topobench.model.model module#

This module defines the TBModel class.

class Any(*args, **kwargs)#

Bases: object

Special type indicating an unconstrained type.

Any is compatible with every type.
Any assumed to have all methods.
All values assumed to be instances of Any.

Note that all the above statements are true from the point of view of static type checkers. At runtime, Any should not be used with instance checks.

class Data(x=None, edge_index=None, edge_attr=None, y=None, pos=None, time=None, **kwargs)#

Bases: BaseData, FeatureStore, GraphStore

A data object describing a homogeneous graph. The data object can hold node-level, link-level and graph-level attributes. In general, Data tries to mimic the behavior of a regular :python:`Python` dictionary. In addition, it provides useful functionality for analyzing graph structures, and provides basic PyTorch tensor functionalities. See here for the accompanying tutorial.

from torch_geometric.data import Data

data = Data(x=x, edge_index=edge_index, ...)

# Add additional arguments to `data`:
data.train_idx = torch.tensor([...], dtype=torch.long)
data.test_mask = torch.tensor([...], dtype=torch.bool)

# Analyzing the graph structure:
data.num_nodes
>>> 23

data.is_directed()
>>> False

# PyTorch tensor functionality:
data = data.pin_memory()
data = data.to('cuda:0', non_blocking=True)

Parameters:

x (torch.Tensor, optional) – Node feature matrix with shape [num_nodes, num_node_features]. (default: None)
edge_index (LongTensor, optional) – Graph connectivity in COO format with shape [2, num_edges]. (default: None)
edge_attr (torch.Tensor, optional) – Edge feature matrix with shape [num_edges, num_edge_features]. (default: None)
y (torch.Tensor, optional) – Graph-level or node-level ground-truth labels with arbitrary shape. (default: None)
pos (torch.Tensor, optional) – Node position matrix with shape [num_nodes, num_dimensions]. (default: None)
time (torch.Tensor, optional) – The timestamps for each event with shape [num_edges] or [num_nodes]. (default: None)
**kwargs (optional) – Additional attributes.

__init__(x=None, edge_index=None, edge_attr=None, y=None, pos=None, time=None, **kwargs)#

connected_components()#

Extracts connected components of the graph using a union-find algorithm. The components are returned as a list of Data objects, where each object represents a connected component of the graph.

data = Data()
data.x = torch.tensor([[1.0], [2.0], [3.0], [4.0]])
data.y = torch.tensor([[1.1], [2.1], [3.1], [4.1]])
data.edge_index = torch.tensor(
    [[0, 1, 2, 3], [1, 0, 3, 2]], dtype=torch.long
)

components = data.connected_components()
print(len(components))
>>> 2

print(components[0].x)
>>> Data(x=[2, 1], y=[2, 1], edge_index=[2, 2])

Returns:: A list of disconnected components.
Return type:: List[Data]

debug()#

edge_subgraph(subset)#

Returns the induced subgraph given by the edge indices subset. Will currently preserve all the nodes in the graph, even if they are isolated after subgraph computation.

Parameters:: subset (LongTensor or BoolTensor) – The edges to keep.

classmethod from_dict(mapping)#

Creates a Data object from a dictionary.

get_all_edge_attrs()#

Returns all registered edge attributes.

get_all_tensor_attrs()#

Obtains all feature attributes stored in Data.

is_edge_attr(key)#

Returns True if the object at key key denotes an edge-level tensor attribute.

is_node_attr(key)#

Returns True if the object at key key denotes a node-level tensor attribute.

stores_as(data)#

subgraph(subset)#

Returns the induced subgraph given by the node indices subset.

Parameters:: subset (LongTensor or BoolTensor) – The nodes to keep.

to_dict()#

Returns a dictionary of stored key/value pairs.

to_heterogeneous(node_type=None, edge_type=None, node_type_names=None, edge_type_names=None)#

Converts a Data object to a heterogeneous HeteroData object. For this, node and edge attributes are splitted according to the node-level and edge-level vectors node_type and edge_type, respectively. node_type_names and edge_type_names can be used to give meaningful node and edge type names, respectively. That is, the node_type 0 is given by node_type_names[0]. If the Data object was constructed via to_homogeneous(), the object can be reconstructed without any need to pass in additional arguments.

Parameters:

node_type (torch.Tensor, optional) – A node-level vector denoting the type of each node. (default: None)
edge_type (torch.Tensor, optional) – An edge-level vector denoting the type of each edge. (default: None)
node_type_names (List[str], optional) – The names of node types. (default: None)
edge_type_names (List[Tuple[str, str, str]], optional) – The names of edge types. (default: None)

to_namedtuple()#

Returns a NamedTuple of stored key/value pairs.

update(data)#

Updates the data object with the elements from another data object. Added elements will override existing ones (in case of duplicates).

validate(raise_on_error=True)#

Validates the correctness of the data.

property batch: Tensor | None#: !! processed by numpydoc !!

property edge_attr: Tensor | None#: !! processed by numpydoc !!

property edge_index: Tensor | None#: !! processed by numpydoc !!

property edge_stores: List[EdgeStorage]#: !! processed by numpydoc !!

property edge_weight: Tensor | None#: !! processed by numpydoc !!

property face: Tensor | None#: !! processed by numpydoc !!

property node_stores: List[NodeStorage]#: !! processed by numpydoc !!

property num_edge_features: int#: Returns the number of features per edge in the graph.

property num_edge_types: int#: Returns the number of edge types in the graph.

property num_faces: int | None#: Returns the number of faces in the mesh.

property num_features: int#: Returns the number of features per node in the graph. Alias for num_node_features.

property num_node_features: int#: Returns the number of features per node in the graph.

property num_node_types: int#: Returns the number of node types in the graph.

property num_nodes: int | None#: Returns the number of nodes in the graph.

Note

The number of nodes in the data object is automatically inferred in case node-level attributes are present, e.g., data.x. In some cases, however, a graph may only be given without any node-level attributes. :pyg:`PyG` then guesses the number of nodes according to edge_index.max().item() + 1. However, in case there exists isolated nodes, this number does not have to be correct which can result in unexpected behavior. Thus, we recommend to set the number of nodes in your data object explicitly via data.num_nodes = .... You will be given a warning that requests you to do so.

property pos: Tensor | None#: !! processed by numpydoc !!

property stores: List[BaseStorage]#: !! processed by numpydoc !!

property time: Tensor | None#: !! processed by numpydoc !!

property x: Tensor | None#: !! processed by numpydoc !!

property y: Tensor | int | float | None#: !! processed by numpydoc !!

class LightningModule(*args, **kwargs)#

Bases: _DeviceDtypeModuleMixin, HyperparametersMixin, ModelHooks, DataHooks, CheckpointHooks, Module

__init__(*args, **kwargs)#

all_gather(data, group=None, sync_grads=False)#

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.

Parameters:

data (Tensor | Dict | List | Tuple) – int, float, tensor of shape (batch, …), or a (possibly nested) collection thereof.
group (Any | None) – the process group to gather results from. Defaults to all processes (world)
sync_grads (bool) – flag that allows users to synchronize gradients for the all_gather operation

Returns:

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. For the special case where world_size is 1, no additional dimension is added to the tensor(s).

Return type:

Tensor | Dict | List | Tuple

backward(loss, *args, **kwargs)#

Called to perform backward on the loss returned in training_step(). Override this hook with your own implementation if you need to.

Parameters:: loss (Tensor) – The loss tensor returned by training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).

Example:

def backward(self, loss):
    loss.backward()

clip_gradients(optimizer, gradient_clip_val=None, gradient_clip_algorithm=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 calling self.clip_gradients(opt, gradient_clip_val=0.5, gradient_clip_algorithm="norm") manually in the training step.

Parameters:

optimizer (Optimizer) – Current optimizer being used.
gradient_clip_val (int | float | None) – The value at which to clip gradients.
gradient_clip_algorithm (str | None) – The gradient clipping algorithm to use. Pass gradient_clip_algorithm="value" to clip by value, and gradient_clip_algorithm="norm" to clip by norm.

configure_callbacks()#

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’s callbacks argument. 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 sure ModelCheckpoint callbacks run last.

Returns:: A callback or a list of callbacks which will extend the list of callbacks in the Trainer.
Return type:: Sequence[Callback] | Callback

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, gradient_clip_val=None, gradient_clip_algorithm=None)#

Perform gradient clipping for the optimizer parameters. Called before optimizer_step().

Parameters:

optimizer (Optimizer) – Current optimizer being used.
gradient_clip_val (int | float | None) – The value at which to clip gradients. By default, value passed in Trainer will be available here.
gradient_clip_algorithm (str | None) – 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_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.

Returns:

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 or lr_scheduler_config.
None - Fit will run without any optimizer.

Return type:

The lr_scheduler_config is 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 the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains 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 your LightningModule.

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.

forward(*args, **kwargs)#

Same as torch.nn.Module.forward().

Parameters:

*args (Any) – Whatever you decide to pass into the forward method.
**kwargs (Any) – Keyword arguments are also possible.

Returns:

Your model’s output

Return type:

Any

freeze()#

Freeze all params for inference.

Example:

model = MyLightningModule(...)
model.freeze()

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

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

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

Parameters:

checkpoint_path (str | Path | IO) – Path to checkpoint. This can also be a URL, or file-like object
map_location (device | str | int | Callable[[UntypedStorage, str], UntypedStorage | None] | Dict[device | str | int, device | str | int] | None) – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().
hparams_file (str | Path | None) –
Optional path to a .yaml or .csv file with hierarchical structure as in this example:
```
drop_prob: 0.2
dataloader:
    batch_size: 32
```
You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningModule for use.

If your model’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your model to treat hparams as dict.
strict (bool | None) – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict. Defaults to True unless LightningModule.strict_loading is set, in which case it defaults to the value of LightningModule.strict_loading.
**kwargs (Any) – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Returns:

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

Return type:

Self

Note

load_from_checkpoint is a class method. You should use your LightningModule class to call it instead of the LightningModule instance, or a TypeError will 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 that load_from_checkpoint does 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)

log(name, value, prog_bar=False, logger=None, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, metric_attribute=None, rank_zero_only=False)#

Log a key, value pair.

Example:

self.log('train_loss', loss)

The default behavior per hook is documented here: extensions/logging:Automatic Logging.

Parameters:

name (str) – key to log. Must be identical across all processes if using DDP or any other distributed strategy.
value (Metric | Tensor | int | float) – value to log. Can be a float, Tensor, or a Metric.
prog_bar (bool) – if True logs to the progress bar.
logger (bool | None) – if True logs to the logger.
on_step (bool | None) – if True logs at this step. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.
on_epoch (bool | None) – if True logs epoch accumulated metrics. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.
reduce_fx (str | Callable) – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph (bool) – if True, will not auto detach the graph.
sync_dist (bool) – if True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.
sync_dist_group (Any | None) – the DDP group to sync across.
add_dataloader_idx (bool) – if True, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.
batch_size (int | None) – 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 (str | None) – To restore the metric state, Lightning requires the reference of the torchmetrics.Metric in your model. This is found automatically if it is a model attribute.
rank_zero_only (bool) – Tells Lightning if you are calling self.log from 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.

log_dict(dictionary, prog_bar=False, logger=None, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, rank_zero_only=False)#

Log a dictionary of values at once.

Example:

values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
self.log_dict(values)

Parameters:

dictionary (Mapping[str, Metric | Tensor | int | float] | MetricCollection) – key value pairs. Keys must be identical across all processes if using DDP or any other distributed strategy. The values can be a float, Tensor, Metric, or MetricCollection.
prog_bar (bool) – if True logs to the progress base.
logger (bool | None) – if True logs to the logger.
on_step (bool | None) – if True logs at this step. None auto-logs for training_step but not validation/test_step. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.
on_epoch (bool | None) – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.
reduce_fx (str | Callable) – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph (bool) – if True, will not auto-detach the graph
sync_dist (bool) – if True, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.
sync_dist_group (Any | None) – the ddp group to sync across.
add_dataloader_idx (bool) – if True, appends the index of the current dataloader to the name (when using multiple). If False, user needs to give unique names for each dataloader to not mix values.
batch_size (int | None) – 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 (bool) – Tells Lightning if you are calling self.log from 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, metric)#

Override this method to adjust the default way the Trainer calls each scheduler. By default, Lightning calls step() and as shown in the example for each scheduler based on its interval.

Parameters:

scheduler (LRScheduler | ReduceLROnPlateau) – Learning rate scheduler.
metric (Any | None) – 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()#

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 None if no schedulers were returned in configure_optimizers().
Return type:: None | List[LRScheduler | ReduceLROnPlateau] | LRScheduler | ReduceLROnPlateau

manual_backward(loss, *args, **kwargs)#

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()

Parameters:

loss (Tensor) – The tensor on which to compute gradients. Must have a graph attached.
*args (Any) – Additional positional arguments to be forwarded to backward()
**kwargs (Any) – Additional keyword arguments to be forwarded to backward()

optimizer_step(epoch, batch_idx, optimizer, optimizer_closure=None)#

Override this method to adjust the default way the Trainer calls the optimizer.

By default, Lightning calls step() and zero_grad() as shown in the example. This method (and zero_grad()) won’t be called during the accumulation phase when Trainer(accumulate_grad_batches != 1). Overriding this hook has no benefit with manual optimization.

Parameters:

epoch (int) – Current epoch
batch_idx (int) – Index of current batch
optimizer (Optimizer | LightningOptimizer) – A PyTorch optimizer
optimizer_closure (Callable[[], Any] | None) – The optimizer closure. This closure must be executed as it includes the calls to training_step(), optimizer.zero_grad(), and backward().

Examples:

def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure):
    # Add your custom logic to run directly before `optimizer.step()`

    optimizer.step(closure=optimizer_closure)

    # Add your custom logic to run directly after `optimizer.step()`

optimizer_zero_grad(epoch, batch_idx, optimizer)#

Override this method to change the default behaviour of optimizer.zero_grad().

Parameters:

epoch (int) – Current epoch
batch_idx (int) – Index of current batch
optimizer (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: Literal[True] = True) → LightningOptimizer | List[LightningOptimizer]#

optimizers(use_pl_optimizer: Literal[False]) → Optimizer | List[Optimizer]

Returns the optimizer(s) that are being used during training. Useful for manual optimization.

Parameters:: use_pl_optimizer – If True, will wrap the optimizer(s) in a LightningOptimizer for automatic handling of precision, profiling, and counting of step calls for proper logging and checkpointing. It specifically wraps the step method and custom optimizers that don’t have this method are not supported.
Returns:: A single optimizer, or a list of optimizers in case multiple ones are present.

predict_step(*args, **kwargs)#

Step function called during predict(). By default, it calls forward(). 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 BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Parameters:

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)

Returns:

Predicted output (optional).

Return type:

Any

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)

print(*args, **kwargs)#

Prints only from process 0. Use this in any distributed mode to log only once.

Parameters:

*args (Any) – The thing to print. The same as for Python’s built-in print function.
**kwargs (Any) – The same as for Python’s built-in print function.

Example:

def forward(self, x):
    self.print(x, 'in forward')

test_step(*args, **kwargs)#

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.

Parameters:

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)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

Return type:

Tensor | Mapping[str, Any] | None

# 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_onnx(file_path, input_sample=None, **kwargs)#

Saves the model in ONNX format.

Parameters:

file_path (str | Path) – The path of the file the onnx model should be saved to.
input_sample (Any | None) – An input for tracing. Default: None (Use self.example_input_array)
**kwargs (Any) – 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=None, method='script', example_inputs=None, **kwargs)#

By default compiles the whole model to a ScriptModule. If you want to use tracing, please provided the argument method='trace' and make sure that either the example_inputs argument is provided, or the model has example_input_array set. 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.

Parameters:

file_path (str | Path | None) – Path where to save the torchscript. Default: None (no file saved).
method (str | None) – Whether to use TorchScript’s script or trace method. Default: ‘script’
example_inputs (Any | None) – An input to be used to do tracing when method is set to ‘trace’. Default: None (uses example_input_array)
**kwargs (Any) – Additional arguments that will be passed to the torch.jit.script() or torch.jit.trace() function.

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.jit documentation 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))
)

Returns:: This LightningModule as a torchscript, regardless of whether file_path is defined or not.
Return type:: ScriptModule | Dict[str, 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.

It works with untoggle_optimizer() to make sure param_requires_grad_state is properly reset.

Parameters:: optimizer (Optimizer | LightningOptimizer) – The optimizer to toggle.

training_step(*args, **kwargs)#

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Parameters:

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)

Returns:

Tensor - The loss tensor
dict - 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.

Return type:

Tensor | Mapping[str, Any] | None

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 by accumulate_grad_batches internally.

unfreeze()#

Unfreeze all parameters for training.

model = MyLightningModule(...)
model.unfreeze()

untoggle_optimizer(optimizer)#

Resets the state of required gradients that were toggled with toggle_optimizer().

Parameters:: optimizer (Optimizer | LightningOptimizer) – The optimizer to untoggle.

validation_step(*args, **kwargs)#

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.

Parameters:

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)

Returns:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

Return type:

Tensor | Mapping[str, Any] | None

# 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.

CHECKPOINT_HYPER_PARAMS_KEY = 'hyper_parameters'#

CHECKPOINT_HYPER_PARAMS_NAME = 'hparams_name'#

CHECKPOINT_HYPER_PARAMS_TYPE = 'hparams_type'#

property automatic_optimization: bool#: If set to False you are responsible for calling .backward(), .step(), .zero_grad().

property current_epoch: int#: The current epoch in the Trainer, or 0 if not attached.

property device_mesh: DeviceMesh | None#: Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the configure_model() hook to parallelize the LightningModule.

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)

property fabric: Fabric | None#: !! processed by numpydoc !!

property global_rank: int#: The index of the current process across all nodes and devices.

property global_step: int#

Total training batches seen across all epochs.

If no Trainer is attached, this propery is 0.

property local_rank: int#: The index of the current process within a single node.

property logger: Logger | Logger | None#: Reference to the logger object in the Trainer.

property loggers: List[Logger] | List[Logger]#: Reference to the list of loggers in the Trainer.

property on_gpu: bool#

Returns True if this model is currently located on a GPU.

Useful to set flags around the LightningModule for different CPU vs GPU behavior.

property strict_loading: bool#: Determines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).

property trainer: Trainer#: !! processed by numpydoc !!

class MeanMetric(nan_strategy='warn', **kwargs)#

Bases: BaseAggregator

Aggregate a stream of value into their mean value.

As input to forward and update the metric accepts the following input

value (float or Tensor): a single float or an tensor of float values with arbitrary shape (...,).
weight (float or Tensor): a single float or an tensor of float value with arbitrary shape (...,). Needs to be broadcastable with the shape of value tensor.

As output of forward and compute the metric returns the following output

agg (Tensor): scalar float tensor with aggregated (weighted) mean over all inputs received

Parameters:

nan_strategy (Literal['error', 'warn', 'ignore', 'disable'] | float) – options: - 'error': if any nan values are encountered will give a RuntimeError - 'warn': if any nan values are encountered will give a warning and continue - 'ignore': all nan values are silently removed - 'disable': disable all nan checks - a float: if a float is provided will impute any nan values with this value
kwargs (Any) – Additional keyword arguments, see Metric kwargs for more info.

Raises:

ValueError – If nan_strategy is not one of error, warn, ignore, disable or a float

Example

>>> from torchmetrics.aggregation import MeanMetric
>>> metric = MeanMetric()
>>> metric.update(1)
>>> metric.update(torch.tensor([2, 3]))
>>> metric.compute()
tensor(2.)

__init__(nan_strategy='warn', **kwargs)#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

compute()#

Compute the aggregated value.

plot(val=None, ax=None)#

Plot a single or multiple values from the metric.

Parameters:

val (Tensor | Sequence[Tensor] | None) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
ax (Axes | None) – An matplotlib axis object. If provided will add plot to that axis

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

Return type:

tuple[Figure, Axes | ndarray]

update(value, weight=None)#

Update state with data.

Parameters:

value (float | Tensor) – Either a float or tensor containing data. Additional tensor dimensions will be flattened
weight (float | Tensor | None) – Either a float or tensor containing weights for calculating the average. Shape of weight should be able to broadcast with the shape of value. Default to None corresponding to simple harmonic average.

mean_value: Tensor#

weight: Tensor#

class TBModel(backbone, readout, loss, backbone_wrapper=None, feature_encoder=None, evaluator=None, optimizer=None, **kwargs)#

Bases: LightningModule

A LightningModule to define a network.

Parameters:

backbonetorch.nn.Module: The backbone model to train.
readouttorch.nn.Module: The readout class.
losstorch.nn.Module: The loss class.
backbone_wrappertorch.nn.Module, optional: The backbone wrapper class (default: None).
feature_encodertorch.nn.Module, optional: The feature encoder (default: None).
evaluatorAny, optional: The evaluator class (default: None).
optimizerAny, optional: The optimizer class (default: None).
**kwargsAny: Additional keyword arguments.

__init__(backbone, readout, loss, backbone_wrapper=None, feature_encoder=None, evaluator=None, optimizer=None, **kwargs)#

configure_optimizers()#

Configure optimizers and learning-rate schedulers.

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.

Examples

https://lightning.ai/docs/pytorch/latest/common/lightning_module.html#configure-optimizers

Returns:

dict:: A dict containing the configured optimizers and learning-rate schedulers to be used for training.

forward(batch)#

Perform a forward pass through the model.

Parameters:

batchtorch_geometric.data.Data: Batch object containing the batched data.

Returns:

dict: Dictionary containing the model output, which includes the logits and other relevant information.

log_metrics(mode=None)#

Log metrics.

Parameters:

modestr, optional: The mode of the model, either “train”, “val”, or “test” (default: None).

model_step(batch)#

Perform a single model step on a batch of data.

Parameters:

batchtorch_geometric.data.Data: Batch object containing the batched data.

Returns:

dict: Dictionary containing the model output and the loss.

on_test_epoch_end()#

Lightning hook that is called when a test epoch ends.

This hook is used to log the test metrics.

on_test_epoch_start()#

Lightning hook that is called when a test epoch begins.

This hook is used to reset the test metrics.

on_train_epoch_end()#

Lightning hook that is called when a train epoch ends.

This hook is used to log the train metrics.

on_train_epoch_start()#

Lightning hook that is called when a train epoch begins.

This hook is used to reset the train metrics.

on_val_epoch_start()#

Lightning hook that is called when a validation epoch begins.

This hook is used to reset the validation metrics.

on_validation_epoch_end()#

Lightning hook that is called when a validation epoch ends.

This hook is used to log the validation metrics.

on_validation_epoch_start()#

Hook called when a validation epoch begins.

According pytorch lightning documentation this hook is called at the beginning of the validation epoch.

https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#hooks

Note that the validation step is within the train epoch. Hence here we have to log the train metrics before we reset the evaluator to start the validation loop.

process_outputs(model_out, batch)#

Handle model outputs.

Parameters:

model_outdict: Dictionary containing the model output.
batchtorch_geometric.data.Data: Batch object containing the batched data.

Returns:

dict: Dictionary containing the updated model output.

setup(stage)#

Hook to call torch.compile.

Lightning hook that is 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.

Parameters:

stagestr: Either “fit”, “validate”, “test”, or “predict”.

test_step(batch, batch_idx)#

Perform a single test step on a batch of data.

Parameters:

batchtorch_geometric.data.Data: Batch object containing the batched data.
batch_idxint: The index of the current batch.

training_step(batch, batch_idx)#

Perform a single training step on a batch of data.

Parameters:

batchtorch_geometric.data.Data: Batch object containing the batched data.
batch_idxint: The index of the current batch.

Returns:

torch.Tensor: A tensor of losses between model predictions and targets.

validation_step(batch, batch_idx)#

Perform a single validation step on a batch of data.

Parameters:

batchtorch_geometric.data.Data: Batch object containing the batched data.
batch_idxint: The index of the current batch.