`cca_zoo.deep`.DTCCA#

class cca_zoo.deep.DTCCA(latent_dimensions: int, encoders=None, eps: float = 1e-05, **kwargs)[source]#

Bases: TCCA, DCCA

A class used to fit a DTCCA model.

Is just a thin wrapper round DCCA with the DTCCA objective

References

Wong, Hok Shing, et al. “Deep Tensor CCA for Multi-view Learning.” IEEE Transactions on Big Data (2021).

add_module(name: str, module: Module | None) → None#

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:

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.

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 – 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

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.

apply(fn: Callable[[Module], None]) → T#

Applies fn recursively 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).

Parameters:: fn (Module -> None) – function to be applied to each submodule
Returns:: self
Return type:: Module

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)
)

average_pairwise_correlations(views: Iterable[ndarray], **kwargs) → ndarray#

Calculate the average pairwise correlations between representations in each dimension.

Parameters:

views (list/tuple of numpy arrays or array-like objects with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –

Returns:

average_pairwise_correlations

Return type:

numpy array of shape (latent_dimensions, )

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.

Parameters:: 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).

Example:

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

bfloat16() → T#

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:: self
Return type:: Module

buffers(recurse: bool = True) → Iterator[Tensor]#

Returns an iterator over module buffers.

Parameters:: 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)

canonical_loadings_(views: Iterable[ndarray], normalize: bool = True, **kwargs) → List[ndarray]#

Calculate canonical loadings for each view.

Canonical loadings represent the correlation between the original variables in a view and their respective canonical variates. Canonical variates are linear combinations of the original variables formed to maximize the correlation with canonical variates from another view.

Mathematically, given two representations (X_i), canonical variates from the representations are:

(Z_i = w_i^T X_i)

The canonical loading for a variable in (X_i) is the correlation between that variable and (Z_i).

Parameters:: views (list/tuple of numpy arrays) – Each array corresponds to a view. All representations must have the same number of rows (observations).
Returns:: loadings_ – Canonical loadings for each view. High absolute values indicate that the respective original variables play a significant role in defining the canonical variate.
Return type:: list of numpy arrays

children() → Iterator[Module]#

Returns an iterator over immediate children modules.

Yields:: Module – a child module

clip_gradients(optimizer: Optimizer, gradient_clip_val: int | float | 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 calling self.clip_gradients(opt, gradient_clip_val=0.5, gradient_clip_algorithm="norm") manually in the training step.

Parameters:

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() → None#: Configures the callbacks for the model.

configure_gradient_clipping(optimizer: Optimizer, gradient_clip_val: int | float | None = None, gradient_clip_algorithm: str | None = None) → None#

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

Parameters:

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.

configure_optimizers() → Optimizer | Tuple[List[Optimizer], List[_LRScheduler]]#: Configures the optimizer and the learning rate scheduler.

configure_sharded_model() → None#

Deprecated.

Use configure_model() instead.

correlations(views: Iterable[ndarray], **kwargs)#

Predicts the correlation for the given data using the fit model

Parameters:

views – list/tuple of numpy arrays or array likes with the same number of rows (samples)
kwargs – any additional keyword arguments required by the given model

cpu() → Self#: See torch.nn.Module.cpu().

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.

Parameters:: device – If specified, all parameters will be copied to that device. If None, the current CUDA device index will be used.
Returns:: self
Return type:: Module

static detach_all(z: List[Tensor]) → List[Tensor]#: Detaches all tensors in a list from the computation graph.

double() → Self#: See torch.nn.Module.double().

eval() → T#

Sets 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:: self
Return type:: Module

explained_covariance(views: Iterable[ndarray]) → ndarray#

Calculates the covariance matrix of the transformed components for each view.

Parameters:: views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
Returns:: explained_covariances – Covariance matrices for the transformed components of each view.
Return type:: list of numpy arrays

explained_covariance_cumulative(views: Iterable[ndarray]) → ndarray#

Calculates the cumulative explained covariance ratio for each latent dimension for each view.

Returns:: cumulative_ratios
Return type:: list of numpy arrays

explained_variance(views: Iterable[ndarray]) → List[ndarray]#

Calculates the variance captured by each latent dimension for each view.

Returns:: transformed_vars
Return type:: list of numpy arrays

explained_variance_cumulative(views: Iterable[ndarray]) → List[ndarray]#

Calculates the cumulative explained variance ratio for each latent dimension for each view.

Returns:: cumulative_ratios
Return type:: list of numpy arrays

explained_variance_ratio(views: Iterable[ndarray]) → List[ndarray]#

Calculates the ratio of the variance captured by each latent dimension to the total variance for each view.

Returns:: explained_variance_ratios
Return type:: list of numpy arrays

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.

fit(views: Iterable[ndarray], y=None, **kwargs)#

Fits the model to the given data

Parameters:

views (list/tuple of numpy arrays or array likes with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –

Returns:

self

Return type:

object

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

float() → Self#: See torch.nn.Module.float().

forward(views, **kwargs)#: Returns the latent representations for each view.

freeze() → None#

Freeze all params for inference.

Example:

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

get_buffer(target: str) → Tensor#

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters:: target – The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns:: The buffer referenced by target
Return type:: torch.Tensor
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not a buffer

get_extra_state() → Any#

Returns 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:: Any extra state to store in the module’s state_dict
Return type:: object

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_parameter(target: str) → Parameter#

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Parameters:: target – The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify a fully-qualified string.)
Returns:: The Parameter referenced by target
Return type:: torch.nn.Parameter
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Parameter

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

get_submodule(target: str) → Module#

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let’s say you have an nn.Module A that 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.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves 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_submodule should always be used.

Parameters:: target – The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
Returns:: The submodule referenced by target
Return type:: torch.nn.Module
Raises:: AttributeError – If the target string references an invalid path or resolves to something that is not an nn.Module

half() → Self#: See torch.nn.Module.half().

ipu(device: int | device | None = None) → T#

Moves 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.

Parameters:: device (int, optional) – if specified, all parameters will be copied to that device
Returns:: self
Return type:: Module

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 – 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 .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 – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict.
**kwargs – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Returns:

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

Note

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

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)#

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Warning

If assign is True the optimizer must be created after the call to load_state_dict.

Parameters:

state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
assign (bool, optional) – whether to assign items in the state dictionary to their corresponding keys in the module instead of copying them inplace into the module’s current parameters and buffers. 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. Default: False

Returns:

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

Log a key, value pair.

Example:

self.log('train_loss', loss)

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

Parameters:

name – key to log.
value – value to log. Can be a float, Tensor, or a Metric.
prog_bar – if True logs to the progress bar.
logger – if True logs to the logger.
on_step – if True logs at this step. The default value is determined by the hook. See Automatic Logging for details.
on_epoch – if True logs 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 – if True, will not auto detach the graph.
sync_dist – if True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.
sync_dist_group – the DDP group to sync across.
add_dataloader_idx – 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 – 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.Metric in your model. This is found automatically if it is a model attribute.
rank_zero_only – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

Log a dictionary of values at once.

Example:

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

Parameters:

dictionary – key value pairs. The values can be a float, Tensor, Metric, or MetricCollection.
prog_bar – if True logs to the progress base.
logger – if True logs to the logger.
on_step – 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 Automatic Logging for details.
on_epoch – 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 Automatic Logging for details.
reduce_fx – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph – if True, will not auto-detach the graph
sync_dist – if True, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.
sync_dist_group – the ddp group to sync across.
add_dataloader_idx – 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 – 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 – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

loss(batch, **kwargs)#: Returns the loss components for each view.

lr_scheduler_step(scheduler: LRScheduler | ReduceLROnPlateau, metric: Any | None) → None#

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 – 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 None if no schedulers were returned in configure_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()

Parameters:

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 to backward()

modules() → Iterator[Module]#

Returns 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, l will 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]]#

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:

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]]#

Returns 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)#

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Parameters:

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, l will 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]]#

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:

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_step if 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/predicting so that you can add different logic as per your requirement.

Parameters:

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_after_batch_transfer()
transfer_batch_to_device()

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:`~lightning.pytorch.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.

Parameters:: 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 before optimizer.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()

Parameters:: optimizer – The optimizer for which grads should be zeroed.

on_fit_end() → None#

Called at the very end of fit.

If on DDP it is called on every process

on_fit_start() → None#

Called at the very beginning of fit.

If on DDP it is called on every process

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.

Parameters:: 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.

Parameters:

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.

Parameters:

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_end() → None#: Called at the end of predicting.

on_predict_epoch_end() → None#: Called at the end of predicting.

on_predict_epoch_start() → None#: Called at the beginning of predicting.

on_predict_model_eval() → None#: Sets the model to eval during the predict loop.

on_predict_start() → None#: Called at the beginning of predicting.

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.

Parameters:: 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.

Parameters:

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.

Parameters:

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_end() → None#: Called at the end of testing.

on_test_epoch_end() → None#: Called in the test loop at the very end of the epoch.

on_test_epoch_start() → None#: Called in the test loop at the very beginning of the epoch.

on_test_model_eval() → None#: Sets the model to eval during the test loop.

on_test_model_train() → None#: Sets the model to train during the test loop.

on_test_start() → None#: Called at the beginning of testing.

on_train_batch_end(outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int) → None#

Called in the training loop after the batch.

Parameters:

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.

Parameters:

batch – The batched data as it is returned by the training DataLoader.
batch_idx – the index of the batch

on_train_end() → None#: Called at the end of training before logger experiment is closed.

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 LightningModule and 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_train_epoch_start() → None#: Called in the training loop at the very beginning of the epoch.

on_train_start() → None#: Called at the beginning of training after sanity check.

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.

Parameters:

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.

Parameters:

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_end() → None#: Called at the end of validation.

on_validation_epoch_end() → None#: Called in the validation loop at the very end of the epoch.

on_validation_epoch_start() → None#: Called in the validation loop at the very beginning of the epoch.

on_validation_model_eval() → None#: Sets the model to eval during the val loop.

on_validation_model_train() → None#: Sets the model to train during the val loop.

on_validation_model_zero_grad() → None#: Called by the training loop to release gradients before entering the validation loop.

on_validation_start() → None#: Called at the beginning of validation.

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 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 – 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(), and backward().

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().

Parameters:

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.

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.

pairwise_correlations(loader: DataLoader)#

Calculate pairwise correlations between representations in each dimension.

Parameters:

views (list/tuple of numpy arrays or array-like objects with the same number of rows (samples)) –
kwargs (any additional keyword arguments required by the given model) –

Returns:

pairwise_correlations

Return type:

numpy array of shape (n_views, n_views, latent_dimensions)

parameters(recurse: bool = True) → Iterator[Parameter]#

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:: 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()
prepare_data()
setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Returns:: A torch.utils.data.DataLoader or a sequence of them specifying prediction samples.

predict_step(*args: Any, **kwargs: Any) → Any#

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).

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 setup instead) since this is NOT called on every device

Example:

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_data can 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.

Parameters:

*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#

Registers 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:: a handle that can be used to remove the added hook by calling handle.remove()
Return type:: torch.utils.hooks.RemovableHandle

register_buffer(name: str, tensor: Tensor | None, persistent: bool = True) → None#

Adds 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_mean is 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 setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters:

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. If None, the buffer is not included in the module’s state_dict.
persistent (bool) – whether the buffer is part of this module’s state_dict.

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#

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output.

If with_kwargs is 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 the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called. The hook should have the following signature:

hook(module, args, output) -> None or modified output

If with_kwargs is True, the forward hook will be passed the kwargs given 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

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If True, the provided hook will be fired before all existing forward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this torch.nn.modules.Module. Note that global forward hooks registered with register_module_forward_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If True, the hook will be passed the kwargs given to the forward function. Default: False
always_call (bool) – If True the hook will be run regardless of whether an exception is raised while calling the Module. Default: False

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

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#

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked.

If with_kwargs is 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 the forward. 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_kwargs is 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

Parameters:

hook (Callable) – The user defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing forward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this torch.nn.modules.Module. Note that global forward_pre hooks registered with register_module_forward_pre_hook() will fire before all hooks registered by this method. Default: False
with_kwargs (bool) – If true, the hook will be passed the kwargs given to the forward function. Default: False

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_full_backward_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor], prepend: bool = False) → RemovableHandle#

Registers 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_input and grad_output are 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 of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for 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.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this torch.nn.modules.Module. Note that global backward hooks registered with register_module_full_backward_hook() will fire before all hooks registered by this method.

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_full_backward_pre_hook(hook: Callable[[Module, Tuple[Tensor, ...] | Tensor], None | Tuple[Tensor, ...] | Tensor], prepend: bool = False) → RemovableHandle#

Registers 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_output is 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 of grad_output in subsequent computations. Entries in grad_output will be None for all non-Tensor arguments.

Warning

Modifying inputs inplace is not allowed when using backward hooks and will raise an error.

Parameters:

hook (Callable) – The user-defined hook to be registered.
prepend (bool) – If true, the provided hook will be fired before all existing backward_pre hooks on this torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this torch.nn.modules.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook() will fire before all hooks registered by this method.

Returns:

a handle that can be used to remove the added hook by calling handle.remove()

Return type:

torch.utils.hooks.RemovableHandle

register_load_state_dict_post_hook(hook)#

Registers a post hook to be run after module’s load_state_dict is called.

It should have the following signature::: hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling load_state_dict() with strict=True are affected by modifications the hook makes to missing_keys or unexpected_keys, as expected. Additions to either set of keys will result in an error being thrown when strict=True, and clearing out both missing and unexpected keys will avoid an error.

Returns:: a handle that can be used to remove the added hook by calling handle.remove()
Return type:: torch.utils.hooks.RemovableHandle

register_module(name: str, module: Module | None) → None#: Alias for add_module().

register_parameter(name: str, param: Parameter | None) → None#

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:

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 as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

register_state_dict_pre_hook(hook)#: These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self. The registered hooks can be used to perform pre-processing before the state_dict call is made.

requires_grad_(requires_grad: bool = True) → T#

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ requires_grad attributes 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.

Parameters:: requires_grad (bool) – whether autograd should record operations on parameters in this module. Default: True.
Returns:: self
Return type:: Module

save_hyperparameters(*args: Any, ignore: Sequence[str] | str | None = None, frame: frame | None = None, logger: bool = True) → None#

Save arguments to hparams attribute.

Parameters:

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 lightning.pytorch.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 lightning.pytorch.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 lightning.pytorch.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 lightning.pytorch.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

score(views: Iterable[ndarray], **kwargs)#

Calculate the sum of average pairwise correlations between representations.

Parameters:

views (list/tuple of numpy arrays or array-like objects with the same number of rows (samples)) –
y (None) –
kwargs (any additional keyword arguments required by the given model) –

Returns:

score – Sum of average pairwise correlations between representations.

Return type:

float

set_extra_state(state: Any)#

This function is called from load_state_dict() to handle any extra state found within the state_dict. Implement this function and a corresponding get_extra_state() for your module if you need to store extra state within its state_dict.

Parameters:: state (dict) – Extra state from the state_dict

set_fit_request(*, views: bool | None | str = '$UNCHANGED$') → DTCCA#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: views (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for views parameter in fit.
Returns:: self – The updated object.
Return type:: object

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas"}, default=None) –

Configure output of transform and fit_transform.

”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

set_score_request(*, views: bool | None | str = '$UNCHANGED$') → DTCCA#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: views (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for views parameter in score.
Returns:: self – The updated object.
Return type:: object

set_transform_request(*, loader: bool | None | str = '$UNCHANGED$') → DTCCA#

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: loader (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for loader parameter in transform.
Returns:: self – The updated object.
Return type:: object

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.

Parameters:: 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)

share_memory() → T#: See torch.Tensor.share_memory_()

state_dict(*args, destination=None, prefix='', keep_vars=False)#

Returns 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 None are 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 for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will 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 Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

teardown(stage: str) → None#

Called at the end of fit (train + validate), validate, test, or predict.

Parameters:: 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()
prepare_data()
setup()

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: Dict[str, Any], batch_idx: int) → Tensor#: Performs one step of testing on a batch of representations.

to(*args: Any, **kwargs: Any) → Self#: See torch.nn.Module.to().

to_empty(*, device: str | device, recurse: bool = True) → T#

Moves the parameters and buffers to the specified device without copying storage.

Parameters:

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.

Returns:

self

Return type:

Module

to_onnx(file_path: str | Path, input_sample: Any | None = None, **kwargs: Any) → None#

Saves the model in ONNX format.

Parameters:

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)

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 – 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.

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.

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 sure param_requires_grad_state is properly reset.

Parameters:: optimizer – The optimizer to toggle.

train(mode: bool = True) → T#

Sets the module in training mode.

Parameters:: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns:: self
Return type:: Module

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:`~lightning.pytorch.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()
prepare_data()
setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

training_step(batch: Dict[str, Any], batch_idx: int) → Tensor#: Performs one step of training on a batch of representations.

transfer_batch_to_device(batch: Any, device: device, dataloader_idx: int) → Any#

Override this hook if your DataLoader returns 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.Tensor or anything that implements .to(…)
list
dict
tuple

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/predicting so that you can add different logic as per your requirement.

Parameters:

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').

cca_zoo.deep.DTCCA#

`cca_zoo.deep`.DTCCA#