DL

Experiment

Experiment

class catalyst.dl.experiment.experiment.Experiment(model: torch.nn.modules.module.Module, datasets: OrderedDict[str, Union[Dataset, Dict, Any]] = None, loaders: OrderedDict[str, DataLoader] = None, callbacks: Union[OrderedDict[str, Callback], List[Callback]] = None, logdir: str = None, stage: str = 'train', criterion: torch.nn.modules.module.Module = None, optimizer: torch.optim.optimizer.Optimizer = None, scheduler: torch.optim.lr_scheduler._LRScheduler = None, num_epochs: int = 1, valid_loader: str = 'valid', main_metric: str = 'loss', minimize_metric: bool = True, verbose: bool = False, check_time: bool = False, check_run: bool = False, stage_kwargs: Dict = None, checkpoint_data: Dict = None, distributed_params: Dict = None, initial_seed: int = 42)

Bases: catalyst.core.experiment._Experiment

Super-simple one-staged experiment, you can use to declare experiment in code.

__init__(model: torch.nn.modules.module.Module, datasets: OrderedDict[str, Union[Dataset, Dict, Any]] = None, loaders: OrderedDict[str, DataLoader] = None, callbacks: Union[OrderedDict[str, Callback], List[Callback]] = None, logdir: str = None, stage: str = 'train', criterion: torch.nn.modules.module.Module = None, optimizer: torch.optim.optimizer.Optimizer = None, scheduler: torch.optim.lr_scheduler._LRScheduler = None, num_epochs: int = 1, valid_loader: str = 'valid', main_metric: str = 'loss', minimize_metric: bool = True, verbose: bool = False, check_time: bool = False, check_run: bool = False, stage_kwargs: Dict = None, checkpoint_data: Dict = None, distributed_params: Dict = None, initial_seed: int = 42)

Parameters

• model (Model) – model

• datasets (OrderedDict[str, Union[Dataset, Dict, Any]]) – dictionary with one or several torch.utils.data.Dataset for training, validation or inference used for Loaders automatic creation preferred way for distributed training setup

• loaders (OrderedDict[str, DataLoader]) – dictionary with one or several torch.utils.data.DataLoader for training, validation or inference

• callbacks (Union[List[Callback], OrderedDict[str, Callback]]) – list or dictionary with Catalyst callbacks

• logdir (str) – path to output directory

• stage (str) – current stage

• criterion (Criterion) – criterion function

• optimizer (Optimizer) – optimizer

• scheduler (Scheduler) – scheduler

• num_epochs (int) – number of experiment’s epochs

• valid_loader (str) – loader name used to calculate the metrics and save the checkpoints. For example, you can pass train and then the metrics will be taken from train loader.

• main_metric (str) – the key to the name of the metric by which the checkpoints will be selected.

• minimize_metric (bool) – flag to indicate whether the main_metric should be minimized.

• verbose (bool) – if True, it displays the status of the training to the console.

• check_time (bool) – if True, computes the execution time of training process and displays it to the console.

• check_run (bool) – if True, we run only 3 batches per loader and 3 epochs per stage to check pipeline correctness

• stage_kwargs (dict) – additional stage params

• checkpoint_data (dict) – additional data to save in checkpoint, for example: class_names, date_of_training, etc

• distributed_params (dict) – dictionary with the parameters for distributed and FP16 method

• initial_seed (int) – experiment’s initial seed value

property distributed_params

Dict with the parameters for distributed and FP16 method.

get_callbacks(stage: str) → OrderedDict[str, Callback]

Returns the callbacks for a given stage.

get_criterion(stage: str) → torch.nn.modules.module.Module

Returns the criterion for a given stage.

get_loaders(stage: str, epoch: int = None) → OrderedDict[str, DataLoader]

Returns the loaders for a given stage.

get_model(stage: str) → torch.nn.modules.module.Module

Returns the model for a given stage.

get_optimizer(stage: str, model: torch.nn.modules.module.Module) → torch.optim.optimizer.Optimizer

Returns the optimizer for a given stage.

get_scheduler(stage: str, optimizer=None) → torch.optim.lr_scheduler._LRScheduler

Returns the scheduler for a given stage.

get_stage_params(stage: str) → Mapping[str, Any]

Returns the state parameters for a given stage.

property initial_seed

Experiment’s initial seed value.

property logdir

Path to the directory where the experiment logs.

static process_loaders(loaders: OrderedDict[str, DataLoader], datasets: Dict, stage: str, valid_loader: str, initial_seed: int) → Tuple[OrderedDict[str, DataLoader], str]

Prepares loaders for a given stage.

property stages

Experiment’s stage names (array with one value).

ConfigExperiment

class catalyst.dl.experiment.config.ConfigExperiment(config: Dict)

Bases: catalyst.core.experiment._Experiment

Experiment created from a configuration file.

STAGE_KEYWORDS = ['criterion_params', 'optimizer_params', 'scheduler_params', 'data_params', 'transform_params', 'stage_params', 'callbacks_params']

__init__(config: Dict)

Parameters

config (dict) – dictionary of parameters

property distributed_params

Dict with the parameters for distributed and FP16 methond.

get_callbacks(stage: str) → OrderedDict[Callback]

Returns the callbacks for a given stage.

get_criterion(stage: str) → torch.nn.modules.module.Module

Returns the criterion for a given stage.

get_loaders(stage: str, epoch: int = None) → OrderedDict[str, DataLoader]

Returns the loaders for a given stage.

get_model(stage: str)

Returns the model for a given stage.

get_optimizer(stage: str, model: Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]]) → Union[torch.optim.optimizer.Optimizer, Dict[str, torch.optim.optimizer.Optimizer]]

Returns the optimizer for a given stage.

Parameters

• stage (str) – stage name

• model (Union[Model, Dict[str, Model]]) – model or a dict of models

get_scheduler(stage: str, optimizer: torch.optim.optimizer.Optimizer) → torch.optim.lr_scheduler._LRScheduler

Returns the scheduler for a given stage.

get_stage_params(stage: str) → Mapping[str, Any]

Returns the state parameters for a given stage.

get_transforms(stage: str = None, dataset: str = None) → Callable

Returns transform for a given stage and mode.

Parameters

• stage (str) – stage name

• dataset (str) – dataset name (e.g. “train”, “valid”), will be used only if the value of _key_value` is True

property initial_seed

Experiment’s initial seed value.

property logdir

Path to the directory where the experiment logs.

property stages

Experiment’s stage names.

SupervisedExperiment

class catalyst.dl.experiment.supervised.SupervisedExperiment(model: torch.nn.modules.module.Module, datasets: OrderedDict[str, Union[Dataset, Dict, Any]] = None, loaders: OrderedDict[str, DataLoader] = None, callbacks: Union[OrderedDict[str, Callback], List[Callback]] = None, logdir: str = None, stage: str = 'train', criterion: torch.nn.modules.module.Module = None, optimizer: torch.optim.optimizer.Optimizer = None, scheduler: torch.optim.lr_scheduler._LRScheduler = None, num_epochs: int = 1, valid_loader: str = 'valid', main_metric: str = 'loss', minimize_metric: bool = True, verbose: bool = False, check_time: bool = False, check_run: bool = False, stage_kwargs: Dict = None, checkpoint_data: Dict = None, distributed_params: Dict = None, initial_seed: int = 42)

Bases: catalyst.dl.experiment.experiment.Experiment

Supervised experiment.

The main difference with Experiment that it will add several callbacks by default if you haven’t.

Here are list of callbacks by default:

CriterionCallback:

measures loss with specified criterion.

OptimizerCallback:

abstraction over optimizer step.

SchedulerCallback:

only in case if you provided scheduler to your experiment does lr_scheduler.step

CheckpointCallback:

saves model and optimizer state each epoch callback to save/restore your model/criterion/optimizer/metrics.

ConsoleLogger:

standard Catalyst logger, translates runner.*_metrics to console and text file

TensorboardLogger:

will write runner.*_metrics to tensorboard

RaiseExceptionCallback:

will raise exception if needed

get_callbacks(stage: str) → OrderedDict[str, Callback]

Override of BaseExperiment.get_callbacks method. Will add several of callbacks by default in case they missed.

Parameters

stage (str) – name of stage. It should start with infer if you don’t need default callbacks, as they required only for training stages.

Returns

Ordered dictionary of callbacks

for experiment

Return type

(OrderedDict[str, Callback])

Runner

Runner

class catalyst.dl.runner.runner.Runner(model: Union[torch.nn.modules.module.Module, Dict[str, torch.nn.modules.module.Module]] = None, device: Union[str, torch.device] = None)

Bases: catalyst.core.runner._StageBasedRunner

Deep Learning Runner for supervised, unsupervised, gan, etc runs.

infer(*, model: torch.nn.modules.module.Module, datasets: OrderedDict[str, Union[Dataset, Dict, Any]] = None, loaders: OrderedDict[str, DataLoader] = None, callbacks: Union[List[Callback], OrderedDict[str, Callback]] = None, logdir: str = None, resume: str = None, verbose: bool = False, stage_kwargs: Dict = None, fp16: Union[Dict, bool] = None, check: bool = False, timeit: bool = False, initial_seed: int = 42, state_kwargs: Dict = None) → None

Starts the inference stage of the model.

Parameters

• model (Model) – model for inference

• datasets (OrderedDict[str, Union[Dataset, Dict, Any]]) – dictionary with one or several torch.utils.data.Dataset for training, validation or inference used for Loaders automatic creation preferred way for distributed training setup

• loaders (OrderedDict[str, DataLoader]) – dictionary with one or several torch.utils.data.DataLoader for training, validation or inference

• callbacks (Union[List[Callback], OrderedDict[str, Callback]]) – list or dictionary with Catalyst callbacks

• logdir (str) – path to output directory

• verbose (bool) – if True, it displays the status of the training to the console.

• stage_kwargs (dict) – additional stage params

• checkpoint_data (dict) – additional data to save in checkpoint, for example: class_names, date_of_training, etc

• fp16 (Union[Dict, bool]) – If not None, then sets training to FP16. See https://nvidia.github.io/apex/amp.html#properties if fp16=True, params by default will be {"opt_level": "O1"}

• check (bool) – if True, then only checks that pipeline is working (3 epochs only)

• timeit (bool) – if True, computes the execution time of training process and displays it to the console.

• initial_seed (int) – experiment’s initial seed value

predict_batch(batch: Mapping[str, Any], **kwargs) → Mapping[str, Any]

Run model inference on specified data batch.

Parameters

• batch (Mapping[str, Any]) – dictionary with data batches from DataLoader.

• **kwargs – additional kwargs to pass to the model

Returns

model output dictionary

Return type

Mapping[str, Any]

predict_loader(*, loader: torch.utils.data.dataloader.DataLoader, model: torch.nn.modules.module.Module = None, resume: str = None, fp16: Union[Dict, bool] = None, initial_seed: int = 42) → Generator

Runs model inference on PyTorch Dataloader and returns python Generator with model predictions from runner.predict_batch

Parameters

• loader (DataLoader) –

• model (Model) –

• resume (str) –

• fp16 (Union[Dict, bool]) –

• initial_seed (int) –

Returns

(Generator) model predictions from runner.predict_batch method.

trace(*, model: torch.nn.modules.module.Module = None, batch: Any = None, logdir: str = None, loader: torch.utils.data.dataloader.DataLoader = None, method_name: str = 'forward', mode: str = 'eval', requires_grad: bool = False, fp16: Union[Dict, bool] = None, device: Union[str, torch.device] = 'cpu', predict_params: dict = None) → torch.jit.ScriptModule

Traces model using Torch Jit.

Parameters

• model (Model) – model to trace

• batch – batch to forward through the model to trace

• logdir (str, optional) – If specified, the result will be written to the directory

• loader (DataLoader, optional) – if batch is not specified, the batch will be next(iter(loader))

• method_name (str) – model’s method name that will be traced

• mode (str) – train or eval

• requires_grad (bool) – flag to trace with gradients

• fp16 (Union[Dict, bool]) – If not None, then sets tracing params to FP16

• device (Device) – Torch device or a string

• predict_params (dict) – additional parameters for model forward

train(*, model: torch.nn.modules.module.Module, criterion: torch.nn.modules.module.Module = None, optimizer: torch.optim.optimizer.Optimizer = None, scheduler: torch.optim.lr_scheduler._LRScheduler = None, datasets: OrderedDict[str, Union[Dataset, Dict, Any]] = None, loaders: OrderedDict[str, DataLoader] = None, callbacks: Union[List[Callback], OrderedDict[str, Callback]] = None, logdir: str = None, resume: str = None, num_epochs: int = 1, valid_loader: str = 'valid', main_metric: str = 'loss', minimize_metric: bool = True, verbose: bool = False, stage_kwargs: Dict = None, checkpoint_data: Dict = None, fp16: Union[Dict, bool] = None, distributed: bool = False, check: bool = False, timeit: bool = False, load_best_on_end: bool = False, initial_seed: int = 42, state_kwargs: Dict = None) → None

Starts the train stage of the model.

Parameters

• model (Model) – model to train

• criterion (Criterion) – criterion function for training

• optimizer (Optimizer) – optimizer for training

• scheduler (Scheduler) – scheduler for training

• datasets (OrderedDict[str, Union[Dataset, Dict, Any]]) – dictionary with one or several torch.utils.data.Dataset for training, validation or inference used for Loaders automatic creation preferred way for distributed training setup

• loaders (OrderedDict[str, DataLoader]) – dictionary with one or several torch.utils.data.DataLoader for training, validation or inference

• callbacks (Union[List[Callback], OrderedDict[str, Callback]]) – list or dictionary with Catalyst callbacks

• logdir (str) – path to output directory

• resume (str) – path to checkpoint for model

• num_epochs (int) – number of training epochs

• valid_loader (str) – loader name used to calculate the metrics and save the checkpoints. For example, you can pass train and then the metrics will be taken from train loader.

• main_metric (str) – the key to the name of the metric by which the checkpoints will be selected.

• minimize_metric (bool) – flag to indicate whether the main_metric should be minimized.

• verbose (bool) – if True, it displays the status of the training to the console.

• stage_kwargs (dict) – additional params for stage

• checkpoint_data (dict) – additional data to save in checkpoint, for example: class_names, date_of_training, etc

• fp16 (Union[Dict, bool]) – If not None, then sets training to FP16. See https://nvidia.github.io/apex/amp.html#properties if fp16=True, params by default will be {"opt_level": "O1"}

• distributed (bool) – if True will start training in distributed mode. Note: Works only with python scripts. No jupyter support.

• check (bool) – if True, then only checks that pipeline is working (3 epochs only)

• timeit (bool) – if True, computes the execution time of training process and displays it to the console.

• load_best_on_end (bool) – if True, Runner will load best checkpoint state (model, optimizer, etc) according to validation metrics. Requires specified logdir.

• initial_seed (int) – experiment’s initial seed value

SupervisedRunner

class catalyst.dl.runner.supervised.SupervisedRunner(model: torch.nn.modules.module.Module = None, device: Union[str, torch.device] = None, input_key: Any = 'features', output_key: Any = 'logits', input_target_key: str = 'targets')

Bases: catalyst.dl.runner.runner.Runner

Runner for experiments with supervised model.

__init__(model: torch.nn.modules.module.Module = None, device: Union[str, torch.device] = None, input_key: Any = 'features', output_key: Any = 'logits', input_target_key: str = 'targets')

Parameters

• model (Module) – Torch model object

• device (Device) – Torch device

• input_key (Any) – Key in batch dict mapping for model input

• output_key (Any) – Key in output dict model output will be stored under

• input_target_key (str) – Key in batch dict mapping for target

forward(batch: Mapping[str, Any], **kwargs) → Mapping[str, Any]

Forward method for your Runner. Should not be called directly outside of runner. If your model has specific interface, override this method to use it

Parameters

• batch (Mapping[str, Any]) – dictionary with data batches from DataLoaders.

• **kwargs – additional parameters to pass to the model

predict_batch(batch: Mapping[str, Any], **kwargs) → Mapping[str, Any]

Run model inference on specified data batch.

Warning

You should not override this method. If you need specific model call, override forward() method

Parameters

• batch (Mapping[str, Any]) – dictionary with data batches from DataLoader.

• **kwargs – additional kwargs to pass to the model

Returns

model output dictionary

Return type

Mapping[str, Any]

Callbacks

ConfusionMatrixCallback

class catalyst.dl.callbacks.confusion_matrix.ConfusionMatrixCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'confusion_matrix', version: str = 'tnt', class_names: List[str] = None, num_classes: int = None, plot_params: Dict = None, tensorboard_callback_name: str = '_tensorboard')

Bases: catalyst.core.callback.Callback

@TODO: Docs. Contribution is welcome.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'confusion_matrix', version: str = 'tnt', class_names: List[str] = None, num_classes: int = None, plot_params: Dict = None, tensorboard_callback_name: str = '_tensorboard')

Parameters

@TODO – Docs. Contribution is welcome

on_batch_end(runner: catalyst.core.runner._Runner)

Batch end hook.

Parameters

runner (_Runner) – current runner

on_loader_end(runner: catalyst.core.runner._Runner)

Loader end hook.

Parameters

runner (_Runner) – current runner

on_loader_start(runner: catalyst.core.runner._Runner)

Loader start hook.

Parameters

runner (_Runner) – current runner

InferCallback

class catalyst.dl.callbacks.inference.InferCallback(out_dir=None, out_prefix=None)

Bases: catalyst.core.callback.Callback

@TODO: Docs. Contribution is welcome.

__init__(out_dir=None, out_prefix=None)

Parameters

@TODO – Docs. Contribution is welcome

on_batch_end(runner: catalyst.core.runner._Runner)

Batch end hook.

Parameters

runner (_Runner) – current runner

on_loader_end(runner: catalyst.core.runner._Runner)

Loader end hook.

Parameters

runner (_Runner) – current runner

on_loader_start(runner: catalyst.core.runner._Runner)

Loader start hook.

Parameters

runner (_Runner) – current runner

on_stage_start(runner: catalyst.core.runner._Runner)

Stage start hook.

Parameters

runner (_Runner) – current runner

MixupCallback

class catalyst.dl.callbacks.mixup.MixupCallback(input_key: str = 'targets', output_key: str = 'logits', fields: List[str] = ('features', ), alpha=1.0, on_train_only=True, **kwargs)

Bases: catalyst.core.callbacks.criterion.CriterionCallback

Callback to do mixup augmentation.

More details about mixin can be found in the paper mixup: Beyond Empirical Risk Minimization.

Warning

catalyst.dl.callbacks.MixupCallback is inherited from catalyst.dl.CriterionCallback and does its work. You may not use them together.

__init__(input_key: str = 'targets', output_key: str = 'logits', fields: List[str] = ('features', ), alpha=1.0, on_train_only=True, **kwargs)

Parameters

• fields (List[str]) – list of features which must be affected.

• alpha (float) – beta distribution a=b parameters. Must be >=0. The more alpha closer to zero the less effect of the mixup.

• on_train_only (bool) – Apply to train only. As the mixup use the proxy inputs, the targets are also proxy. We are not interested in them, are we? So, if on_train_only is True, use a standard output/metric for validation.

on_batch_start(runner: catalyst.core.runner._Runner)

Batch start hook.

Parameters

runner (_Runner) – current runner

on_loader_start(runner: catalyst.core.runner._Runner)

Loader start hook.

Parameters

runner (_Runner) – current runner

LRFinder

class catalyst.dl.callbacks.scheduler.LRFinder(final_lr, scale: str = 'log', num_steps: Optional[int] = None, optimizer_key: str = None)

Bases: catalyst.core.callbacks.scheduler.LRUpdater

Helps you find an optimal learning rate for a model, as per suggestion of Cyclical Learning Rates for Training Neural Networks paper. Learning rate is increased in linear or log scale, depending on user input.

See How Do You Find A Good Learning Rate article for details.

__init__(final_lr, scale: str = 'log', num_steps: Optional[int] = None, optimizer_key: str = None)

Parameters

• final_lr – final learning rate to try with

• scale (str) – learning rate increasing scale (“log” or “linear”)

• num_steps (Optional[int]) – number of batches to try; if None - whole loader would be used.

• optimizer_key (str) – which optimizer key to use for learning rate scheduling

calc_lr()

@TODO: Docs. Contribution is welcome.

on_batch_end(runner: catalyst.core.runner._Runner)

@TODO: Docs. Contribution is welcome.

Parameters

runner (_Runner) – current runner

on_loader_start(runner: catalyst.core.runner._Runner)

@TODO: Docs. Contribution is welcome.

Parameters

runner (_Runner) – current runner

Metrics

Accuracy

class catalyst.dl.callbacks.metrics.accuracy.AccuracyCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'accuracy', accuracy_args: List[int] = None, num_classes: int = None, threshold: float = None, activation: str = None)

Bases: catalyst.core.callbacks.metrics.MultiMetricCallback

Accuracy metric callback.

It can be used either for

• multi-class task:

  -you can use accuracy_args. -threshold and activation are not required. -input_key point on tensor: batch_size. -output_key point on tensor: batch_size x num_classes.

• OR multi-label task, in this case:

  -you must specify threshold and activation. -accuracy_args and num_classes will not be used (because of there is no method to apply top-k in multi-label classification). -input_key, output_key point on tensor: batch_size x num_classes. -output_key point on a tensor with binary vectors.

Note

There is no need to choose a type (multi-class/multi label). An appropriate type will be chosen automatically via shape of tensors.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'accuracy', accuracy_args: List[int] = None, num_classes: int = None, threshold: float = None, activation: str = None)

Parameters

• input_key (str) – input key to use for accuracy calculation; specifies our y_true

• output_key (str) – output key to use for accuracy calculation; specifies our y_pred

• prefix (str) – key for the metric’s name

• accuracy_args (List[int]) – specifies which accuracy@K to log: [1] - accuracy [1, 3] - accuracy at 1 and 3 [1, 3, 5] - accuracy at 1, 3 and 5

• num_classes (int) – number of classes to calculate accuracy_args if accuracy_args is None

• threshold (float) – threshold for outputs binarization.

• activation (str) – An torch.nn activation applied to the outputs. Must be one of "none", "Sigmoid", or "Softmax"

class catalyst.dl.callbacks.metrics.accuracy.MapKCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'map', map_args: List[int] = None, num_classes: int = None)

Bases: catalyst.core.callbacks.metrics.MultiMetricCallback

mAP@k metric callback.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'map', map_args: List[int] = None, num_classes: int = None)

Parameters

• input_key (str) – input key to use for calculation mean average accuracy at k; specifies our y_true

• output_key (str) – output key to use for calculation mean average accuracy at k; specifies our y_pred

• prefix (str) – key for the metric’s name

• map_args (List[int]) – specifies which map@K to log. [1] - map@1 [1, 3] - map@1 and map@3 [1, 3, 5] - map@1, map@3 and map@5

• num_classes (int) – number of classes to calculate map_args if map_args is None

AUC

class catalyst.dl.callbacks.metrics.auc.AUCCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'auc', class_names: List[str] = None, num_classes: int = 2, activation: str = 'Sigmoid')

Bases: catalyst.dl.callbacks.meter.MeterMetricsCallback

Calculates the AUC per class for each loader.

Note

Currently, supports binary and multi-label cases.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'auc', class_names: List[str] = None, num_classes: int = 2, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for auc calculation specifies our y_true

• output_key (str) – output key to use for auc calculation; specifies our y_pred

• prefix (str) – name to display for auc when printing

• class_names (List[str]) – class names to display in the logs. If None, defaults to indices for each class, starting from 0

• num_classes (int) – Number of classes; must be > 1

• activation (str) – An torch.nn activation applied to the outputs. Must be one of 'none', 'Sigmoid', or 'Softmax2d'

Dice

class catalyst.dl.callbacks.metrics.dice.DiceCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'dice', eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Bases: catalyst.core.callbacks.metrics.MetricCallback

Dice metric callback.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'dice', eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for dice calculation; specifies our y_true

• output_key (str) – output key to use for dice calculation; specifies our y_pred

class catalyst.dl.callbacks.metrics.dice.MulticlassDiceMetricCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'dice', class_names=None)

Bases: catalyst.core.callback.Callback

Global Multi-Class Dice Metric Callback: calculates the exact dice score across multiple batches. This callback is good for getting the dice score with small batch sizes where the batchwise dice is noisier.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'dice', class_names=None)

Parameters

• input_key (str) – input key to use for dice calculation; specifies our y_true

• output_key (str) – output key to use for dice calculation; specifies our y_pred

• prefix (str) – prefix for printing the metric

• class_names (dict/List) – if dictionary, should be: {class_id: class_name, …} where class_id is an integer This allows you to ignore class indices. if list, make sure it corresponds to the number of classes

on_batch_end(runner: catalyst.core.runner._Runner)

Records the confusion matrix at the end of each batch.

Parameters

runner (_Runner) – current runner

on_loader_end(runner: catalyst.core.runner._Runner)

@TODO: Docs. Contribution is welcome.

Parameters

runner (_Runner) – current runner

F1 score

class catalyst.dl.callbacks.metrics.f1_score.F1ScoreCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'f1_score', beta: float = 1.0, eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Bases: catalyst.core.callbacks.metrics.MetricCallback

F1 score metric callback.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'f1_score', beta: float = 1.0, eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for iou calculation specifies our y_true

• output_key (str) – output key to use for iou calculation; specifies our y_pred

• prefix (str) – key to store in logs

• beta (float) – beta param for f_score

• eps (float) – epsilon to avoid zero division

• threshold (float) – threshold for outputs binarization

• activation (str) – An torch.nn activation applied to the outputs. Must be one of 'none', 'Sigmoid', or 'Softmax2d'

IOU

class catalyst.dl.callbacks.metrics.iou.IouCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'iou', eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Bases: catalyst.core.callbacks.metrics.MetricCallback

IoU (Jaccard) metric callback.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'iou', eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for iou calculation specifies our y_true

• output_key (str) – output key to use for iou calculation; specifies our y_pred

• prefix (str) – key to store in logs

• eps (float) – epsilon to avoid zero division

• threshold (float) – threshold for outputs binarization

• activation (str) – An torch.nn activation applied to the outputs. Must be one of 'none', 'Sigmoid', 'Softmax2d'

catalyst.dl.callbacks.metrics.iou.JaccardCallback

alias of catalyst.dl.callbacks.metrics.iou.IouCallback

class catalyst.dl.callbacks.metrics.iou.ClasswiseIouCallback(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'iou', classes: List[str] = None, num_classes: int = None, eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Bases: catalyst.core.callbacks.metrics.MultiMetricCallback

Classwise IoU (Jaccard) metric callback.

__init__(input_key: str = 'targets', output_key: str = 'logits', prefix: str = 'iou', classes: List[str] = None, num_classes: int = None, eps: float = 1e-07, threshold: float = None, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for iou calculation specifies our y_true

• output_key (str) – output key to use for iou calculation; specifies our y_pred

• prefix (str) – key to store in logs (will be prefix_class_name)

• classes (List[str]) – list of class names You should specify either ‘classes’ or ‘num_classes’

• num_classes (int) – number of classes You should specify either ‘classes’ or ‘num_classes’

• eps (float) – epsilon to avoid zero division

• threshold (float) – threshold for outputs binarization

• activation (str) – An torch.nn activation applied to the outputs. Must be one of 'none', 'Sigmoid', 'Softmax2d'

catalyst.dl.callbacks.metrics.iou.ClasswiseJaccardCallback

alias of catalyst.dl.callbacks.metrics.iou.ClasswiseIouCallback

Global precision, recall and F1-score

class catalyst.dl.callbacks.metrics.ppv_tpr_f1.PrecisionRecallF1ScoreCallback(input_key: str = 'targets', output_key: str = 'logits', class_names: List[str] = None, num_classes: int = 2, threshold: float = 0.5, activation: str = 'Sigmoid')

Bases: catalyst.dl.callbacks.meter.MeterMetricsCallback

Calculates the global precision (positive predictive value or ppv), recall (true positive rate or tpr), and F1-score per class for each loader.

Note

Currently, supports binary and multi-label cases.

__init__(input_key: str = 'targets', output_key: str = 'logits', class_names: List[str] = None, num_classes: int = 2, threshold: float = 0.5, activation: str = 'Sigmoid')

Parameters

• input_key (str) – input key to use for metric calculation specifies our y_true

• output_key (str) – output key to use for metric calculation; specifies our y_pred

• class_names (List[str]) – class names to display in the logs. If None, defaults to indices for each class, starting from 0.

• num_classes (int) – Number of classes; must be > 1

• threshold (float) – threshold for outputs binarization

• activation (str) – An torch.nn activation applied to the outputs. Must be one of 'none', 'Sigmoid', 'Softmax2d'

Utils

Torch

catalyst.dl.utils.torch.get_loader(data_source: Iterable[dict], open_fn: Callable, dict_transform: Callable = None, sampler=None, collate_fn: Callable = <function default_collate>, batch_size: int = 32, num_workers: int = 4, shuffle: bool = False, drop_last: bool = False)

Creates a DataLoader from given source and its open/transform params.

Parameters

• data_source (Iterable[dict]) – and iterable containing your data annotations, (for example path to images, labels, bboxes, etc)

• open_fn (Callable) – function, that can open your annotations dict and transfer it to data, needed by your network (for example open image by path, or tokenize read string)

• dict_transform (callable) – transforms to use on dict (for example normalize image, add blur, crop/resize/etc)

• sampler (Sampler, optional) – defines the strategy to draw samples from the dataset

• collate_fn (callable, optional) – merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset

• batch_size (int, optional) – how many samples per batch to load

• num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process

• shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).

• drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)

Returns

DataLoader with catalyst.data.ListDataset

Trace

catalyst.dl.utils.trace.trace_model(model: torch.nn.modules.module.Module, predict_fn: Callable, batch=None, method_name: str = 'forward', mode: str = 'eval', requires_grad: bool = False, opt_level: str = None, device: Union[str, torch.device] = 'cpu', predict_params: dict = None) → torch.jit.ScriptModule

Traces model using runner and batch.

Parameters

• model – Model to trace

• predict_fn – Function to run prediction with the model provided, takes model, inputs parameters

• batch – Batch to trace the model

• method_name (str) – Model’s method name that will be used as entrypoint during tracing

• mode (str) – Mode for model to trace (train or eval)

• requires_grad (bool) – Flag to use grads

• opt_level (str) – Apex FP16 init level, optional

• device (str) – Torch device

• predict_params (dict) – additional parameters for model forward

Returns

Traced model

Return type

(ScriptModule)

catalyst.dl.utils.trace.trace_model_from_checkpoint(logdir: pathlib.Path, method_name: str, checkpoint_name: str, stage: str = None, loader: Union[str, int] = None, mode: str = 'eval', requires_grad: bool = False, opt_level: str = None, device: Union[str, torch.device] = 'cpu')

Traces model using created experiment and runner.

Parameters

• logdir (Union[str, Path]) – Path to Catalyst logdir with model

• checkpoint_name (str) – Name of model checkpoint to use

• stage (str) – experiment’s stage name

• loader (Union[str, int]) – experiment’s loader name or its index

• method_name (str) – Model’s method name that will be used as entrypoint during tracing

• mode (str) – Mode for model to trace (train or eval)

• requires_grad (bool) – Flag to use grads

• opt_level (str) – AMP FP16 init level

• device (str) – Torch device

Returns

the traced model

catalyst.dl.utils.trace.trace_model_from_runner(runner: catalyst.core.runner._Runner, checkpoint_name: str = None, method_name: str = 'forward', mode: str = 'eval', requires_grad: bool = False, opt_level: str = None, device: Union[str, torch.device] = 'cpu') → torch.jit.ScriptModule

Traces model using created experiment and runner.

Parameters

• runner (Runner) – Current runner.

• checkpoint_name (str) – Name of model checkpoint to use, if None traces current model from runner

• method_name (str) – Model’s method name that will be used as entrypoint during tracing

• mode (str) – Mode for model to trace (train or eval)

• requires_grad (bool) – Flag to use grads

• opt_level (str) – AMP FP16 init level

• device (str) – Torch device

Returns

Traced model

Return type

(ScriptModule)

catalyst.dl.utils.trace.get_trace_name(method_name: str, mode: str = 'eval', requires_grad: bool = False, opt_level: str = None, additional_string: str = None) → str

Creates a file name for the traced model.

Parameters

• method_name (str) – model’s method name

• mode (str) – train or eval

• requires_grad (bool) – flag if model was traced with gradients

• opt_level (str) – opt_level if model was traced in FP16

• additional_string (str) – any additional information

Returns

Filename for traced model to be saved.

Return type

file_name (str)

catalyst.dl.utils.trace.save_traced_model(model: torch.jit.ScriptModule, logdir: Union[str, pathlib.Path] = None, method_name: str = 'forward', mode: str = 'eval', requires_grad: bool = False, opt_level: str = None, out_dir: Union[str, pathlib.Path] = None, out_model: Union[str, pathlib.Path] = None, checkpoint_name: str = None)

Saves traced model.

Parameters

• model (ScriptModule) – Traced model

• logdir (Union[str, Path]) – Path to experiment

• method_name (str) – Name of the method was traced

• mode (str) – Model’s mode - train or eval

• requires_grad (bool) – Whether model was traced with require_grad or not

• opt_level (str) – Apex FP16 init level used during tracing

• out_dir (Union[str, Path]) – Directory to save model to (overrides logdir)

• out_model (Union[str, Path]) – Path to save model to (overrides logdir & out_dir)

• checkpoint_name (str) – Checkpoint name used to restore the model

catalyst.dl.utils.trace.load_traced_model(model_path: Union[str, pathlib.Path], device: Union[str, torch.device] = 'cpu', opt_level: str = None) → torch.jit.ScriptModule

Loads a traced model.

Parameters

• model_path – Path to traced model

• device (str) – Torch device

• opt_level (str) – Apex FP16 init level, optional

Returns

Traced model

Return type

(ScriptModule)

Wizard

catalyst.dl.utils.wizard.run_wizard()

Method to initialize and run wizard.

class catalyst.dl.utils.wizard.Wizard

Bases: object

Class for Catalyst Config API Wizard.

The instance of this class will be created and called from cli command: catalyst-dl init --interactive.

With help of this Wizard user will be able to setup pipeline from available templates and make choices of what predefined classes to use in different parts of pipeline.

__init__()

Initialization of instance of this class will print welcome message and logo of Catalyst in ASCII format. Also here we’ll save all classes of Catalyst own pipeline parts to be able to put user’s modules on top of lists to ease the choice.

run()

Walks user through predefined wizard steps.

Registry

catalyst.dl.registry.Callback(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Criterion(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Optimizer(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Scheduler(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Module(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Model(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Sampler(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Transform(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)

catalyst.dl.registry.Experiment(factory: Union[Type, Callable[[...], Any]] = None, *factories: Union[Type, Callable[[...], Any]], name: str = None, **named_factories: Union[Type, Callable[[...], Any]]) → Union[Type, Callable[[...], Any]]

Adds factory to registry with it’s __name__ attribute or provided name. Signature is flexible.

Parameters

• factory – Factory instance

• factories – More instances

• name – Provided name for first instance. Use only when pass single instance.

• named_factories – Factory and their names as kwargs

Returns

First factory passed

Return type

(Factory)