoch_end(trainer: Trainer, pl_module: LightningModule) None#
Called when the predict epoch ends.
- on_predict_epoch_start(trainer: Trainer, pl_module: LightningModule) None#
Called when the predict epoch begins.
- on_predict_start(trainer: Trainer, pl_module: LightningModule) None#
Called when the predict begins.
- on_sanity_check_end(trainer: Trainer, pl_module: LightningModule) None#
Called when the validation sanity check ends.
- on_sanity_check_start(trainer: Trainer, pl_module: LightningModule) None#
Called when the validation sanity check starts.
- on_save_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]) None#
Called when saving a checkpoint to give you a chance to store anything else you might want to save.
- Args:
trainer: the current
Trainerinstance. pl_module: the currentLightningModuleinstance. checkpoint: the checkpoint dictionary that will be saved.
- on_test_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called when the test batch ends.
- on_test_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)#
Called when the test batch begins.
- on_test_epoch_end(trainer, pl_module)#
Called when the test epoch ends.
- on_test_epoch_start(trainer, pl_module)#
Called when the test epoch begins.
- on_train_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int) None#
Called when the train batch ends.
- Note:
The value
outputs["loss"]here will be the normalized value w.r.taccumulate_grad_batchesof the loss returned fromtraining_step.
- on_train_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int) None#
Called when the train batch begins.
- on_train_epoch_end(trainer: Trainer, pl_module: LightningModule) None#
Called when the train epoch ends.
To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the
pytorch_lightning.core.LightningModuleand access them in this hook:class MyLightningModule(L.LightningModule): def __init__(self): super().__init__() self.training_step_outputs = [] def training_step(self): loss = ... self.training_step_outputs.append(loss) return loss class MyCallback(L.Callback): def on_train_epoch_end(self, trainer, pl_module): # do something with all training_step outputs, for example: epoch_mean = torch.stack(pl_module.training_step_outputs).mean() pl_module.log("training_epoch_mean", epoch_mean) # free up the memory pl_module.training_step_outputs.clear()
- on_train_epoch_start(trainer: Trainer, pl_module: LightningModule) None#
Called when the train epoch begins.
- on_validation_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called when the validation batch ends.
- on_validation_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)#
Called when the validation batch begins.
- on_validation_end(trainer: Trainer, pl_module: LightningModule) None#
Called when the validation loop ends.
- on_validation_epoch_end(trainer, pl_module)#
Called when the val epoch ends.
- on_validation_epoch_start(trainer, pl_module)#
Called when the val epoch begins.
- on_validation_start(trainer: Trainer, pl_module: LightningModule) None#
Called when the validation loop begins.
- setup(trainer: Trainer, pl_module: LightningModule, stage: str) None#
Called when fit, validate, test, predict, or tune begins.
- state_dict() Dict[str, Any]#
Called when saving a checkpoint, implement to generate callback’s
state_dict.- Returns:
A dictionary containing callback state.
- property state_key: str#
Identifier for the state of the callback.
Used to store and retrieve a callback’s state from the checkpoint dictionary by
checkpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.
SimpleDataModule#
- class ISLP.torch.lightning.SimpleDataModule(train_dataset, test_dataset, batch_size=32, num_workers=0, persistent_workers=True, validation=None, seed=0)#
Bases:
LightningDataModule- Attributes:
hparamsThe collection of hyperparameters saved with
save_hyperparameters().hparams_initialThe collection of hyperparameters saved with
save_hyperparameters().- name
Methods
from_datasets([train_dataset, val_dataset, ...])Create an instance from torch.utils.data.Dataset.
load_from_checkpoint(checkpoint_path[, ...])Primary way of loading a datamodule from a checkpoint.
load_state_dict(state_dict)Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.
on_after_batch_transfer(batch, dataloader_idx)Override to alter or apply batch augmentations to your batch after it is transferred to the device.
on_before_batch_transfer(batch, dataloader_idx)Override to alter or apply batch augmentations to your batch before it is transferred to the device.
An iterable or collection of iterables specifying prediction samples.
Use this to download and prepare data.
save_hyperparameters(*args[, ignore, frame, ...])Save arguments to
hparamsattribute.setup(stage)Called at the beginning of fit (train + validate), validate, test, or predict.
Called when saving a checkpoint, implement to generate and save datamodule state.
teardown(stage)Called at the end of fit (train + validate), validate, test, or predict.
An iterable or collection of iterables specifying test samples.
An iterable or collection of iterables specifying training samples.
transfer_batch_to_device(batch, device, ...)Override this hook if your
DataLoaderreturns tensors wrapped in a custom data structure.An iterable or collection of iterables specifying validation samples.
fromarrays
- __init__(train_dataset, test_dataset, batch_size=32, num_workers=0, persistent_workers=True, validation=None, seed=0)#
- Attributes:
- prepare_data_per_node:
If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.
- allow_zero_length_dataloader_with_multiple_devices:
If True, dataloader with zero length within local rank is allowed. Default value is False.
- CHECKPOINT_HYPER_PARAMS_KEY = 'datamodule_hyper_parameters'#
- CHECKPOINT_HYPER_PARAMS_NAME = 'datamodule_hparams_name'#
- CHECKPOINT_HYPER_PARAMS_TYPE = 'datamodule_hparams_type'#
- classmethod from_datasets(train_dataset: Dataset | Iterable[Dataset] | None = None, val_dataset: Dataset | Iterable[Dataset] | None = None, test_dataset: Dataset | Iterable[Dataset] | None = None, predict_dataset: Dataset | Iterable[Dataset] | None = None, batch_size: int = 1, num_workers: int = 0, **datamodule_kwargs: Any) LightningDataModule#
Create an instance from torch.utils.data.Dataset.
- Args:
train_dataset: Optional dataset or iterable of datasets to be used for train_dataloader() val_dataset: Optional dataset or iterable of datasets to be used for val_dataloader() test_dataset: Optional dataset or iterable of datasets to be used for test_dataloader() predict_dataset: Optional dataset or iterable of datasets to be used for predict_dataloader() batch_size: Batch size to use for each dataloader. Default is 1. This parameter gets forwarded to the
__init__if the datamodule has such a name defined in its signature.- num_workers: Number of subprocesses to use for data loading. 0 means that the
data will be loaded in the main process. Number of CPUs available. This parameter gets forwarded to the
__init__if the datamodule has such a name defined in its signature.
**datamodule_kwargs: Additional parameters that get passed down to the datamodule’s
__init__.
- static fromarrays(*arrays, test=0, validation=0, batch_size=32, num_workers=0, persistent_workers=True, test_as_validation=False, seed=0)#
- property hparams: AttributeDict | MutableMapping#
The collection of hyperparameters saved with
save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, usehparams_initial.- Returns:
Mutable hyperparameters dictionary
- property hparams_initial: AttributeDict#
The collection of hyperparameters saved with
save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed throughhparams.- Returns:
AttributeDict: immutable initial hyperparameters
- load_from_checkpoint(checkpoint_path: str | Path | IO, map_location: device | str | int | Callable[[UntypedStorage, str], UntypedStorage | None] | Dict[device | str | int, device | str | int] | None = None, hparams_file: str | Path | None = None, **kwargs: Any) Self#
Primary way of loading a datamodule from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to
__init__in the checkpoint under"datamodule_hyper_parameters".Any arguments specified through **kwargs will override args stored in
"datamodule_hyper_parameters".- Args:
checkpoint_path: Path to checkpoint. This can also be a URL, or file-like object map_location:
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 path to a
.yamlor.csvfile with hierarchical structure as in this example:
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
.yamlfile with the hparams you’d like to use. These will be converted into adictand passed into yourLightningDataModulefor use.If your datamodule’s
hparamsargument isNamespaceand.yamlfile has hierarchical structure, you need to refactor your datamodule to treathparamsasdict.- **kwargs: Any extra keyword args needed to init the datamodule. Can also be used to override saved
hyperparameter values.
- hparams_file: Optional path to a
- Return:
LightningDataModuleinstance with loaded weights and hyperparameters (if available).- Note:
load_from_checkpointis a class method. You must use yourLightningDataModuleclass to call it instead of theLightningDataModuleinstance, or aTypeErrorwill be raised.
Example:
# load weights without mapping ... datamodule = MyLightningDataModule.load_from_checkpoint('path/to/checkpoint.ckpt') # or load weights and hyperparameters from separate files. datamodule = MyLightningDataModule.load_from_checkpoint( 'path/to/checkpoint.ckpt', hparams_file='/path/to/hparams_file.yaml' ) # override some of the params with new values datamodule = MyLightningDataModule.load_from_checkpoint( PATH, batch_size=32, num_workers=10, )
- load_state_dict(state_dict: Dict[str, Any]) None#
Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.
- Args:
state_dict: the datamodule state returned by
state_dict.
- on_after_batch_transfer(batch: Any, dataloader_idx: int) Any#
Override to alter or apply batch augmentations to your batch after it is transferred to the device.
- Note:
To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be altered or augmented. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A batch of data
Example:
def on_after_batch_transfer(self, batch, dataloader_idx): batch['x'] = gpu_transforms(batch['x']) return batch
- Raises:
- MisconfigurationException:
If using IPUs,
Trainer(accelerator='ipu').
- See Also:
- on_before_batch_transfer(batch: Any, dataloader_idx: int) Any#
Override to alter or apply batch augmentations to your batch before it is transferred to the device.
- Note:
To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be altered or augmented. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A batch of data
Example:
def on_before_batch_transfer(self, batch, dataloader_idx): batch['x'] = transforms(batch['x']) return batch
- predict_dataloader()#
An iterable or collection of iterables specifying prediction samples.
For more information about multiple dataloaders, see this section.
It’s recommended that all data downloads and preparation happen in
prepare_data().predict()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Return:
A
torch.utils.data.DataLoaderor a sequence of them specifying prediction samples.
- prepare_data() None#
Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within.
Warning
DO NOT set state to the model (use
setupinstead) since this is NOT called on every deviceExample:
def prepare_data(self): # good download_data() tokenize() etc() # bad self.split = data_split self.some_state = some_other_state()
In a distributed environment,
prepare_datacan be called in two ways (using prepare_data_per_node)Once per node. This is the default and is only called on LOCAL_RANK=0.
Once in total. Only called on GLOBAL_RANK=0.
Example:
# DEFAULT # called once per node on LOCAL_RANK=0 of that node class LitDataModule(LightningDataModule): def __init__(self): super().__init__() self.prepare_data_per_node = True # call on GLOBAL_RANK=0 (great for shared file systems) class LitDataModule(LightningDataModule): def __init__(self): super().__init__() self.prepare_data_per_node = False
This is called before requesting the dataloaders:
model.prepare_data() initialize_distributed() model.setup(stage) model.train_dataloader() model.val_dataloader() model.test_dataloader() model.predict_dataloader()
- save_hyperparameters(*args: Any, ignore: Sequence[str] | str | None = None, frame: FrameType | None = None, logger: bool = True) None#
Save arguments to
hparamsattribute.- Args:
- args: single object of dict, NameSpace or OmegaConf
or string names or arguments from class
__init__- ignore: an argument name or a list of argument names from
class
__init__to be ignored
frame: a frame object. Default is None logger: Whether to send the hyperparameters to the logger. Default: True
- Example::
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class ManuallyArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # manually assign arguments ... self.save_hyperparameters('arg1', 'arg3') ... def forward(self, *args, **kwargs): ... ... >>> model = ManuallyArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class AutomaticArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # equivalent automatic ... self.save_hyperparameters() ... def forward(self, *args, **kwargs): ... ... >>> model = AutomaticArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg2": abc "arg3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class SingleArgModel(HyperparametersMixin): ... def __init__(self, params): ... super().__init__() ... # manually assign single argument ... self.save_hyperparameters(params) ... def forward(self, *args, **kwargs): ... ... >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14)) >>> model.hparams "p1": 1 "p2": abc "p3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class ManuallyArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # pass argument(s) to ignore as a string or in a list ... self.save_hyperparameters(ignore='arg2') ... def forward(self, *args, **kwargs): ... ... >>> model = ManuallyArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg3": 3.14
- setup(stage: str) None#
Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.
- Args:
stage: either
'fit','validate','test', or'predict'
Example:
class LitModel(...): def __init__(self): self.l1 = None def prepare_data(self): download_data() tokenize() # don't do this self.something = else def setup(self, stage): data = load_data(...) self.l1 = nn.Linear(28, data.num_classes)
- state_dict() Dict[str, Any]#
Called when saving a checkpoint, implement to generate and save datamodule state.
- Returns:
A dictionary containing datamodule state.
- teardown(stage: str) None#
Called at the end of fit (train + validate), validate, test, or predict.
- Args:
stage: either
'fit','validate','test', or'predict'
- test_dataloader()#
An iterable or collection of iterables specifying test samples.
For more information about multiple dataloaders, see this section.
For data processing use the following pattern:
download in
prepare_data()process and split in
setup()
However, the above are only necessary for distributed processing.
Warning
do not assign state in prepare_data
test()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
- Note:
If you don’t need a test dataset and a
test_step(), you don’t need to implement this method.
- train_dataloader()#
An iterable or collection of iterables specifying training samples.
For more information about multiple dataloaders, see this section.
The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.
For data processing use the following pattern:
download in
prepare_data()process and split in
setup()
However, the above are only necessary for distributed processing.
Warning
do not assign state in prepare_data
fit()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
- transfer_batch_to_device(batch: Any, device: device, dataloader_idx: int) Any#
Override this hook if your
DataLoaderreturns tensors wrapped in a custom data structure.The data types listed below (and any arbitrary nesting of them) are supported out of the box:
torch.Tensoror anything that implements .to(…)
For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).
- Note:
This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be transferred to a new device. device: The target device as defined in PyTorch. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A reference to the data on the new device.
Example:
def transfer_batch_to_device(self, batch, device, dataloader_idx): if isinstance(batch, CustomBatch): # move all tensors in your custom data structure to the device batch.samples = batch.samples.to(device) batch.targets = batch.targets.to(device) elif dataloader_idx == 0: # skip device transfer for the first dataloader or anything you wish pass else: batch = super().transfer_batch_to_device(batch, device, dataloader_idx) return batch
- Raises:
- MisconfigurationException:
If using IPUs,
Trainer(accelerator='ipu').
- See Also:
move_data_to_device()apply_to_collection()
- val_dataloader()#
An iterable or collection of iterables specifying validation samples.
For more information about multiple dataloaders, see this section.
The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.
It’s recommended that all data downloads and preparation happen in
prepare_data().fit()validate()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Note:
If you don’t need a validation dataset and a
validation_step(), you don’t need to implement this method.
SimpleModule#
- class ISLP.torch.lightning.SimpleModule(model, loss, optimizer=None, metrics=None, on_epoch=True, pre_process_y_for_metrics=<function SimpleModule.<lambda>>)#
Bases:
LightningModuleA simple pytorch_lightning module for regression problems.
- Attributes:
automatic_optimizationIf set to
Falseyou are responsible for calling.backward(),.step(),.zero_grad().current_epochThe current epoch in the
Trainer, or 0 if not attached.- device
- dtype
example_input_arrayThe example input array is a specification of what the module can consume in the
forward()method.- fabric
global_rankThe index of the current process across all nodes and devices.
global_stepTotal training batches seen across all epochs.
hparamsThe collection of hyperparameters saved with
save_hyperparameters().hparams_initialThe collection of hyperparameters saved with
save_hyperparameters().local_rankThe index of the current process within a single node.
loggerReference to the logger object in the Trainer.
loggersReference to the list of loggers in the Trainer.
on_gpuReturns
Trueif this model is currently located on a GPU.strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
- trainer
Methods
add_module(name, module)Add a child module to the current module.
all_gather(data[, group, sync_grads])Gather tensors or collections of tensors from multiple processes.
apply(fn)Apply
fnrecursively to every submodule (as returned by.children()) as well as self.backward(loss, *args, **kwargs)Called to perform backward on the loss returned in
training_step().bfloat16()Casts all floating point parameters and buffers to
bfloat16datatype.buffers([recurse])Return an iterator over module buffers.
children()Return an iterator over immediate children modules.
clip_gradients(optimizer[, ...])Handles gradient clipping internally.
compile(*args, **kwargs)Compile this Module's forward using
torch.compile().Configure model-specific callbacks.
configure_gradient_clipping(optimizer[, ...])Perform gradient clipping for the optimizer parameters.
Hook to create modules in a strategy and precision aware context.
Choose what optimizers and learning-rate schedulers to use in your optimization.
Deprecated.
cpu()cuda([device])Moves all model parameters and buffers to the GPU.
double()eval()Set the module in evaluation mode.
Set the extra representation of the module.
float()forward(x)Same as
torch.nn.Module.forward().freeze()Freeze all params for inference.
get_buffer(target)Return the buffer given by
targetif it exists, otherwise throw an error.Return any extra state to include in the module's state_dict.
get_parameter(target)Return the parameter given by
targetif it exists, otherwise throw an error.get_submodule(target)Return the submodule given by
targetif it exists, otherwise throw an error.half()ipu([device])Move all model parameters and buffers to the IPU.
load_from_checkpoint(checkpoint_path[, ...])Primary way of loading a model from a checkpoint.
load_state_dict(state_dict[, strict, assign])Copy parameters and buffers from
state_dictinto this module and its descendants.log(name, value[, prog_bar, logger, ...])Log a key, value pair.
log_dict(dictionary[, prog_bar, logger, ...])Log a dictionary of values at once.
lr_scheduler_step(scheduler, metric)Override this method to adjust the default way the
Trainercalls each scheduler.Returns the learning rate scheduler(s) that are being used during training.
manual_backward(loss, *args, **kwargs)Call this directly from your
training_step()when doing optimizations manually.modules()Return an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Called after
loss.backward()and before optimizers are stepped.on_after_batch_transfer(batch, dataloader_idx)Override to alter or apply batch augmentations to your batch after it is transferred to the device.
on_before_backward(loss)Called before
loss.backward().on_before_batch_transfer(batch, dataloader_idx)Override to alter or apply batch augmentations to your batch before it is transferred to the device.
on_before_optimizer_step(optimizer)Called before
optimizer.step().on_before_zero_grad(optimizer)Called after
training_step()and beforeoptimizer.zero_grad().Called at the very end of fit.
Called at the very beginning of fit.
on_load_checkpoint(checkpoint)Called by Lightning to restore your model.
on_predict_batch_end(outputs, batch, batch_idx)Called in the predict loop after the batch.
on_predict_batch_start(batch, batch_idx[, ...])Called in the predict loop before anything happens for that batch.
Called at the end of predicting.
Called at the end of predicting.
Called at the beginning of predicting.
Called when the predict loop starts.
Called at the beginning of predicting.
on_save_checkpoint(checkpoint)Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
on_test_batch_end(outputs, batch, batch_idx)Called in the test loop after the batch.
on_test_batch_start(batch, batch_idx[, ...])Called in the test loop before anything happens for that batch.
Called at the end of testing.
Called in the test loop at the very end of the epoch.
Called in the test loop at the very beginning of the epoch.
Called when the test loop starts.
Called when the test loop ends.
Called at the beginning of testing.
on_train_batch_end(outputs, batch, batch_idx)Called in the training loop after the batch.
on_train_batch_start(batch, batch_idx)Called in the training loop before anything happens for that batch.
Called at the end of training before logger experiment is closed.
Called in the training loop at the very end of the epoch.
Called in the training loop at the very beginning of the epoch.
Called at the beginning of training after sanity check.
on_validation_batch_end(outputs, batch, ...)Called in the validation loop after the batch.
on_validation_batch_start(batch, batch_idx)Called in the validation loop before anything happens for that batch.
Called at the end of validation.
Called in the validation loop at the very end of the epoch.
Called in the validation loop at the very beginning of the epoch.
Called when the validation loop starts.
Called when the validation loop ends.
Called by the training loop to release gradients before entering the validation loop.
Called at the beginning of validation.
optimizer_step(epoch, batch_idx, optimizer)Override this method to adjust the default way the
Trainercalls the optimizer.optimizer_zero_grad(epoch, batch_idx, optimizer)Override this method to change the default behaviour of
optimizer.zero_grad().optimizers([use_pl_optimizer])Returns the optimizer(s) that are being used during training.
parameters([recurse])Return an iterator over module parameters.
An iterable or collection of iterables specifying prediction samples.
predict_step(batch, batch_idx)Step function called during
predict().Use this to download and prepare data.
print(*args, **kwargs)Prints only from process 0.
register_backward_hook(hook)Register a backward hook on the module.
register_buffer(name, tensor[, persistent])Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])Register a backward pre-hook on the module.
Register a post hook to be run after module's
load_state_dictis called.register_module(name, module)Alias for
add_module().register_parameter(name, param)Add a parameter to the module.
Register a pre-hook for the
state_dict()method.requires_grad_([requires_grad])Change if autograd should record operations on parameters in this module.
save_hyperparameters(*args[, ignore, frame, ...])Save arguments to
hparamsattribute.set_extra_state(state)Set extra state contained in the loaded state_dict.
setup(stage)Called at the beginning of fit (train + validate), validate, test, or predict.
state_dict(*args[, destination, prefix, ...])Return a dictionary containing references to the whole state of the module.
teardown(stage)Called at the end of fit (train + validate), validate, test, or predict.
An iterable or collection of iterables specifying test samples.
test_step(batch, batch_idx)Operates on a single batch of data from the test set.
to(*args, **kwargs)See
torch.nn.Module.to().to_empty(*, device[, recurse])Move the parameters and buffers to the specified device without copying storage.
to_onnx(file_path[, input_sample])Saves the model in ONNX format.
to_torchscript([file_path, method, ...])By default compiles the whole model to a
ScriptModule.toggle_optimizer(optimizer)Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
train([mode])Set the module in training mode.
An iterable or collection of iterables specifying training samples.
training_step(batch, batch_idx)Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
transfer_batch_to_device(batch, device, ...)Override this hook if your
DataLoaderreturns tensors wrapped in a custom data structure.type(dst_type)unfreeze()Unfreeze all parameters for training.
untoggle_optimizer(optimizer)Resets the state of required gradients that were toggled with
toggle_optimizer().An iterable or collection of iterables specifying validation samples.
validation_step(batch, batch_idx)Operates on a single batch of data from the validation set.
xpu([device])Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])Reset gradients of all model parameters.
__call__
binary_classification
classification
regression
- __init__(model, loss, optimizer=None, metrics=None, on_epoch=True, pre_process_y_for_metrics=<function SimpleModule.<lambda>>)#
- CHECKPOINT_HYPER_PARAMS_KEY = 'hyper_parameters'#
- CHECKPOINT_HYPER_PARAMS_NAME = 'hparams_name'#
- CHECKPOINT_HYPER_PARAMS_TYPE = 'hparams_type'#
- T_destination = ~T_destination#
- add_module(name: str, module: Module | None) None#
Add a child module to the current module.
The module can be accessed as an attribute using the given name.
- Args:
- name (str): name of the child module. The child module can be
accessed from this module using the given name
module (Module): child module to be added to the module.
- all_gather(data: Tensor | Dict | List | Tuple, group: Any | None = None, sync_grads: bool = False) Tensor | Dict | List | Tuple#
Gather tensors or collections of tensors from multiple processes.
This method needs to be called on all processes and the tensors need to have the same shape across all processes, otherwise your program will stall forever.
- Args:
data: int, float, tensor of shape (batch, …), or a (possibly nested) collection thereof. group: the process group to gather results from. Defaults to all processes (world) sync_grads: flag that allows users to synchronize gradients for the all_gather operation
- Return:
A tensor of shape (world_size, batch, …), or if the input was a collection the output will also be a collection with tensors of this shape.
- apply(fn: Callable[[Module], None]) T#
Apply
fnrecursively to every submodule (as returned by.children()) as well as self.Typical use includes initializing the parameters of a model (see also torch.nn.init).
- Args:
fn (
Module-> None): function to be applied to each submodule- Returns:
Module: self
Example:
>>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )
- property automatic_optimization: bool#
If set to
Falseyou are responsible for calling.backward(),.step(),.zero_grad().
- backward(loss: Tensor, *args: Any, **kwargs: Any) None#
Called to perform backward on the loss returned in
training_step(). Override this hook with your own implementation if you need to.- Args:
- loss: The loss tensor returned by
training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).
- loss: The loss tensor returned by
Example:
def backward(self, loss): loss.backward()
- bfloat16() T#
Casts all floating point parameters and buffers to
bfloat16datatype.Note
This method modifies the module in-place.
- Returns:
Module: self
- static binary_classification(model, metrics=None, device='cpu', **kwargs)#
- buffers(recurse: bool = True) Iterator[Tensor]#
Return an iterator over module buffers.
- Args:
- recurse (bool): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that are direct members of this module.
- Yields:
torch.Tensor: module buffer
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- children() Iterator[Module]#
Return an iterator over immediate children modules.
- Yields:
Module: a child module
- static classification(model, num_classes, metrics=None, device='cpu', **kwargs)#
- clip_gradients(optimizer: Optimizer, gradient_clip_val: float | int | None = None, gradient_clip_algorithm: str | None = None) None#
Handles gradient clipping internally.
- Note:
Do not override this method. If you want to customize gradient clipping, consider using
configure_gradient_clipping()method.For manual optimization (
self.automatic_optimization = False), if you want to use gradient clipping, consider callingself.clip_gradients(opt, gradient_clip_val=0.5, gradient_clip_algorithm="norm")manually in the training step.
- Args:
optimizer: Current optimizer being used. gradient_clip_val: The value at which to clip gradients. gradient_clip_algorithm: The gradient clipping algorithm to use. Pass
gradient_clip_algorithm="value"to clip by value, and
gradient_clip_algorithm="norm"to clip by norm.
- compile(*args, **kwargs)#
Compile this Module’s forward using
torch.compile().This Module’s __call__ method is compiled and all arguments are passed as-is to
torch.compile().See
torch.compile()for details on the arguments for this function.
- configure_callbacks() Sequence[Callback] | Callback#
Configure model-specific callbacks. When the model gets attached, e.g., when
.fit()or.test()gets called, the list or a callback returned here will be merged with the list of callbacks passed to the Trainer’scallbacksargument. If a callback returned here has the same type as one or several callbacks already present in the Trainer’s callbacks list, it will take priority and replace them. In addition, Lightning will make sureModelCheckpointcallbacks run last.- Return:
A callback or a list of callbacks which will extend the list of callbacks in the Trainer.
Example:
def configure_callbacks(self): early_stop = EarlyStopping(monitor="val_acc", mode="max") checkpoint = ModelCheckpoint(monitor="val_loss") return [early_stop, checkpoint]
- configure_gradient_clipping(optimizer: Optimizer, gradient_clip_val: float | int | None = None, gradient_clip_algorithm: str | None = None) None#
Perform gradient clipping for the optimizer parameters. Called before
optimizer_step().- Args:
optimizer: Current optimizer being used. gradient_clip_val: The value at which to clip gradients. By default, value passed in Trainer
will be available here.
- gradient_clip_algorithm: The gradient clipping algorithm to use. By default, value
passed in Trainer will be available here.
Example:
def configure_gradient_clipping(self, optimizer, gradient_clip_val, gradient_clip_algorithm): # Implement your own custom logic to clip gradients # You can call `self.clip_gradients` with your settings: self.clip_gradients( optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm )
- configure_model() None#
Hook to create modules in a strategy and precision aware context.
This is particularly useful for when using sharded strategies (FSDP and DeepSpeed), where we’d like to shard the model instantly to save memory and initialization time. For non-sharded strategies, you can choose to override this hook or to initialize your model under the
init_module()context manager.This hook is called during each of fit/val/test/predict stages in the same process, so ensure that implementation of this hook is idempotent, i.e., after the first time the hook is called, subsequent calls to it should be a no-op.
- configure_optimizers()#
Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.
- Return:
Any of these 6 options.
Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple
lr_scheduler_config).Dictionary, with an
"optimizer"key, and (optionally) a"lr_scheduler"key whose value is a single LR scheduler orlr_scheduler_config.None - Fit will run without any optimizer.
The
lr_scheduler_configis a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.lr_scheduler_config = { # REQUIRED: The scheduler instance "scheduler": lr_scheduler, # The unit of the scheduler's step size, could also be 'step'. # 'epoch' updates the scheduler on epoch end whereas 'step' # updates it after a optimizer update. "interval": "epoch", # How many epochs/steps should pass between calls to # `scheduler.step()`. 1 corresponds to updating the learning # rate after every epoch/step. "frequency": 1, # Metric to to monitor for schedulers like `ReduceLROnPlateau` "monitor": "val_loss", # If set to `True`, will enforce that the value specified 'monitor' # is available when the scheduler is updated, thus stopping # training if not found. If set to `False`, it will only produce a warning "strict": True, # If using the `LearningRateMonitor` callback to monitor the # learning rate progress, this keyword can be used to specify # a custom logged name "name": None, }
When there are schedulers in which the
.step()method is conditioned on a value, such as thetorch.optim.lr_scheduler.ReduceLROnPlateauscheduler, Lightning requires that thelr_scheduler_configcontains the keyword"monitor"set to the metric name that the scheduler should be conditioned on.# The ReduceLROnPlateau scheduler requires a monitor def configure_optimizers(self): optimizer = Adam(...) return { "optimizer": optimizer, "lr_scheduler": { "scheduler": ReduceLROnPlateau(optimizer, ...), "monitor": "metric_to_track", "frequency": "indicates how often the metric is updated", # If "monitor" references validation metrics, then "frequency" should be set to a # multiple of "trainer.check_val_every_n_epoch". }, } # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler def configure_optimizers(self): optimizer1 = Adam(...) optimizer2 = SGD(...) scheduler1 = ReduceLROnPlateau(optimizer1, ...) scheduler2 = LambdaLR(optimizer2, ...) return ( { "optimizer": optimizer1, "lr_scheduler": { "scheduler": scheduler1, "monitor": "metric_to_track", }, }, {"optimizer": optimizer2, "lr_scheduler": scheduler2}, )
Metrics can be made available to monitor by simply logging it using
self.log('metric_to_track', metric_val)in yourLightningModule.- Note:
Some things to know:
Lightning calls
.backward()and.step()automatically in case of automatic optimization.If a learning rate scheduler is specified in
configure_optimizers()with key"interval"(default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s.step()method automatically in case of automatic optimization.If you use 16-bit precision (
precision=16), Lightning will automatically handle the optimizer.If you use
torch.optim.LBFGS, Lightning handles the closure function automatically for you.If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the
optimizer_step()hook.
- cuda(device: device | int | None = None) Self#
Moves all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
- Arguments:
- device: If specified, all parameters will be copied to that device. If None, the current CUDA device
index will be used.
- Returns:
Module: self
- eval() T#
Set the module in evaluation mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout,BatchNorm, etc.This is equivalent with
self.train(False).See Locally disabling gradient computation for a comparison between .eval() and several similar mechanisms that may be confused with it.
- Returns:
Module: self
- property example_input_array: Tensor | Tuple | Dict | None#
The example input array is a specification of what the module can consume in the
forward()method. The return type is interpreted as follows:Single tensor: It is assumed the model takes a single argument, i.e.,
model.forward(model.example_input_array)Tuple: The input array should be interpreted as a sequence of positional arguments, i.e.,
model.forward(*model.example_input_array)Dict: The input array represents named keyword arguments, i.e.,
model.forward(**model.example_input_array)
- extra_repr() str#
Set the extra representation of the module.
To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
- forward(x)#
Same as
torch.nn.Module.forward().
- freeze() None#
Freeze all params for inference.
Example:
model = MyLightningModule(...) model.freeze()
- get_buffer(target: str) Tensor#
Return the buffer given by
targetif it exists, otherwise throw an error.See the docstring for
get_submodulefor a more detailed explanation of this method’s functionality as well as how to correctly specifytarget.- Args:
- target: The fully-qualified string name of the buffer
to look for. (See
get_submodulefor how to specify a fully-qualified string.)
- Returns:
torch.Tensor: The buffer referenced by
target- Raises:
- AttributeError: If the target string references an invalid
path or resolves to something that is not a buffer
- get_extra_state() Any#
Return any extra state to include in the module’s state_dict.
Implement this and a corresponding
set_extra_state()for your module if you need to store extra state. This function is called when building the module’s state_dict().Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.
- Returns:
object: Any extra state to store in the module’s state_dict
- get_parameter(target: str) Parameter#
Return the parameter given by
targetif it exists, otherwise throw an error.See the docstring for
get_submodulefor a more detailed explanation of this method’s functionality as well as how to correctly specifytarget.- Args:
- target: The fully-qualified string name of the Parameter
to look for. (See
get_submodulefor how to specify a fully-qualified string.)
- Returns:
torch.nn.Parameter: The Parameter referenced by
target- Raises:
- AttributeError: If the target string references an invalid
path or resolves to something that is not an
nn.Parameter
- get_submodule(target: str) Module#
Return the submodule given by
targetif it exists, otherwise throw an error.For example, let’s say you have an
nn.ModuleAthat looks like this:A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) )(The diagram shows an
nn.ModuleA.Ahas a nested submodulenet_b, which itself has two submodulesnet_candlinear.net_cthen has a submoduleconv.)To check whether or not we have the
linearsubmodule, we would callget_submodule("net_b.linear"). To check whether we have theconvsubmodule, we would callget_submodule("net_b.net_c.conv").The runtime of
get_submoduleis bounded by the degree of module nesting intarget. A query againstnamed_modulesachieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists,get_submoduleshould always be used.- Args:
- target: The fully-qualified string name of the submodule
to look for. (See above example for how to specify a fully-qualified string.)
- Returns:
torch.nn.Module: The submodule referenced by
target- Raises:
- AttributeError: If the target string references an invalid
path or resolves to something that is not an
nn.Module
- property global_step: int#
Total training batches seen across all epochs.
If no Trainer is attached, this propery is 0.
- property hparams: AttributeDict | MutableMapping#
The collection of hyperparameters saved with
save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, usehparams_initial.- Returns:
Mutable hyperparameters dictionary
- property hparams_initial: AttributeDict#
The collection of hyperparameters saved with
save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed throughhparams.- Returns:
AttributeDict: immutable initial hyperparameters
- ipu(device: int | device | None = None) T#
Move all model parameters and buffers to the IPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.
Note
This method modifies the module in-place.
- Arguments:
- device (int, optional): if specified, all parameters will be
copied to that device
- Returns:
Module: self
- load_from_checkpoint(checkpoint_path: str | Path | IO, map_location: device | str | int | Callable[[UntypedStorage, str], UntypedStorage | None] | Dict[device | str | int, device | str | int] | None = None, hparams_file: str | Path | None = None, strict: bool | None = None, **kwargs: Any) Self#
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".- Args:
checkpoint_path: Path to checkpoint. This can also be a URL, or file-like object map_location:
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 path to a
.yamlor.csvfile 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
.yamlfile with the hparams you’d like to use. These will be converted into adictand passed into yourLightningModulefor use.If your model’s
hparamsargument isNamespaceand.yamlfile has hierarchical structure, you need to refactor your model to treathparamsasdict.- strict: Whether to strictly enforce that the keys in
checkpoint_pathmatch the keys returned by this module’s state dict. Defaults to
TrueunlessLightningModule.strict_loadingis set, in which case it defaults to the value ofLightningModule.strict_loading.- **kwargs: Any extra keyword args needed to init the model. Can also be used to override saved
hyperparameter values.
- hparams_file: Optional path to a
- Return:
LightningModuleinstance with loaded weights and hyperparameters (if available).- Note:
load_from_checkpointis a class method. You should use yourLightningModuleclass to call it instead of theLightningModuleinstance, or aTypeErrorwill be raised.- Note:
To ensure all layers can be loaded from the checkpoint, this function will call
configure_model()directly after instantiating the model if this hook is overridden in your LightningModule. However, note thatload_from_checkpointdoes not support loading sharded checkpoints, and you may run out of memory if the model is too large. In this case, consider loading through the Trainer via.fit(ckpt_path=...).
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)
- load_state_dict(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)#
Copy parameters and buffers from
state_dictinto this module and its descendants.If
strictisTrue, then the keys ofstate_dictmust exactly match the keys returned by this module’sstate_dict()function.Warning
If
assignisTruethe optimizer must be created after the call toload_state_dictunlessget_swap_module_params_on_conversion()isTrue.- Args:
- state_dict (dict): a dict containing parameters and
persistent buffers.
- strict (bool, optional): whether to strictly enforce that the keys
in
state_dictmatch the keys returned by this module’sstate_dict()function. Default:True- assign (bool, optional): When
False, the properties of the tensors in the current module are preserved while when
True, the properties of the Tensors in the state dict are preserved. The only exception is therequires_gradfield ofDefault: ``False`
- Returns:
NamedTuplewithmissing_keysandunexpected_keysfields:missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys
- Note:
If a parameter or buffer is registered as
Noneand its corresponding key exists instate_dict,load_state_dict()will raise aRuntimeError.
- log(name: str, value: Metric | Tensor | int | float, prog_bar: bool = False, logger: bool | None = None, on_step: bool | None = None, on_epoch: bool | None = None, reduce_fx: str | Callable = 'mean', enable_graph: bool = False, sync_dist: bool = False, sync_dist_group: Any | None = None, add_dataloader_idx: bool = True, batch_size: int | None = None, metric_attribute: str | None = None, rank_zero_only: bool = False) None#
Log a key, value pair.
Example:
self.log('train_loss', loss)
The default behavior per hook is documented here: Automatic Logging.
- Args:
name: key to log. value: value to log. Can be a
float,Tensor, or aMetric. prog_bar: ifTruelogs to the progress bar. logger: ifTruelogs to the logger. on_step: ifTruelogs at this step. The default value is determined by the hook.See Automatic Logging for details.
- on_epoch: if
Truelogs epoch accumulated metrics. The default value is determined by the hook. See Automatic Logging for details.
reduce_fx: reduction function over step values for end of epoch.
torch.mean()by default. enable_graph: ifTrue, will not auto detach the graph. sync_dist: ifTrue, reduces the metric across devices. Use with care as this may lead to a significantcommunication overhead.
sync_dist_group: the DDP group to sync across. add_dataloader_idx: if
True, appends the index of the current dataloader tothe name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.
- batch_size: Current batch_size. This will be directly inferred from the loaded batch,
but for some data structures you might need to explicitly provide it.
- metric_attribute: To restore the metric state, Lightning requires the reference of the
torchmetrics.Metricin your model. This is found automatically if it is a model attribute.- rank_zero_only: Tells Lightning if you are calling
self.logfrom every process (default) or only from rank 0. If
True, you won’t be able to use this metric as a monitor in callbacks (e.g., early stopping). Warning: Improper use can lead to deadlocks! See Advanced Logging for more details.
- on_epoch: if
- log_dict(dictionary: Mapping[str, Metric | Tensor | int | float] | MetricCollection, prog_bar: bool = False, logger: bool | None = None, on_step: bool | None = None, on_epoch: bool | None = None, reduce_fx: str | Callable = 'mean', enable_graph: bool = False, sync_dist: bool = False, sync_dist_group: Any | None = None, add_dataloader_idx: bool = True, batch_size: int | None = None, rank_zero_only: bool = False) None#
Log a dictionary of values at once.
Example:
values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n} self.log_dict(values)
- Args:
- dictionary: key value pairs.
The values can be a
float,Tensor,Metric, orMetricCollection.
prog_bar: if
Truelogs to the progress base. logger: ifTruelogs to the logger. on_step: ifTruelogs at this step.Noneauto-logs for training_step but not validation/test_step. The default value is determined by the hook. See Automatic Logging for details.- on_epoch: if
Truelogs epoch accumulated metrics. Noneauto-logs for val/test step but nottraining_step. The default value is determined by the hook. See Automatic Logging for details.
reduce_fx: reduction function over step values for end of epoch.
torch.mean()by default. enable_graph: ifTrue, will not auto-detach the graph sync_dist: ifTrue, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significantcommunication overhead.
sync_dist_group: the ddp group to sync across. add_dataloader_idx: if
True, appends the index of the current dataloader tothe name (when using multiple). If
False, user needs to give unique names for each dataloader to not mix values.- batch_size: Current batch size. This will be directly inferred from the loaded batch,
but some data structures might need to explicitly provide it.
- rank_zero_only: Tells Lightning if you are calling
self.logfrom every process (default) or only from rank 0. If
True, you won’t be able to use this metric as a monitor in callbacks (e.g., early stopping). Warning: Improper use can lead to deadlocks! See Advanced Logging for more details.
- lr_scheduler_step(scheduler: LRScheduler | ReduceLROnPlateau, metric: Any | None) None#
Override this method to adjust the default way the
Trainercalls each scheduler. By default, Lightning callsstep()and as shown in the example for each scheduler based on itsinterval.- Args:
scheduler: Learning rate scheduler. metric: Value of the monitor used for schedulers like
ReduceLROnPlateau.
Examples:
# DEFAULT def lr_scheduler_step(self, scheduler, metric): if metric is None: scheduler.step() else: scheduler.step(metric) # Alternative way to update schedulers if it requires an epoch value def lr_scheduler_step(self, scheduler, metric): scheduler.step(epoch=self.current_epoch)
- lr_schedulers() None | List[LRScheduler | ReduceLROnPlateau] | LRScheduler | ReduceLROnPlateau#
Returns the learning rate scheduler(s) that are being used during training. Useful for manual optimization.
- Returns:
A single scheduler, or a list of schedulers in case multiple ones are present, or
Noneif no schedulers were returned inconfigure_optimizers().
- manual_backward(loss: Tensor, *args: Any, **kwargs: Any) None#
Call this directly from your
training_step()when doing optimizations manually. By using this, Lightning can ensure that all the proper scaling gets applied when using mixed precision.See manual optimization for more examples.
Example:
def training_step(...): opt = self.optimizers() loss = ... opt.zero_grad() # automatically applies scaling, etc... self.manual_backward(loss) opt.step()
- Args:
loss: The tensor on which to compute gradients. Must have a graph attached. *args: Additional positional arguments to be forwarded to
backward()**kwargs: Additional keyword arguments to be forwarded tobackward()
- modules() Iterator[Module]#
Return an iterator over all modules in the network.
- Yields:
Module: a module in the network
- Note:
Duplicate modules are returned only once. In the following example,
lwill be returned only once.
Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True)
- named_buffers(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) Iterator[Tuple[str, Tensor]]#
Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Args:
prefix (str): prefix to prepend to all buffer names. recurse (bool, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
- Yields:
(str, torch.Tensor): Tuple containing the name and buffer
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size())
- named_children() Iterator[Tuple[str, Module]]#
Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
- Yields:
(str, Module): Tuple containing a name and child module
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module)
- named_modules(memo: Set[Module] | None = None, prefix: str = '', remove_duplicate: bool = True)#
Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Args:
memo: a memo to store the set of modules already added to the result prefix: a prefix that will be added to the name of the module remove_duplicate: whether to remove the duplicated module instances in the result
or not
- Yields:
(str, Module): Tuple of name and module
- Note:
Duplicate modules are returned only once. In the following example,
lwill be returned only once.
Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
- named_parameters(prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) Iterator[Tuple[str, Parameter]]#
Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Args:
prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that are direct members of this module.
- remove_duplicate (bool, optional): whether to remove the duplicated
parameters in the result. Defaults to True.
- Yields:
(str, Parameter): Tuple containing the name and parameter
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())
- on_after_backward() None#
Called after
loss.backward()and before optimizers are stepped.- Note:
If using native AMP, the gradients will not be unscaled at this point. Use the
on_before_optimizer_stepif you need the unscaled gradients.
- on_after_batch_transfer(batch: Any, dataloader_idx: int) Any#
Override to alter or apply batch augmentations to your batch after it is transferred to the device.
- Note:
To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be altered or augmented. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A batch of data
Example:
def on_after_batch_transfer(self, batch, dataloader_idx): batch['x'] = gpu_transforms(batch['x']) return batch
- Raises:
- MisconfigurationException:
If using IPUs,
Trainer(accelerator='ipu').
- See Also:
- on_before_backward(loss: Tensor) None#
Called before
loss.backward().- Args:
loss: Loss divided by number of batches for gradient accumulation and scaled if using AMP.
- on_before_batch_transfer(batch: Any, dataloader_idx: int) Any#
Override to alter or apply batch augmentations to your batch before it is transferred to the device.
- Note:
To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be altered or augmented. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A batch of data
Example:
def on_before_batch_transfer(self, batch, dataloader_idx): batch['x'] = transforms(batch['x']) return batch
- on_before_optimizer_step(optimizer: Optimizer) None#
Called before
optimizer.step().If using gradient accumulation, the hook is called once the gradients have been accumulated. See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.accumulate_grad_batches`.
If using AMP, the loss will be unscaled before calling this hook. See these docs for more information on the scaling of gradients.
If clipping gradients, the gradients will not have been clipped yet.
- Args:
optimizer: Current optimizer being used.
Example:
def on_before_optimizer_step(self, optimizer): # example to inspect gradient information in tensorboard if self.trainer.global_step % 25 == 0: # don't make the tf file huge for k, v in self.named_parameters(): self.logger.experiment.add_histogram( tag=k, values=v.grad, global_step=self.trainer.global_step )
- on_before_zero_grad(optimizer: Optimizer) None#
Called after
training_step()and beforeoptimizer.zero_grad().Called in the training loop after taking an optimizer step and before zeroing grads. Good place to inspect weight information with weights updated.
This is where it is called:
for optimizer in optimizers: out = training_step(...) model.on_before_zero_grad(optimizer) # < ---- called here optimizer.zero_grad() backward()
- Args:
optimizer: The optimizer for which grads should be zeroed.
- property on_gpu: bool#
Returns
Trueif this model is currently located on a GPU.Useful to set flags around the LightningModule for different CPU vs GPU behavior.
- on_load_checkpoint(checkpoint: Dict[str, Any]) None#
Called by Lightning to restore your model. If you saved something with
on_save_checkpoint()this is your chance to restore this.- Args:
checkpoint: Loaded checkpoint
Example:
def on_load_checkpoint(self, checkpoint): # 99% of the time you don't need to implement this method self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
- Note:
Lightning auto-restores global step, epoch, and train state including amp scaling. There is no need for you to restore anything regarding training.
- on_predict_batch_end(outputs: Any | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the predict loop after the batch.
- Args:
outputs: The outputs of predict_step(x) batch: The batched data as it is returned by the prediction DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_predict_batch_start(batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the predict loop before anything happens for that batch.
- Args:
batch: The batched data as it is returned by the test DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_predict_model_eval() None#
Called when the predict loop starts.
The predict loop by default calls
.eval()on the LightningModule before it starts. Override this hook to change the behavior.
- on_save_checkpoint(checkpoint: Dict[str, Any]) None#
Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
- Args:
- checkpoint: The full checkpoint dictionary before it gets dumped to a file.
Implementations of this hook can insert additional data into this dictionary.
Example:
def on_save_checkpoint(self, checkpoint): # 99% of use cases you don't need to implement this method checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
- Note:
Lightning saves all aspects of training (epoch, global step, etc…) including amp scaling. There is no need for you to store anything about training.
- on_test_batch_end(outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the test loop after the batch.
- Args:
outputs: The outputs of test_step(x) batch: The batched data as it is returned by the test DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_test_batch_start(batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the test loop before anything happens for that batch.
- Args:
batch: The batched data as it is returned by the test DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_test_model_eval() None#
Called when the test loop starts.
The test loop by default calls
.eval()on the LightningModule before it starts. Override this hook to change the behavior. See alsoon_test_model_train().
- on_test_model_train() None#
Called when the test loop ends.
The test loop by default restores the training mode of the LightningModule to what it was before starting testing. Override this hook to change the behavior. See also
on_test_model_eval().
- on_train_batch_end(outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int) None#
Called in the training loop after the batch.
- Args:
outputs: The outputs of training_step(x) batch: The batched data as it is returned by the training DataLoader. batch_idx: the index of the batch
- on_train_batch_start(batch: Any, batch_idx: int) int | None#
Called in the training loop before anything happens for that batch.
If you return -1 here, you will skip training for the rest of the current epoch.
- Args:
batch: The batched data as it is returned by the training DataLoader. batch_idx: the index of the batch
- on_train_epoch_end() None#
Called in the training loop at the very end of the epoch.
To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the
LightningModuleand access them in this hook:class MyLightningModule(L.LightningModule): def __init__(self): super().__init__() self.training_step_outputs = [] def training_step(self): loss = ... self.training_step_outputs.append(loss) return loss def on_train_epoch_end(self): # do something with all training_step outputs, for example: epoch_mean = torch.stack(self.training_step_outputs).mean() self.log("training_epoch_mean", epoch_mean) # free up the memory self.training_step_outputs.clear()
- on_validation_batch_end(outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the validation loop after the batch.
- Args:
outputs: The outputs of validation_step(x) batch: The batched data as it is returned by the validation DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_validation_batch_start(batch: Any, batch_idx: int, dataloader_idx: int = 0) None#
Called in the validation loop before anything happens for that batch.
- Args:
batch: The batched data as it is returned by the validation DataLoader. batch_idx: the index of the batch dataloader_idx: the index of the dataloader
- on_validation_model_eval() None#
Called when the validation loop starts.
The validation loop by default calls
.eval()on the LightningModule before it starts. Override this hook to change the behavior. See alsoon_validation_model_train().
- on_validation_model_train() None#
Called when the validation loop ends.
The validation loop by default restores the training mode of the LightningModule to what it was before starting validation. Override this hook to change the behavior. See also
on_validation_model_eval().
- on_validation_model_zero_grad() None#
Called by the training loop to release gradients before entering the validation loop.
- optimizer_step(epoch: int, batch_idx: int, optimizer: Optimizer | LightningOptimizer, optimizer_closure: Callable[[], Any] | None = None) None#
Override this method to adjust the default way the
Trainercalls the optimizer.By default, Lightning calls
step()andzero_grad()as shown in the example. This method (andzero_grad()) won’t be called during the accumulation phase whenTrainer(accumulate_grad_batches != 1). Overriding this hook has no benefit with manual optimization.- Args:
epoch: Current epoch batch_idx: Index of current batch optimizer: A PyTorch optimizer optimizer_closure: The optimizer closure. This closure must be executed as it includes the
calls to
training_step(),optimizer.zero_grad(), andbackward().
Examples:
# DEFAULT def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure): optimizer.step(closure=optimizer_closure) # Learning rate warm-up def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure): # update params optimizer.step(closure=optimizer_closure) # manually warm up lr without a scheduler if self.trainer.global_step < 500: lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0) for pg in optimizer.param_groups: pg["lr"] = lr_scale * self.learning_rate
- optimizer_zero_grad(epoch: int, batch_idx: int, optimizer: Optimizer) None#
Override this method to change the default behaviour of
optimizer.zero_grad().- Args:
epoch: Current epoch batch_idx: Index of current batch optimizer: A PyTorch optimizer
Examples:
# DEFAULT def optimizer_zero_grad(self, epoch, batch_idx, optimizer): optimizer.zero_grad() # Set gradients to `None` instead of zero to improve performance (not required on `torch>=2.0.0`). def optimizer_zero_grad(self, epoch, batch_idx, optimizer): optimizer.zero_grad(set_to_none=True)
See
torch.optim.Optimizer.zero_grad()for the explanation of the above example.
- optimizers(use_pl_optimizer: bool = True) Optimizer | LightningOptimizer | _FabricOptimizer | List[Optimizer] | List[LightningOptimizer] | List[_FabricOptimizer]#
Returns the optimizer(s) that are being used during training. Useful for manual optimization.
- Args:
- use_pl_optimizer: If
True, will wrap the optimizer(s) in a LightningOptimizerfor automatic handling of precision, profiling, and counting of step calls for proper logging and checkpointing. It specifically wraps thestepmethod and custom optimizers that don’t have this method are not supported.
- use_pl_optimizer: If
- Returns:
A single optimizer, or a list of optimizers in case multiple ones are present.
- parameters(recurse: bool = True) Iterator[Parameter]#
Return an iterator over module parameters.
This is typically passed to an optimizer.
- Args:
- recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that are direct members of this module.
- Yields:
Parameter: module parameter
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
- predict_dataloader() Any#
An iterable or collection of iterables specifying prediction samples.
For more information about multiple dataloaders, see this section.
It’s recommended that all data downloads and preparation happen in
prepare_data().predict()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Return:
A
torch.utils.data.DataLoaderor a sequence of them specifying prediction samples.
- predict_step(batch, batch_idx)#
Step function called during
predict(). By default, it callsforward(). Override to add any processing logic.The
predict_step()is used to scale inference on multi-devices.To prevent an OOM error, it is possible to use
BasePredictionWritercallback to write the predictions to disk or database after each batch or on epoch end.The
BasePredictionWritershould be used while using a spawn based accelerator. This happens forTrainer(strategy="ddp_spawn")or training on 8 TPU cores withTrainer(accelerator="tpu", devices=8)as predictions won’t be returned.- Args:
batch: The output of your data iterable, normally a
DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.(only if multiple dataloaders used)
- Return:
Predicted output (optional).
Example
class MyModel(LightningModule): def predict_step(self, batch, batch_idx, dataloader_idx=0): return self(batch) dm = ... model = MyModel() trainer = Trainer(accelerator="gpu", devices=2) predictions = trainer.predict(model, dm)
- prepare_data() None#
Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within.
Warning
DO NOT set state to the model (use
setupinstead) since this is NOT called on every deviceExample:
def prepare_data(self): # good download_data() tokenize() etc() # bad self.split = data_split self.some_state = some_other_state()
In a distributed environment,
prepare_datacan be called in two ways (using prepare_data_per_node)Once per node. This is the default and is only called on LOCAL_RANK=0.
Once in total. Only called on GLOBAL_RANK=0.
Example:
# DEFAULT # called once per node on LOCAL_RANK=0 of that node class LitDataModule(LightningDataModule): def __init__(self): super().__init__() self.prepare_data_per_node = True # call on GLOBAL_RANK=0 (great for shared file systems) class LitDataModule(LightningDataModule): def __init__(self): super().__init__() self.prepare_data_per_node = False
This is called before requesting the dataloaders:
model.prepare_data() initialize_distributed() model.setup(stage) model.train_dataloader() model.val_dataloader() model.test_dataloader() model.predict_dataloader()
- print(*args: Any, **kwargs: Any) None#
Prints only from process 0. Use this in any distributed mode to log only once.
- Args:
*args: The thing to print. The same as for Python’s built-in print function. **kwargs: The same as for Python’s built-in print function.
Example:
def forward(self, x): self.print(x, 'in forward')
- register_backward_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor]) RemovableHandle#
Register a backward hook on the module.
This function is deprecated in favor of
register_full_backward_hook()and the behavior of this function will change in future versions.- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_buffer(name: str, tensor: Tensor | None, persistent: bool = True) None#
Add a buffer to the module.
This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s
running_meanis not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by settingpersistenttoFalse. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict.Buffers can be accessed as attributes using given names.
- Args:
- name (str): name of the buffer. The buffer can be accessed
from this module using the given name
- tensor (Tensor or None): buffer to be registered. If
None, then operations that run on buffers, such as
cuda, are ignored. IfNone, the buffer is not included in the module’sstate_dict.- persistent (bool): whether the buffer is part of this module’s
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features))
- register_forward_hook(hook: Callable[[T, Tuple[Any, ...], Any], Any | None] | Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Any | None], *, prepend: bool = False, with_kwargs: bool = False, always_call: bool = False) RemovableHandle#
Register a forward hook on the module.
The hook will be called every time after
forward()has computed an output.If
with_kwargsisFalseor not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to theforward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()is called. The hook should have the following signature:hook(module, args, output) -> None or modified output
If
with_kwargsisTrue, the forward hook will be passed thekwargsgiven to the forward function and be expected to return the output possibly modified. The hook should have the following signature:hook(module, args, kwargs, output) -> None or modified output
- Args:
hook (Callable): The user defined hook to be registered. prepend (bool): If
True, the providedhookwill be firedbefore all existing
forwardhooks on thistorch.nn.modules.Module. Otherwise, the providedhookwill be fired after all existingforwardhooks on thistorch.nn.modules.Module. Note that globalforwardhooks registered withregister_module_forward_hook()will fire before all hooks registered by this method. Default:False- with_kwargs (bool): If
True, thehookwill be passed the kwargs given to the forward function. Default:
False- always_call (bool): If
Truethehookwill be run regardless of whether an exception is raised while calling the Module. Default:
False
- with_kwargs (bool): If
- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_forward_pre_hook(hook: Callable[[T, Tuple[Any, ...]], Any | None] | Callable[[T, Tuple[Any, ...], Dict[str, Any]], Tuple[Any, Dict[str, Any]] | None], *, prepend: bool = False, with_kwargs: bool = False) RemovableHandle#
Register a forward pre-hook on the module.
The hook will be called every time before
forward()is invoked.If
with_kwargsis false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to theforward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:hook(module, args) -> None or modified input
If
with_kwargsis true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:hook(module, args, kwargs) -> None or a tuple of modified input and kwargs
- Args:
hook (Callable): The user defined hook to be registered. prepend (bool): If true, the provided
hookwill be fired beforeall existing
forward_prehooks on thistorch.nn.modules.Module. Otherwise, the providedhookwill be fired after all existingforward_prehooks on thistorch.nn.modules.Module. Note that globalforward_prehooks registered withregister_module_forward_pre_hook()will fire before all hooks registered by this method. Default:False- with_kwargs (bool): If true, the
hookwill be passed the kwargs given to the forward function. Default:
False
- with_kwargs (bool): If true, the
- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_full_backward_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor], prepend: bool = False) RemovableHandle#
Register a backward hook on the module.
The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:
hook(module, grad_input, grad_output) -> tuple(Tensor) or None
The
grad_inputandgrad_outputare tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place ofgrad_inputin subsequent computations.grad_inputwill only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries ingrad_inputandgrad_outputwill beNonefor all non-Tensor arguments.For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.
Warning
Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.
- Args:
hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided
hookwill be fired beforeall existing
backwardhooks on thistorch.nn.modules.Module. Otherwise, the providedhookwill be fired after all existingbackwardhooks on thistorch.nn.modules.Module. Note that globalbackwardhooks registered withregister_module_full_backward_hook()will fire before all hooks registered by this method.- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_full_backward_pre_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor], prepend: bool = False) RemovableHandle#
Register a backward pre-hook on the module.
The hook will be called every time the gradients for the module are computed. The hook should have the following signature:
hook(module, grad_output) -> tuple[Tensor] or None
The
grad_outputis a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place ofgrad_outputin subsequent computations. Entries ingrad_outputwill beNonefor all non-Tensor arguments.For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.
Warning
Modifying inputs inplace is not allowed when using backward hooks and will raise an error.
- Args:
hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided
hookwill be fired beforeall existing
backward_prehooks on thistorch.nn.modules.Module. Otherwise, the providedhookwill be fired after all existingbackward_prehooks on thistorch.nn.modules.Module. Note that globalbackward_prehooks registered withregister_module_full_backward_pre_hook()will fire before all hooks registered by this method.- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_load_state_dict_post_hook(hook)#
Register a post hook to be run after module’s
load_state_dictis called.- It should have the following signature::
hook(module, incompatible_keys) -> None
The
moduleargument is the current module that this hook is registered on, and theincompatible_keysargument is aNamedTupleconsisting of attributesmissing_keysandunexpected_keys.missing_keysis alistofstrcontaining the missing keys andunexpected_keysis alistofstrcontaining the unexpected keys.The given incompatible_keys can be modified inplace if needed.
Note that the checks performed when calling
load_state_dict()withstrict=Trueare affected by modifications the hook makes tomissing_keysorunexpected_keys, as expected. Additions to either set of keys will result in an error being thrown whenstrict=True, and clearing out both missing and unexpected keys will avoid an error.- Returns:
torch.utils.hooks.RemovableHandle:a handle that can be used to remove the added hook by calling
handle.remove()
- register_parameter(name: str, param: Parameter | None) None#
Add a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Args:
- name (str): name of the parameter. The parameter can be accessed
from this module using the given name
- param (Parameter or None): parameter to be added to the module. If
None, then operations that run on parameters, such ascuda, are ignored. IfNone, the parameter is not included in the module’sstate_dict.
- register_state_dict_pre_hook(hook)#
Register a pre-hook for the
state_dict()method.These hooks will be called with arguments:
self,prefix, andkeep_varsbefore callingstate_dictonself. The registered hooks can be used to perform pre-processing before thestate_dictcall is made.
- static regression(model, metrics=None, device='cpu', **kwargs)#
- requires_grad_(requires_grad: bool = True) T#
Change if autograd should record operations on parameters in this module.
This method sets the parameters’
requires_gradattributes in-place.This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
See Locally disabling gradient computation for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.
- Args:
- requires_grad (bool): whether autograd should record operations on
parameters in this module. Default:
True.
- Returns:
Module: self
- save_hyperparameters(*args: Any, ignore: Sequence[str] | str | None = None, frame: FrameType | None = None, logger: bool = True) None#
Save arguments to
hparamsattribute.- Args:
- args: single object of dict, NameSpace or OmegaConf
or string names or arguments from class
__init__- ignore: an argument name or a list of argument names from
class
__init__to be ignored
frame: a frame object. Default is None logger: Whether to send the hyperparameters to the logger. Default: True
- Example::
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class ManuallyArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # manually assign arguments ... self.save_hyperparameters('arg1', 'arg3') ... def forward(self, *args, **kwargs): ... ... >>> model = ManuallyArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class AutomaticArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # equivalent automatic ... self.save_hyperparameters() ... def forward(self, *args, **kwargs): ... ... >>> model = AutomaticArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg2": abc "arg3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class SingleArgModel(HyperparametersMixin): ... def __init__(self, params): ... super().__init__() ... # manually assign single argument ... self.save_hyperparameters(params) ... def forward(self, *args, **kwargs): ... ... >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14)) >>> model.hparams "p1": 1 "p2": abc "p3": 3.14
>>> from pytorch_lightning.core.mixins import HyperparametersMixin >>> class ManuallyArgsModel(HyperparametersMixin): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # pass argument(s) to ignore as a string or in a list ... self.save_hyperparameters(ignore='arg2') ... def forward(self, *args, **kwargs): ... ... >>> model = ManuallyArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg3": 3.14
- set_extra_state(state: Any) None#
Set extra state contained in the loaded state_dict.
This function is called from
load_state_dict()to handle any extra state found within the state_dict. Implement this function and a correspondingget_extra_state()for your module if you need to store extra state within its state_dict.- Args:
state (dict): Extra state from the state_dict
- setup(stage: str) None#
Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.
- Args:
stage: either
'fit','validate','test', or'predict'
Example:
class LitModel(...): def __init__(self): self.l1 = None def prepare_data(self): download_data() tokenize() # don't do this self.something = else def setup(self, stage): data = load_data(...) self.l1 = nn.Linear(28, data.num_classes)
- state_dict(*args, destination=None, prefix='', keep_vars=False)#
Return a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to
Noneare not included.Note
The returned object is a shallow copy. It contains references to the module’s parameters and buffers.
Warning
Currently
state_dict()also accepts positional arguments fordestination,prefixandkeep_varsin order. However, this is being deprecated and keyword arguments will be enforced in future releases.Warning
Please avoid the use of argument
destinationas it is not designed for end-users.- Args:
- destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned. Otherwise, an
OrderedDictwill be created and returned. Default:None.- prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default:
''.- keep_vars (bool, optional): by default the
Tensors returned in the state dict are detached from autograd. If it’s set to
True, detaching will not be performed. Default:False.
- Returns:
- dict:
a dictionary containing a whole state of the module
Example:
>>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']
- property strict_loading: bool#
Determines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
- teardown(stage: str) None#
Called at the end of fit (train + validate), validate, test, or predict.
- Args:
stage: either
'fit','validate','test', or'predict'
- test_dataloader() Any#
An iterable or collection of iterables specifying test samples.
For more information about multiple dataloaders, see this section.
For data processing use the following pattern:
download in
prepare_data()process and split in
setup()
However, the above are only necessary for distributed processing.
Warning
do not assign state in prepare_data
test()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
- Note:
If you don’t need a test dataset and a
test_step(), you don’t need to implement this method.
- test_step(batch, batch_idx)#
Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.
- Args:
batch: The output of your data iterable, normally a
DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.(only if multiple dataloaders used)
- Return:
Tensor- The loss tensordict- A dictionary. Can include any keys, but must include the key'loss'.None- Skip to the next batch.
# if you have one test dataloader: def test_step(self, batch, batch_idx): ... # if you have multiple test dataloaders: def test_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single test dataset def test_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple test dataloaders,
test_step()will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple test dataloaders def test_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
- Note:
If you don’t need to test you don’t need to implement this method.
- Note:
When the
test_step()is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.
- to(*args: Any, **kwargs: Any) Self#
See
torch.nn.Module.to().
- to_empty(*, device: int | str | device | None, recurse: bool = True) T#
Move the parameters and buffers to the specified device without copying storage.
- Args:
- device (
torch.device): The desired device of the parameters and buffers in this module.
- recurse (bool): Whether parameters and buffers of submodules should
be recursively moved to the specified device.
- device (
- Returns:
Module: self
- to_onnx(file_path: str | Path, input_sample: Any | None = None, **kwargs: Any) None#
Saves the model in ONNX format.
- Args:
file_path: The path of the file the onnx model should be saved to. input_sample: An input for tracing. Default: None (Use self.example_input_array) **kwargs: Will be passed to torch.onnx.export function.
Example:
class SimpleModel(LightningModule): def __init__(self): super().__init__() self.l1 = torch.nn.Linear(in_features=64, out_features=4) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1) model = SimpleModel() input_sample = torch.randn(1, 64) model.to_onnx("export.onnx", input_sample, export_params=True)
- to_torchscript(file_path: str | Path | None = None, method: str | None = 'script', example_inputs: Any | None = None, **kwargs: Any) ScriptModule | Dict[str, ScriptModule]#
By default compiles the whole model to a
ScriptModule. If you want to use tracing, please provided the argumentmethod='trace'and make sure that either the example_inputs argument is provided, or the model hasexample_input_arrayset. If you would like to customize the modules that are scripted you should override this method. In case you want to return multiple modules, we recommend using a dictionary.- Args:
file_path: Path where to save the torchscript. Default: None (no file saved). method: Whether to use TorchScript’s script or trace method. Default: ‘script’ example_inputs: An input to be used to do tracing when method is set to ‘trace’.
Default: None (uses
example_input_array)- **kwargs: Additional arguments that will be passed to the
torch.jit.script()or torch.jit.trace()function.
- **kwargs: Additional arguments that will be passed to the
- Note:
Requires the implementation of the
forward()method.The exported script will be set to evaluation mode.
It is recommended that you install the latest supported version of PyTorch to use this feature without limitations. See also the
torch.jitdocumentation for supported features.
Example:
class SimpleModel(LightningModule): def __init__(self): super().__init__() self.l1 = torch.nn.Linear(in_features=64, out_features=4) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) model = SimpleModel() model.to_torchscript(file_path="model.pt") torch.jit.save(model.to_torchscript( file_path="model_trace.pt", method='trace', example_inputs=torch.randn(1, 64)) )
- Return:
This LightningModule as a torchscript, regardless of whether file_path is defined or not.
- toggle_optimizer(optimizer: Optimizer | LightningOptimizer) None#
Makes sure only the gradients of the current optimizer’s parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
It works with
untoggle_optimizer()to make sureparam_requires_grad_stateis properly reset.- Args:
optimizer: The optimizer to toggle.
- train(mode: bool = True) T#
Set the module in training mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout,BatchNorm, etc.- Args:
- mode (bool): whether to set training mode (
True) or evaluation mode (
False). Default:True.
- mode (bool): whether to set training mode (
- Returns:
Module: self
- train_dataloader() Any#
An iterable or collection of iterables specifying training samples.
For more information about multiple dataloaders, see this section.
The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.
For data processing use the following pattern:
download in
prepare_data()process and split in
setup()
However, the above are only necessary for distributed processing.
Warning
do not assign state in prepare_data
fit()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
- property trainer: Trainer#
- training_step(batch, batch_idx)#
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Args:
batch: The output of your data iterable, normally a
DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.(only if multiple dataloaders used)
- Return:
Tensor- The loss tensordict- A dictionary which can include any keys, but must include the key'loss'in the case of automatic optimization.None- In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.
In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.
Example:
def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss
To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:
def __init__(self): super().__init__() self.automatic_optimization = False # Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx): opt1, opt2 = self.optimizers() # do training_step with encoder ... opt1.step() # do training_step with decoder ... opt2.step()
- Note:
When
accumulate_grad_batches> 1, the loss returned here will be automatically normalized byaccumulate_grad_batchesinternally.
- transfer_batch_to_device(batch: Any, device: device, dataloader_idx: int) Any#
Override this hook if your
DataLoaderreturns tensors wrapped in a custom data structure.The data types listed below (and any arbitrary nesting of them) are supported out of the box:
torch.Tensoror anything that implements .to(…)
For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).
- Note:
This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can use
self.trainer.training/testing/validating/predictingso that you can add different logic as per your requirement.- Args:
batch: A batch of data that needs to be transferred to a new device. device: The target device as defined in PyTorch. dataloader_idx: The index of the dataloader to which the batch belongs.
- Returns:
A reference to the data on the new device.
Example:
def transfer_batch_to_device(self, batch, device, dataloader_idx): if isinstance(batch, CustomBatch): # move all tensors in your custom data structure to the device batch.samples = batch.samples.to(device) batch.targets = batch.targets.to(device) elif dataloader_idx == 0: # skip device transfer for the first dataloader or anything you wish pass else: batch = super().transfer_batch_to_device(batch, device, dataloader_idx) return batch
- Raises:
- MisconfigurationException:
If using IPUs,
Trainer(accelerator='ipu').
- See Also:
move_data_to_device()apply_to_collection()
- unfreeze() None#
Unfreeze all parameters for training.
model = MyLightningModule(...) model.unfreeze()
- untoggle_optimizer(optimizer: Optimizer | LightningOptimizer) None#
Resets the state of required gradients that were toggled with
toggle_optimizer().- Args:
optimizer: The optimizer to untoggle.
- val_dataloader() Any#
An iterable or collection of iterables specifying validation samples.
For more information about multiple dataloaders, see this section.
The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.
It’s recommended that all data downloads and preparation happen in
prepare_data().fit()validate()
- Note:
Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Note:
If you don’t need a validation dataset and a
validation_step(), you don’t need to implement this method.
- validation_step(batch, batch_idx)#
Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.
- Args:
batch: The output of your data iterable, normally a
DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.(only if multiple dataloaders used)
- Return:
Tensor- The loss tensordict- A dictionary. Can include any keys, but must include the key'loss'.None- Skip to the next batch.
# if you have one val dataloader: def validation_step(self, batch, batch_idx): ... # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val dataloaders,
validation_step()will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple validation dataloaders def validation_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
- Note:
If you don’t need to validate you don’t need to implement this method.
- Note:
When the
validation_step()is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.
- xpu(device: int | device | None = None) T#
Move all model parameters and buffers to the XPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.
Note
This method modifies the module in-place.
- Arguments:
- device (int, optional): if specified, all parameters will be
copied to that device
- Returns:
Module: self
- zero_grad(set_to_none: bool = True) None#
Reset gradients of all model parameters.
See similar function under
torch.optim.Optimizerfor more context.- Args:
- set_to_none (bool): instead of setting to zero, set the grads to None.
See
torch.optim.Optimizer.zero_grad()for details.