Callbacks¶

Run-based ¶

BackwardCallback ¶

class catalyst.callbacks.backward.BackwardCallback(metric_key: str, grad_clip_fn: Union[str, Callable] = None, grad_clip_params: Dict = None, log_gradient: bool = False)[source]¶

Bases: catalyst.core.callback.IBackwardCallback

Optimizer callback, abstraction over backward step.

Parameters

metric_key – a key to get loss from runner.batch_metrics
grad_clip_fn – callable gradient cliping function or it’s name
grad_clip_params – key-value parameters for grad_clip_fn
log_gradient – boolean flag to log gradient norm to runner.batch_metrics

Note

Please follow the minimal examples sections for more use cases.

BatchOverfitCallback ¶

class catalyst.callbacks.batch_overfit.BatchOverfitCallback(**kwargs)[source]¶

Bases: catalyst.core.callback.Callback

Callback to overfit loaders with specified number of batches. By default we use 1 batch for loader.

Parameters: kwargs – loader names and their number of batches to overfit.

For example, if you have train, train_additional, valid and valid_additional loaders and wan’t to overfit train on first 1 batch, train_additional on first 2 batches, valid - on first 20% of batches and valid_additional - on 50% batches:

from catalyst.dl import SupervisedRunner, BatchOverfitCallback
runner = SupervisedRunner()
runner.train(
    ...
    loaders={
        "train": ...,
        "train_additional": ...,
        "valid": ...,
        "valid_additional":...
    }
    ...
    callbacks=[
        ...
        BatchOverfitCallback(
            train_additional=2,
            valid=0.2,
            valid_additional=0.5
        ),
        ...
    ]
    ...
)

Minimal working example

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
    callbacks=[dl.BatchOverfitCallback(train=10, valid=0.5)]
)

BatchTransformCallback ¶

class catalyst.callbacks.batch_transform.BatchTransformCallback(transform: Union[Callable, str], scope: str, input_key: Union[List[str], str] = None, output_key: Union[List[str], str] = None, transform_kwargs: Dict[str, Any] = None)[source]¶

Bases: catalyst.core.callback.Callback

Preprocess your batch with specified function.

Parameters

transform – Function to apply. If string will get function from registry.
scope – "on_batch_end" (post-processing model output) or "on_batch_start" (pre-processing model input).
input_key – Keys in batch dict to apply function. Defaults to None.
output_key – Keys for output. If None then will apply function inplace to keys_to_apply. Defaults to None.
transform_kwargs – Kwargs for transform.

Raises

TypeError – When keys is not str or a list. When scope is not in ["on_batch_end", "on_batch_start"].

Examples

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            input_key="logits", output_key="scores", transform="F.sigmoid"
        ),
        dl.CriterionCallback(
            input_key="logits", target_key="targets", metric_key="loss"
        ),
        dl.OptimizerCallback(metric_key="loss"),
        dl.SchedulerCallback(),
        dl.CheckpointCallback(
            logdir="./logs",
            loader_key="valid",
            metric_key="map01",
            minimize=False
        ),
    ]
)

class CustomRunner(dl.Runner):

    def handle_batch(self, batch):
        logits = self.model(
            batch["features"].view(batch["features"].size(0), -1)
        )

        loss = F.cross_entropy(logits, batch["targets"])
        accuracy01, accuracy03 = metrics.accuracy(
            logits, batch["targets"], topk=(1, 3)
        )
        self.batch_metrics.update({
            "loss": loss,
            "accuracy01":accuracy01,
            "accuracy03": accuracy03
        })

        if self.is_train_loader:
            self.engine.backward(loss)
            self.optimizer.step()
            self.optimizer.zero_grad()


class MnistDataset(torch.utils.data.Dataset):
    def __init__(self, dataset):
        self.dataset = dataset

    def __getitem__(self, item):
        return {
            "features": self.dataset[item][0],
            "targets": self.dataset[item][1]
        }

    def __len__(self):
        return len(self.dataset)

model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MnistDataset(
            MNIST(os.getcwd(), train=False)
        ),
        batch_size=32,
    ),
    "valid": DataLoader(
        MnistDataset(
            MNIST(os.getcwd(), train=False)
        ),
        batch_size=32,
    ),
}

transrorms = [
    augmentation.RandomAffine(degrees=(-15, 20), scale=(0.75, 1.25)),
]

runner = CustomRunner()

# model training
runner.train(
    model=model,
    optimizer=optimizer,
    loaders=loaders,
    logdir="./logs",
    num_epochs=5,
    verbose=False,
    load_best_on_end=True,
    check=True,
    callbacks=[
        BatchTransformCallback(
            transform=transrorms,
            scope="on_batch_start",
            input_key="features"
        )
    ],
)

...
callbacks:
    transform:
        _target_: BatchTransformCallback
        transform: catalyst.ToTensor
        scope: on_batch_start
        input_key: features

CheckpointCallback ¶

class catalyst.callbacks.checkpoint.CheckpointCallback(logdir: str, loader_key: str = None, metric_key: str = None, minimize: bool = None, topk: int = 1, mode: str = 'model', save_last: bool = True, save_best: bool = True, resume_model: str = None, resume_runner: str = None, load_best_on_end: bool = False)[source]¶

Bases: catalyst.core.callback.ICheckpointCallback

Checkpoint callback to save/restore your model/runner.

Parameters

logdir – directory to store checkpoints
loader_key – loader key for best model selection (based on metric score over the dataset)
metric_key – metric key for best model selection (based on metric score over the dataset)
minimize – boolean flag to minimize the required metric
topk – number of best checkpoint to keep
mode – checkpoint type to save, model or runner. (default: model)
save_last – boolean flag to save extra last checkpoint as {mode}.last.pth
save_best – boolean flag to save extra best checkpoint as {mode}.best.pth
resume_model – path to model checkpoint to load on experiment start
resume_runner – path to runner checkpoint to load on experiment start
load_best_on_end – boolean flag to load best model on experiment end

on_epoch_end_best(runner: catalyst.core.runner.IRunner) → None[source]¶: Event handler.

on_epoch_end_last(runner: catalyst.core.runner.IRunner) → None[source]¶: Event handler.

CheckRunCallback ¶

class catalyst.callbacks.misc.CheckRunCallback(num_batch_steps: int = 3, num_epoch_steps: int = 3)[source]¶

Bases: catalyst.core.callback.Callback

Executes only a pipeline part from the run.

Parameters

num_batch_steps – number of batches to iterate in epoch
num_epoch_steps – number of epoch to perform in an experiment

Minimal working example (Notebook API):

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
    callbacks=[
        dl.CheckRunCallback(num_batch_steps=3, num_epoch_steps=3)
    ]
)

ControlFlowCallbackWrapper ¶

class catalyst.callbacks.control_flow.ControlFlowCallbackWrapper(base_callback: catalyst.core.callback.Callback, epochs: Union[int, Sequence[int]] = None, ignore_epochs: Union[int, Sequence[int]] = None, loaders: Union[str, Sequence[str], Mapping[str, Union[int, Sequence[int]]]] = None, ignore_loaders: Union[str, Sequence[str], Mapping[str, Union[int, Sequence[int]]]] = None, filter_fn: Union[str, Callable[[int, str], bool]] = None)[source]¶

Bases: catalyst.core.callback.CallbackWrapper

Enable/disable callback execution on different epochs and loaders.

Parameters

base_callback – callback to wrap
epochs –
epochs where need to enable callback, on other epochs callback will be disabled.

If passed int/float then callback will be enabled with period specified as epochs value (epochs expression epoch_number % epochs == 0) and disabled on other epochs.

If passed list of epochs then will be executed callback on specified epochs.

Default value is None.
ignore_epochs: –
epochs where: need to disable callback, on other epochs callback will be enabled.

If passed int/float then callback will be disabled with period specified as epochs value (epochs expression epoch_number % epochs != 0) and enabled on other epochs.

If passed list of epochs then will be disabled callback on specified epochs.

Default value is None.
loaders (str/Sequence[str]/Mapping[str, int/Sequence[str]]) –
loaders where should be enabled callback, on other loaders callback will be disabled.

If passed string object then will be disabled callback for loader with specified name.

If passed list/tuple of strings then will be disabled callback for loaders with specified names.

If passed dictionary where key is a string and values int or list of integers then callback will be disabled on epochs (dictionary value) for specified loader (dictionary key).

Default value is None.
ignore_loaders (str/Sequence[str]/Mapping[str, int/Sequence[str]]) –
loader names where should be disabled callback, on other loaders callback will be enabled.

If passed string object then will be disabled callback for loader with specified name.

If passed list/tuple of strings then will be disabled callback for loaders with specified names.

If passed dictionary where key is a string and values int or list of integers then callback will be disabled on epochs (dictionary value) for specified loader (dictionary key).

Default value is None.
filter_fn (str or Callable[[int, str], bool]) –
function to use instead of loaders or epochs arguments.

If the object passed to a filter_fn is a string then it will be interpreted as python code. Expected lambda function with two arguments: epoch number (int) and loader name (str). This function should return True if callback should be enabled on some condition.

If passed callable object then it should accept two arguments: epoch number (int) and loader name (str). It should return True if callback should be enabled on some condition othervise should return False.

Default value is None.

Examples:
```
# enable callback on all loaders
# exept "train" loader every 2 epochs
ControlFlowCallback(
    ...
    filter_fn=lambda e, l: l != "train" and e % 2 == 0
    ...
)
# or with string equivalent
ControlFlowCallback(
    ...
    filter_fn="lambda e, l: l != 'train' and e % 2 == 0"
    ...
)
```

Note

Please run experiment with check option to check if everything works as expected with this callback.

For example, if you don’t want to compute loss on a validation you can ignore CriterionCallback, for notebook API need to wrap callback:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.dl import (
    SupervisedRunner, AccuracyCallback,
    CriterionCallback, ControlFlowCallback,
)

num_samples, num_features = 10_000, 10
n_classes = 10
X = torch.rand(num_samples, num_features)
y = torch.randint(0, n_classes, [num_samples])
loader = DataLoader(TensorDataset(X, y), batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

model = torch.nn.Linear(num_features, n_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=5,
    verbose=False,
    valid_metric="accuracy03",
    minimize_metric=False,
    callbacks=[
        AccuracyCallback(
            accuracy_args=[1, 3, 5]
        ),
        ControlFlowCallback(
            base_callback=CriterionCallback(),
            ignore_loaders="valid"  # or loaders="train"
        )
    ]
)

CriterionCallback ¶

class catalyst.callbacks.criterion.CriterionCallback(input_key: str, target_key: str, metric_key: str, criterion_key: str = None, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metrics.functional_metric.FunctionalMetricCallback, catalyst.core.callback.ICriterionCallback

Criterion callback, abstraction over criterion step.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
metric_key – key to store computed metric in runner.batch_metrics dictionary
criterion_key – A key to take a criterion in case there are several of them, and they are in a dictionary format.

Note

Please follow the minimal examples sections for more use cases.

EarlyStoppingCallback ¶

class catalyst.callbacks.misc.EarlyStoppingCallback(patience: int, loader_key: str, metric_key: str, minimize: bool, min_delta: float = 1e-06)[source]¶

Bases: catalyst.callbacks.misc.IEpochMetricHandlerCallback

Early stop based on metric.

Parameters

patience – number of epochs with no improvement after which training will be stopped.
loader_key – loader key for early stopping (based on metric score over the dataset)
metric_key – metric key for early stopping (based on metric score over the dataset)
minimize – if True then expected that metric should decrease and early stopping will be performed only when metric stops decreasing. If False then expected that metric should increase. Default value True.
min_delta – minimum change in the monitored metric to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement, default value is 1e-6.

Minimal working example (Notebook API):

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=100,
    callbacks=[
        dl.EarlyStoppingCallback(
            loader_key="valid",
            metric_key="loss",
            minimize=True,
            patience=3,
            min_delta=1e-2
        )
    ]
)

LRFinder ¶

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

Bases: catalyst.callbacks.scheduler.ILRUpdater

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.

MetricAggregationCallback ¶

class catalyst.callbacks.metric_aggregation.MetricAggregationCallback(metric_key: str, metrics: Union[str, List[str], Dict[str, float]] = None, mode: Union[str, Callable] = 'mean', scope: str = 'batch', multiplier: float = 1.0)[source]¶

Bases: catalyst.core.callback.Callback

A callback to aggregate several metrics in one value.

Parameters

metric_key – new key for aggregated metric.
metrics (Union[str, List[str], Dict[str, float]]) – If not None, it aggregates only the values from the metric by these keys. for weighted_sum aggregation it must be a Dict[str, float].
mode – function for aggregation. Must be either sum, mean or weighted_sum or user’s function to aggregate metrics. This function must get dict of metrics and runner and return aggregated metric. It can be useful for complicated fine tuning with different losses that depends on epochs and loader or something also
scope – type of metric. Must be either batch or loader
multiplier – scale factor for the aggregated metric.

Python example - loss is a weighted sum of cross entropy loss and binary cross entropy loss:

import torch
from torch.utils.data import DataLoader, TensorDataset

from catalyst import dl

# data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = {
    "ce": torch.nn.CrossEntropyLoss(),
    "bce": torch.nn.BCEWithLogitsLoss()
}
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# runner
class CustomRunner(dl.Runner):
    def handle_batch(self, batch):
        x, y = batch
        logits = self.model(x)
        num_classes = logits.shape[-1]
        targets_onehot = torch.nn.functional.one_hot(y, num_classes=num_classes)
        self.batch = {
            "features": x,
            "logits": logits,
            "targets": y,
            "targets_onehot": targets_onehot.float(),
        }


# training
runner = CustomRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    num_epochs=3,
    callbacks=[
        dl.AccuracyCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.CriterionCallback(
            input_key="logits",
            target_key="targets",
            metric_key="loss_ce",
            criterion_key="ce",
        ),
        dl.CriterionCallback(
            input_key="logits",
            target_key="targets_onehot",
            metric_key="loss_bce",
            criterion_key="bce",
        ),
        # loss aggregation
        dl.MetricAggregationCallback(
            metric_key="loss",
            metrics={"loss_ce": 0.6, "loss_bce": 0.4},
            mode="weighted_sum",
        ),
        dl.OptimizerCallback(metric_key="loss"),
    ],
)

MixupCallback ¶

class catalyst.callbacks.mixup.MixupCallback(keys: Union[str, List[str]], alpha=0.2, mode='replace', on_train_only=True)[source]¶

Bases: catalyst.core.callback.Callback

Callback to do mixup augmentation. More details about mixin can be found in the paper mixup: Beyond Empirical Risk Minimization: https://arxiv.org/abs/1710.09412 .

Parameters

keys – batch keys to which you want to apply augmentation
alpha – beta distribution a=b parameters. Must be >=0. The more alpha closer to zero the less effect of the mixup.
mode – mode determines the method of use. Must be in [“replace”, “add”]. If “replace” then replaces the batch with a mixed one, while the batch size is not changed. If “add”, concatenates mixed examples to the current ones, the batch size increases by 2 times.
on_train_only – 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.

Examples:

from typing import Any, Dict
import os

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader

from catalyst import dl
from catalyst.callbacks import MixupCallback
from catalyst.contrib.datasets import MNIST


class SimpleNet(nn.Module):
    def __init__(self, in_channels, in_hw, out_features):
        super().__init__()
        self.encoder = nn.Sequential(nn.Conv2d(in_channels,
                                               in_channels, 3, 1, 1), nn.Tanh())
        self.clf = nn.Linear(in_channels * in_hw * in_hw, out_features)

    def forward(self, x):
        features = self.encoder(x)
        features = features.view(features.size(0), -1)
        logits = self.clf(features)
        return logits


class SimpleDataset(torch.utils.data.Dataset):
    def __init__(self, train: bool = False):
        self.mnist = MNIST(os.getcwd(), train=train)

    def __len__(self) -> int:
        return len(self.mnist)

    def __getitem__(self, idx: int) -> Dict[str, Any]:
        x, y = self.mnist.__getitem__(idx)
        y_one_hot = np.zeros(10)
        y_one_hot[y] = 1
        return {"image": x,
                "clf_targets": y,
                "clf_targets_one_hot": torch.Tensor(y_one_hot)}


model = SimpleNet(1, 28, 10)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(SimpleDataset(train=True), batch_size=32),
    "valid": DataLoader(SimpleDataset(train=False), batch_size=32),
}


class CustomRunner(dl.Runner):
    def handle_batch(self, batch):
        image = batch["image"]
        clf_logits = self.model(image)
        self.batch["clf_logits"] = clf_logits


runner = CustomRunner()
runner.train(
    loaders=loaders,
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    logdir="./logdir14",
    num_epochs=2,
    verbose=True,
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    callbacks={
        "mixup": MixupCallback(keys=["image", "clf_targets_one_hot"]),
        "criterion": dl.CriterionCallback(
            metric_key="loss",
            input_key="clf_logits",
            target_key="clf_targets_one_hot"
        ),
        "backward": dl.BackwardCallback(metric_key="loss"),
        "optimizer": dl.OptimizerCallback(metric_key="loss"),
        "classification": dl.ControlFlowCallback(
            dl.PrecisionRecallF1SupportCallback(
                input_key="clf_logits", target_key="clf_targets", num_classes=10
            ),
            ignore_loaders="train",
        ),
    },
)

OptimizerCallback ¶

class catalyst.callbacks.optimizer.OptimizerCallback(metric_key: str, optimizer_key: str = None, accumulation_steps: int = 1, grad_clip_fn: Union[str, Callable] = None, grad_clip_params: Dict = None)[source]¶

Bases: catalyst.core.callback.IOptimizerCallback

Optimizer callback, abstraction over optimizer step.

Parameters

metric_key – a key to get loss from runner.batch_metrics
model_key – a key to select a model from runner.model in case there are several of them and they are in a dictionary format.
optimizer_key – a key to select a optimizer from runner.optimizer in case there are several of them and they are in a dictionary format.
accumulation_steps – number of steps before optimizer.step()
grad_clip_fn – callable gradient cliping function or it’s name or
grad_clip_params – key-value parameters for grad_clip_fn

Note

Please follow the minimal examples sections for more use cases.

OptunaPruningCallback ¶

class catalyst.callbacks.optuna.OptunaPruningCallback(trial: optuna.trial._trial.Trial, loader_key: str, metric_key: str, minimize: bool, min_delta: float = 1e-06)[source]¶

Bases: catalyst.core.callback.Callback

Optuna callback for pruning unpromising runs. This callback can be used for early stopping (pruning) unpromising runs.

Parameters

trial – Optuna.Trial for the experiment.
loader_key – loader key for best model selection (based on metric score over the dataset)
metric_key – metric key for best model selection (based on metric score over the dataset)
minimize – boolean flag to minimize the required metric
min_delta – minimal delta for metric improve

import optuna

from catalyst.dl import SupervisedRunner, OptunaPruningCallback

# some python code ...

def objective(trial: optuna.Trial):
    # standard optuna code for model and/or optimizer suggestion ...
    runner = SupervisedRunner()
    runner.train(
        model=model,
        loaders=loaders,
        criterion=criterion,
        optimizer=optimizer,
        callbacks=[
            OptunaPruningCallback(trial)
            # some other callbacks ...
        ],
        num_epochs=num_epochs,
    )
    return runner.best_valid_metrics[runner.valid_metric]

study = optuna.create_study()
study.optimize(objective, n_trials=100, timeout=600)

PeriodicLoaderCallback ¶

class catalyst.callbacks.periodic_loader.PeriodicLoaderCallback(valid_loader_key: str, valid_metric_key: str, minimize: bool, **kwargs)[source]¶

Bases: catalyst.core.callback.Callback

Callback for runing loaders with specified period. To disable loader use 0 as period (if specified 0 for validation loader then will be raised an error).

Parameters: kwargs – loader names and their run periods.

For example, if you have train, train_additional, valid and valid_additional loaders and wan’t to use train_additional every 2 epochs, valid - every 3 epochs and valid_additional - every 5 epochs:

from catalyst.dl import SupervisedRunner, PeriodicLoaderCallback
runner = SupervisedRunner()
runner.train(
    ...
    loaders={
        "train": ...,
        "train_additional": ...,
        "valid": ...,
        "valid_additional":...
    }
    ...
    callbacks=[
        ...
        PeriodicLoaderCallback(
            train_additional=2,
            valid=3,
            valid_additional=5
        ),
        ...
    ]
    ...
)

ProfilerCallback ¶

class catalyst.callbacks.profiler.ProfilerCallback(loader_key: str = None, epoch: int = 1, num_batches: int = None, profiler_kwargs: Dict[str, Any] = None, tensorboard_path: str = None, export_chrome_trace_path: str = None, export_stacks_kwargs: Dict[str, Any] = None)[source]¶

Bases: catalyst.core.callback.Callback

Profile specified epoch or some fixed number of batches.

Parameters

loader_key – name of the loader to use for profiling. If None then will be used first loader from experiment.
epoch – epoch number to use for profiling.
num_batches – number of batches to use in epoch to do a profiling. If None then will be used all batches in loader.
profiler_kwargs – arguments to pass to a profiler. To get more info about possible arguments please use PyTorch profiler docs.
tensorboard_path – path where should be stored logs for tensorboard. If None then will be ignored.
export_chrome_trace_path – path to export chrome trace. If None then will be ignored exporting chrome trace to a file.
export_stacks_kwargs – arguments to pass to a profiler.export_stacks method. If None then triggering profiler.export_stacks will be avoided.

Example of using FlameGraph tool for profiler.export_stacks:

git clone https://github.com/brendangregg/FlameGraph
cd FlameGraph
./flamegraph.pl –title “CPU time” –countname “us.” profiler.stacks > perf_viz.svg

Note

Export to tensorboard and chrome trace mutually exclusive and specifying both of them will raise an error.

Example

import os

import torch
from torch import nn
from torch.utils.data import DataLoader

from catalyst import dl
from catalyst.data import ToTensor
from catalyst.contrib.datasets import MNIST

loaders = {
    "train": DataLoader(MNIST(os.getcwd(), train=False), batch_size=32),
    "valid": DataLoader(MNIST(os.getcwd(), train=False), batch_size=32),
}

model = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    callbacks=[dl.ProfilerCallback(
        loader_key="train", epoch=3,
        profiler_kwargs=dict(
            activities=[
                torch.profiler.ProfilerActivity.CPU,
                torch.profiler.ProfilerActivity.CUDA,
            ],
            on_trace_ready=torch.profiler.tensorboard_trace_handler(
                "./logs/tb_profile"
            ),
            with_stack=True,
            with_flops=True,
        )
    )],
    loaders=loaders,
    criterion=criterion,
    optimizer=optimizer,
    num_epochs=5,
    logdir="./logs",
)

SchedulerCallback ¶

class catalyst.callbacks.scheduler.SchedulerCallback(scheduler_key: str = None, mode: str = None, loader_key: str = None, metric_key: str = None)[source]¶

Bases: catalyst.core.callback.ISchedulerCallback

Scheduler callback, abstraction over scheduler step.

Parameters

scheduler_key – scheduler name, if None, default is None.
mode – scheduler mode, should be one of "epoch" or "batch", default is None. If None and object is instance of BatchScheduler or OneCycleLRWithWarmup then will be used "batch" otherwise - "epoch".
loader_key – loader name to look after for ReduceLROnPlateau scheduler
metric_key – metric name to forward to scheduler object, if None then will be used main metric specified in experiment.

Note

Please follow the minimal examples sections for more use cases.

TimerCallback ¶

class catalyst.callbacks.misc.TimerCallback[source]¶

Bases: catalyst.core.callback.Callback

Logs pipeline execution time.

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=1,
    verbose=True,
    callbacks=[dl.TimerCallback()]
)

You should see additional extra metrics, such as:

_timer/_fps - number handled samples per second during run.
_timer/batch_time - time required for single batch handling.
_timer/data_time - time required for single batch data preparation handling.
_timer/model_time - time required for single batch model forwarding.

Moreover, you could use it throught timeit=True flag:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=1,
    verbose=True,
    timeit=True,
)

TqdmCallback ¶

class catalyst.callbacks.misc.TqdmCallback[source]¶

Bases: catalyst.core.callback.Callback

Logs the params into tqdm console.

Minimal working example (Notebook API):

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=1,
    callbacks=[dl.TqdmCallback()]
)

You should see a tqdm progress bar during the training.

Moreover, you could use it throught verbose=True flag:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=1,
    verbose=True,
)

on_exception(runner: catalyst.core.runner.IRunner)[source]¶: Called if an Exception was raised.

Metric-based Interfaces ¶

BatchMetricCallback ¶

class catalyst.callbacks.metric.BatchMetricCallback(metric: catalyst.metrics._metric.ICallbackBatchMetric, input_key: Union[str, Iterable[str], Dict[str, str]], target_key: Union[str, Iterable[str], Dict[str, str]], log_on_batch: bool = True)[source]¶

Bases: catalyst.callbacks.metric._MetricCallback

BatchMetricCallback implements batch-based metrics update and computation over loader

Parameters

metric – metric to calculate in callback
input_key – keys of tensors that should be used as inputs in metric calculation
target_key – keys of tensors that should be used as targets in metric calculation
log_on_batch – boolean flag to log computed metrics every batch

LoaderMetricCallback ¶

class catalyst.callbacks.metric.LoaderMetricCallback(metric: catalyst.metrics._metric.ICallbackLoaderMetric, input_key: Union[str, Iterable[str], Dict[str, str]], target_key: Union[str, Iterable[str], Dict[str, str]])[source]¶

Bases: catalyst.callbacks.metric._MetricCallback

LoaderMetricCallback implements loader-based metrics update and computation over loader

Parameters

metric – metric to calculate in callback
input_key – keys of tensors that should be used as inputs in metric calculation
target_key – keys of tensors that should be used as targets in metric calculation

Metric-based ¶

AccuracyCallback ¶

class catalyst.callbacks.metrics.accuracy.AccuracyCallback(input_key: str, target_key: str, topk: Iterable[int] = None, num_classes: int = None, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Accuracy metric callback. Computes multiclass accuracy@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
topk – specifies which accuracy@K to log
num_classes – number of classes to calculate topk if accuracy_args is None
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy03",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.AccuracyCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
    ],
)

Note

Metric names depending on input parameters:

topk = None —> see catalyst.metrics.functional._misc.get_default_topk
topk = (1,) —> "accuracy01"
topk = (1, 3) —> "accuracy01", "accuracy03"
topk = (1, 3, 5) —> "accuracy01", "accuracy03", "accuracy05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

AUCCallback ¶

class catalyst.callbacks.metrics.auc.AUCCallback(input_key: str, target_key: str, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.LoaderMetricCallback

ROC-AUC metric callback.

Parameters

input_key – input key to use for auc calculation, specifies our y_true.
target_key – output key to use for auc calculation, specifies our y_pred.
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy03",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.AccuracyCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
    ],
)

Note

Please follow the minimal examples sections for more use cases.

CMCScoreCallback ¶

class catalyst.callbacks.metrics.cmc_score.CMCScoreCallback(embeddings_key: str, labels_key: str, is_query_key: str, topk: Iterable[int] = None, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.LoaderMetricCallback

Cumulative Matching Characteristics callback.

This callback was designed to count cumulative matching characteristics. If current object is from query your dataset should output True in is_query_key and false if current object is from gallery. You can see QueryGalleryDataset in catalyst.contrib.datasets.metric_learning for more information. On batch end callback accumulate all embeddings

Parameters

embeddings_key – embeddings key in output dict
labels_key – labels key in output dict
is_query_key – bool key True if current object is from query
topk – specifies which cmc@K to log
prefix – metric prefix
suffix – metric suffix

Note

You should use it with ControlFlowCallback and add all query/gallery sets to loaders. Loaders should contain “is_query” and “label” key.

Examples:

import os
from torch.optim import Adam
from torch.utils.data import DataLoader
from catalyst import data, dl
from catalyst.contrib import data, datasets, models, nn


# 1. train and valid loaders
transforms = data.Compose([
    data.ImageToTensor(), data.NormalizeImage((0.1307,), (0.3081,))
])

train_dataset = datasets.MnistMLDataset(
    root=os.getcwd(), download=True, transform=transforms
    )
sampler = data.BatchBalanceClassSampler(
    labels=train_dataset.get_labels(), num_classes=5, num_samples=10
)
train_loader = DataLoader(dataset=train_dataset, batch_sampler=sampler)

valid_dataset = datasets.MnistQGDataset(
    root=os.getcwd(), transform=transforms, gallery_fraq=0.2
)
valid_loader = DataLoader(dataset=valid_dataset, batch_size=1024)

# 2. model and optimizer
model = models.MnistSimpleNet(out_features=16)
optimizer = Adam(model.parameters(), lr=0.001)

# 3. criterion with triplets sampling
sampler_inbatch = data.HardTripletsSampler(norm_required=False)
criterion = nn.TripletMarginLossWithSampler(
    margin=0.5, sampler_inbatch=sampler_inbatch
)

# 4. training with catalyst Runner
class CustomRunner(dl.SupervisedRunner):
    def handle_batch(self, batch) -> None:
        if self.is_train_loader:
            images, targets = batch["features"].float(), batch["targets"].long()
            features = self.model(images)
            self.batch = {"embeddings": features, "targets": targets}
        else:
            images, targets, is_query = (
                batch["features"].float(),
                batch["targets"].long(),
                batch["is_query"].bool()
            )
            features = self.model(images)
            self.batch = {
                "embeddings": features, "targets": targets, "is_query": is_query
            }

callbacks = [
    dl.ControlFlowCallback(
        dl.CriterionCallback(
            input_key="embeddings", target_key="targets", metric_key="loss"
        ),
        loaders="train",
    ),
    dl.ControlFlowCallback(
        dl.CMCScoreCallback(
            embeddings_key="embeddings",
            labels_key="targets",
            is_query_key="is_query",
            topk=[1],
        ),
        loaders="valid",
    ),
    dl.PeriodicLoaderCallback(
        valid_loader_key="valid",
        valid_metric_key="cmc01",
        minimize=False,
        valid=2
    ),
]

runner = CustomRunner(input_key="features", output_key="embeddings")
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    callbacks=callbacks,
    loaders={"train": train_loader, "valid": valid_loader},
    verbose=False,
    logdir="./logs",
    valid_loader="valid",
    valid_metric="cmc01",
    minimize_valid_metric=False,
    num_epochs=10,
)

Note

Metric names depending on input parameters:

topk = (1,) or None —> "cmc01"
topk = (1, 3) —> "cmc01", "cmc03"
topk = (1, 3, 5) —> "cmc01", "cmc03", "cmc05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

ConfusionMatrixCallback ¶

class catalyst.callbacks.metrics.confusion_matrix.ConfusionMatrixCallback(input_key: str, target_key: str, prefix: str = None, class_names: List[str] = None, num_classes: int = None, normalize: bool = False, plot_params: Dict = None)[source]¶

Bases: catalyst.core.callback.Callback

Callback to plot your confusion matrix to the loggers.

Parameters

input_key – key to use from runner.batch, specifies our y_pred
target_key – key to use from runner.batch, specifies our y_true
prefix – plot name for monitoring tools
class_names – list with class names
num_classes – number of classes
normalize – boolean flag for confusion matrix normalization
plot_params – extra params for plt.figure rendering

Note

catalyst[ml] required for this callback

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy03",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.AccuracyCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
        dl.ConfusionMatrixCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
    ],
)

Note

Please follow the minimal examples sections for more use cases.

DiceCallback ¶

class catalyst.callbacks.metrics.segmentation.DiceCallback(input_key: str, target_key: str, class_dim: int = 1, weights: Optional[List[float]] = None, class_names: Optional[List[str]] = None, threshold: Optional[float] = None, log_on_batch: bool = True, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Dice metric callback.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
class_dim – indicates class dimension (K) for outputs and targets tensors (default = 1)
weights – class weights
class_names – class names
threshold – threshold for outputs binarization
log_on_batch – boolean flag to log computed metrics every batch
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data import ToTensor
from catalyst.contrib import MNIST, IoULoss


model = nn.Sequential(
    nn.Conv2d(1, 1, 3, 1, 1), nn.ReLU(),
    nn.Conv2d(1, 1, 3, 1, 1), nn.Sigmoid(),
)
criterion = IoULoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()),
        batch_size=32
    ),
    "valid": DataLoader(
        MNIST(os.getcwd(), train=False),
        batch_size=32
    ),
}

class CustomRunner(dl.SupervisedRunner):
    def handle_batch(self, batch):
        x = batch[self._input_key]
        x_noise = (x + torch.rand_like(x)).clamp_(0, 1)
        x_ = self.model(x_noise)
        self.batch = {
            self._input_key: x, self._output_key: x_, self._target_key: x
        }

runner = CustomRunner(
    input_key="features",
    output_key="scores",
    target_key="targets",
    loss_key="loss"
)
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.IOUCallback(input_key="scores", target_key="targets"),
        dl.DiceCallback(input_key="scores", target_key="targets"),
        dl.TrevskyCallback(input_key="scores", target_key="targets", alpha=0.2),
    ],
    logdir="./logdir",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
)

Note

Please follow the minimal examples sections for more use cases.

FunctionalMetricCallback ¶

class catalyst.callbacks.metrics.functional_metric.FunctionalMetricCallback(input_key: Union[str, Iterable[str], Dict[str, str]], target_key: Union[str, Iterable[str], Dict[str, str]], metric_fn: Callable, metric_key: str, compute_on_call: bool = True, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.FunctionalBatchMetricCallback

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
metric_fn – metric function, that get outputs, targets and return score as torch.Tensor
metric_key – key to store computed metric in runner.batch_metrics dictionary
compute_on_call – Computes and returns metric value during metric call. Used for per-batch logging. default: True
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

HitrateCallback ¶

class catalyst.callbacks.metrics.recsys.HitrateCallback(input_key: str, target_key: str, topk: Iterable[int] = None, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Hitrate metric callback. Computes HR@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
topk – specifies which HR@K to log
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            transform=torch.sigmoid,
            scope="on_batch_end",
            input_key="logits",
            output_key="scores"
        ),
        dl.CriterionCallback(
            input_key="logits", target_key="targets", metric_key="loss"
        ),
        dl.AUCCallback(input_key="scores", target_key="targets"),
        dl.HitrateCallback(
            input_key="scores", target_key="targets", topk=(1, 3, 5)
        ),
        dl.MRRCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.MAPCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.NDCGCallback(input_key="scores", target_key="targets", topk=(1, 3)),
        dl.OptimizerCallback(metric_key="loss"),
        dl.SchedulerCallback(),
        dl.CheckpointCallback(
            logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
        ),
    ]
)

Note

Metric names depending on input parameters:

topk = (1,) or None —> "hitrate01"
topk = (1, 3) —> "hitrate01", "hitrate03"
topk = (1, 3, 5) —> "hitrate01", "hitrate03", "hitrate05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

IOUCallback ¶

class catalyst.callbacks.metrics.segmentation.IOUCallback(input_key: str, target_key: str, class_dim: int = 1, weights: Optional[List[float]] = None, class_names: Optional[List[str]] = None, threshold: Optional[float] = None, log_on_batch: bool = True, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

IOU metric callback.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
class_dim – indicates class dimension (K) for outputs and targets tensors (default = 1)
weights – class weights
class_names – class names
threshold – threshold for outputs binarization
log_on_batch – boolean flag to log computed metrics every batch
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.data import ToTensor
from catalyst.contrib import MNIST, IoULoss


model = nn.Sequential(
    nn.Conv2d(1, 1, 3, 1, 1), nn.ReLU(),
    nn.Conv2d(1, 1, 3, 1, 1), nn.Sigmoid(),
)
criterion = IoULoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()),
        batch_size=32
    ),
    "valid": DataLoader(
        MNIST(os.getcwd(), train=False),
        batch_size=32
    ),
}

class CustomRunner(dl.SupervisedRunner):
    def handle_batch(self, batch):
        x = batch[self._input_key]
        x_noise = (x + torch.rand_like(x)).clamp_(0, 1)
        x_ = self.model(x_noise)
        self.batch = {
            self._input_key: x, self._output_key: x_, self._target_key: x
        }

runner = CustomRunner(
    input_key="features",
    output_key="scores",
    target_key="targets",
    loss_key="loss"
)
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.IOUCallback(input_key="scores", target_key="targets"),
        dl.DiceCallback(input_key="scores", target_key="targets"),
        dl.TrevskyCallback(input_key="scores", target_key="targets", alpha=0.2),
    ],
    logdir="./logdir",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
)

Note

Please follow the minimal examples sections for more use cases.

MAPCallback ¶

class catalyst.callbacks.metrics.recsys.MAPCallback(input_key: str, target_key: str, topk: Iterable[int] = None, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

MAP metric callback. Computes MAP@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
prefix – key for the metric’s name
topk – specifies which MAP@K to log
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            transform=torch.sigmoid,
            scope="on_batch_end",
            input_key="logits",
            output_key="scores"
        ),
        dl.CriterionCallback(
            input_key="logits", target_key="targets", metric_key="loss"
        ),
        dl.AUCCallback(input_key="scores", target_key="targets"),
        dl.HitrateCallback(
            input_key="scores", target_key="targets", topk=(1, 3, 5)
        ),
        dl.MRRCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.MAPCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.NDCGCallback(input_key="scores", target_key="targets", topk=(1, 3)),
        dl.OptimizerCallback(metric_key="loss"),
        dl.SchedulerCallback(),
        dl.CheckpointCallback(
            logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
        ),
    ]
)

Note

Metric names depending on input parameters:

topk = (1,) or None —> "map01"
topk = (1, 3) —> "map01", "map03"
topk = (1, 3, 5) —> "map01", "map03", "map05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

MultilabelAccuracyCallback ¶

class catalyst.callbacks.metrics.accuracy.MultilabelAccuracyCallback(input_key: str, target_key: str, threshold: Union[float, torch.Tensor] = 0.5, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Multilabel accuracy metric callback. Computes multilabel accuracy@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
threshold – thresholds for model scores
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.AUCCallback(input_key="logits", target_key="targets"),
        dl.MultilabelAccuracyCallback(
            input_key="logits", target_key="targets", threshold=0.5
        )
    ]

Note

Please follow the minimal examples sections for more use cases.

MultilabelPrecisionRecallF1SupportCallback ¶

class catalyst.callbacks.metrics.classification.MultilabelPrecisionRecallF1SupportCallback(input_key: str, target_key: str, num_classes: Optional[int] = None, zero_division: int = 0, log_on_batch: bool = True, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Multilabel PrecisionRecallF1Support metric callback.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
num_classes – number of classes
zero_division – value to set in case of zero division during metrics (precision, recall) computation; should be one of 0 or 1
log_on_batch – boolean flag to log computed metrics every batch
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            transform=torch.sigmoid,
            scope="on_batch_end",
            input_key="logits",
            output_key="scores"
        ),
        dl.AUCCallback(input_key="scores", target_key="targets"),
        dl.MultilabelAccuracyCallback(
            input_key="scores", target_key="targets", threshold=0.5
        ),
        dl.MultilabelPrecisionRecallF1SupportCallback(
            input_key="scores", target_key="targets", num_classes=num_classes
        ),
    ]
)

Note

Please follow the minimal examples sections for more use cases.

MRRCallback ¶

class catalyst.callbacks.metrics.recsys.MRRCallback(input_key: str, target_key: str, topk: Iterable[int] = None, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

MRR metric callback. Computes MRR@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
prefix – key for the metric’s name
topk – specifies which MRR@K to log
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            transform=torch.sigmoid,
            scope="on_batch_end",
            input_key="logits",
            output_key="scores"
        ),
        dl.CriterionCallback(
            input_key="logits", target_key="targets", metric_key="loss"
        ),
        dl.AUCCallback(input_key="scores", target_key="targets"),
        dl.HitrateCallback(
            input_key="scores", target_key="targets", topk=(1, 3, 5)
        ),
        dl.MRRCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.MAPCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.NDCGCallback(input_key="scores", target_key="targets", topk=(1, 3)),
        dl.OptimizerCallback(metric_key="loss"),
        dl.SchedulerCallback(),
        dl.CheckpointCallback(
            logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
        ),
    ]
)

Note

Metric names depending on input parameters:

topk = (1,) or None —> "mrr01"
topk = (1, 3) —> "mrr01", "mrr03"
topk = (1, 3, 5) —> "mrr01", "mrr03", "mrr05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

NDCGCallback ¶

class catalyst.callbacks.metrics.recsys.NDCGCallback(input_key: str, target_key: str, topk: Iterable[int] = None, log_on_batch: bool = True, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

NDCG metric callback. Computes NDCG@topk for the specified values of topk.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
prefix – key for the metric’s name
topk – specifies which NDCG@K to log
log_on_batch – boolean flag to log computed metrics every batch
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            transform=torch.sigmoid,
            scope="on_batch_end",
            input_key="logits",
            output_key="scores"
        ),
        dl.CriterionCallback(
            input_key="logits", target_key="targets", metric_key="loss"
        ),
        dl.AUCCallback(input_key="scores", target_key="targets"),
        dl.HitrateCallback(
            input_key="scores", target_key="targets", topk=(1, 3, 5)
        ),
        dl.MRRCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.MAPCallback(input_key="scores", target_key="targets", topk=(1, 3, 5)),
        dl.NDCGCallback(input_key="scores", target_key="targets", topk=(1, 3)),
        dl.OptimizerCallback(metric_key="loss"),
        dl.SchedulerCallback(),
        dl.CheckpointCallback(
            logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
        ),
    ]
)

Note

Metric names depending on input parameters:

topk = (1,) or None —> "ndcg01"
topk = (1, 3) —> "ndcg01", "ndcg03"
topk = (1, 3, 5) —> "ndcg01", "ndcg03", "ndcg05"

You can find them in runner.batch_metrics, runner.loader_metrics or runner.epoch_metrics.

Note

Please follow the minimal examples sections for more use cases.

PrecisionRecallF1SupportCallback ¶

class catalyst.callbacks.metrics.classification.PrecisionRecallF1SupportCallback(input_key: str, target_key: str, num_classes: Optional[int] = None, zero_division: int = 0, log_on_batch: bool = True, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Multiclass PrecisionRecallF1Support metric callback.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
num_classes – number of classes
zero_division – value to set in case of zero division during metrics (precision, recall) computation; should be one of 0 or 1
log_on_batch – boolean flag to log computed metrics every batch
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples,) * num_classes).to(torch.int64)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=3,
    valid_loader="valid",
    valid_metric="accuracy03",
    minimize_valid_metric=False,
    verbose=True,
    callbacks=[
        dl.AccuracyCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=num_classes
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
    ],
)

Note

Please follow the minimal examples sections for more use cases.

R2SquaredCallback ¶

class catalyst.callbacks.metrics.r2_squared.R2SquaredCallback(input_key: str, target_key: str, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.LoaderMetricCallback

R2 Squared metric callback.

Parameters

input_key – input key to use for r2squared calculation, specifies our y_true
target_key – output key to use for r2squared calculation, specifies our y_pred
prefix – metric prefix
suffix – metric suffix

Examples:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = dl.SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    num_epochs=8,
    verbose=True,
    callbacks=[
        dl.R2SquaredCallback(input_key="logits", target_key="targets")
    ]
)

Note

Please follow the minimal examples sections for more use cases.

ReidCMCScoreCallback ¶

class catalyst.callbacks.metrics.cmc_score.ReidCMCScoreCallback(embeddings_key: str, pids_key: str, cids_key: str, is_query_key: str, topk: Iterable[int] = None, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.LoaderMetricCallback

Cumulative Matching Characteristics callback for reID case. More information about cmc-based callbacks in CMCScoreCallback’s docs.

Parameters

embeddings_key – embeddings key in output dict
pids_key – pids key in output dict
cids_key – cids key in output dict
is_query_key – bool key True if current object is from query
topk – specifies which cmc@K to log. [1] - cmc@1 [1, 3] - cmc@1 and cmc@3 [1, 3, 5] - cmc@1, cmc@3 and cmc@5
prefix – metric prefix
suffix – metric suffix

SklearnBatchCallback ¶

class catalyst.callbacks.metrics.scikit_learn.SklearnBatchCallback(keys: Mapping[str, Any], metric_fn: Union[Callable, str], metric_key: str, log_on_batch: bool = True, **metric_kwargs)[source]¶

Bases: catalyst.callbacks.metric.FunctionalBatchMetricCallback

SklearnBatchCallback implements an integration of batch-based Sklearn metrics

Parameters

keys – a dictionary containing: a mapping between metric_fn arguments and keys in runner.batch other arguments needed for metric_fn
metric_fn – metric function that gets outputs, targets, and other arguments given in keys and returns score
metric_key – key to store computed metric in runner.batch_metrics dictionary
log_on_batch – boolean flag to log computed metrics every batch
metric_kwargs – additional parameters for metric_fn

Note

catalyst[ml] required for this callback

Examples:

import sklearn
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
from functools import partial

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            input_key="targets",
            output_key="labels",
            transform=partial(torch.argmax, dim=1),
            scope="on_batch_end",
        ),
        dl.BatchTransformCallback(
            input_key="logits",
            output_key="scores",
            transform=partial(torch.softmax, dim=1),
            scope="on_batch_end",
        ),
        dl.BatchTransformCallback(
            input_key="scores",
            output_key="preds",
            transform=partial(torch.argmax, dim=1),
            scope="on_batch_end",
        ),
        dl.MultilabelAccuracyCallback(
            input_key="logits", target_key="targets", threshold=0.5
        ),
        dl.SklearnBatchCallback(
            keys={"y_pred": "preds", "y_true": "labels"},
            metric_fn="f1_score",
            metric_key="sk_f1",
            average="macro",
            zero_division=1,
        )
    ]
)

Note

Please follow the minimal examples sections for more use cases.

SklearnLoaderCallback ¶

class catalyst.callbacks.metrics.scikit_learn.SklearnLoaderCallback(keys: Mapping[str, Any], metric_fn: Union[Callable, str], metric_key: str, **metric_kwargs)[source]¶

Bases: catalyst.callbacks.metric.LoaderMetricCallback

SklearnLoaderCallback implements an integration of loader-based Sklearn metrics

Parameters

keys – a mapping between metric_fn arguments and keys in runner.batch
metric_fn – metric function that gets outputs, targets, and other arguments given in keys and returns score
metric_key – key to store computed metric in runner.batch_metrics dictionary
metric_kwargs – additional parameters for metric_fn

Note

catalyst[ml] required for this callback

Examples:

import sklearn
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
from functools import partial

# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)

# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])

# model training
runner = dl.SupervisedRunner(
    input_key="features",
    output_key="logits",
    target_key="targets",
    loss_key="loss"
)
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    num_epochs=3,
    verbose=True,
    callbacks=[
        dl.BatchTransformCallback(
            input_key="targets",
            output_key="labels",
            transform=partial(torch.argmax, dim=1),
            scope="on_batch_end",
        ),
        dl.BatchTransformCallback(
            input_key="logits",
            output_key="scores",
            transform=partial(torch.softmax, dim=1),
            scope="on_batch_end",
        ),
        dl.BatchTransformCallback(
            input_key="scores",
            output_key="preds",
            transform=partial(torch.argmax, dim=1),
            scope="on_batch_end",
        ),
        dl.MultilabelAccuracyCallback(
            input_key="logits", target_key="targets", threshold=0.5
        ),
        dl.SklearnLoaderCallback(
            keys={"y_score": "scores", "y_true": "labels"},
            metric_fn="roc_auc_score",
            metric_key="roc_auc_score",
            average="macro",
            multi_class="ovo"
        )
    ]
)

Note

Please follow the minimal examples sections for more use cases.

SklearnModelCallback ¶

class catalyst.callbacks.sklearn_model.SklearnModelCallback(feature_key: str, target_key: Optional[str], train_loader: str, valid_loaders: Union[str, List[str]], model_fn: Union[Callable, str], predict_method: str = 'predict', predict_key: str = 'sklearn_predict', **model_kwargs)[source]¶

Bases: catalyst.core.callback.Callback

Callback to train a classifier on the train loader and to give predictions on the valid loader.

Parameters

feature_key – keys of tensors that should be used as features for the classifier fit
target_key – keys of tensors that should be used as targets for the classifier fit
train_loader – train loader name
valid_loaders – valid loaders where model should be predicted
model_fn – fabric to produce objects with .fit and predict method
predict_method – predict method name for the classifier
predict_key – key to store computed classifier predicts in runner.batch
model_kwargs – additional parameters for model_fn

Note

catalyst[ml] required for this callback

TrevskyCallback ¶

class catalyst.callbacks.metrics.segmentation.TrevskyCallback(input_key: str, target_key: str, alpha: float, beta: Optional[float] = None, class_dim: int = 1, weights: Optional[List[float]] = None, class_names: Optional[List[str]] = None, threshold: Optional[float] = None, log_on_batch: bool = True, compute_per_class_metrics: bool = False, prefix: str = None, suffix: str = None)[source]¶

Bases: catalyst.callbacks.metric.BatchMetricCallback

Trevsky metric callback.

Parameters

input_key – input key to use for metric calculation, specifies our y_pred
target_key – output key to use for metric calculation, specifies our y_true
alpha – false negative coefficient, bigger alpha bigger penalty for false negative. if beta is None, alpha must be in (0, 1)
beta – false positive coefficient, bigger alpha bigger penalty for false positive. Must be in (0, 1), if None beta = (1 - alpha)
class_dim – indicates class dimension (K) for outputs and targets tensors (default = 1)
weights – class weights
class_names – class names
threshold – threshold for outputs binarization
log_on_batch – boolean flag to log computed metrics every batch
compute_per_class_metrics – boolean flag to compute per-class metrics (default: SETTINGS.compute_per_class_metrics or False).
prefix – metric prefix
suffix – metric suffix

Examples:

import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from catalyst import dl
from catalyst.contrib import MNIST, IoULoss

model = nn.Sequential(
    nn.Conv2d(1, 1, 3, 1, 1), nn.ReLU(),
    nn.Conv2d(1, 1, 3, 1, 1), nn.Sigmoid(),
)
criterion = IoULoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MNIST(os.getcwd(), train=True),
        batch_size=32
    ),
    "valid": DataLoader(
        MNIST(os.getcwd(), train=False),
        batch_size=32
    ),
}

class CustomRunner(dl.SupervisedRunner):
    def handle_batch(self, batch):
        x = batch[self._input_key]
        x_noise = (x + torch.rand_like(x)).clamp_(0, 1)
        x_ = self.model(x_noise)
        self.batch = {
            self._input_key: x, self._output_key: x_, self._target_key: x
        }

runner = CustomRunner(
    input_key="features",
    output_key="scores",
    target_key="targets",
    loss_key="loss"
)
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.IOUCallback(input_key="scores", target_key="targets"),
        dl.DiceCallback(input_key="scores", target_key="targets"),
        dl.TrevskyCallback(input_key="scores", target_key="targets", alpha=0.2),
    ],
    logdir="./logdir",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
)

Note

Please follow the minimal examples sections for more use cases.