ride.core

Module Contents

Classes

Configs	Configs module for holding project configurations.
RideModule	Base-class for modules using the Ride ecosystem.
RideMixin	Abstract base-class for Ride mixins
DefaultMethods	Abstract base-class for Ride mixins
OptimizerMixin	Abstract base-class for Optimizer mixins
RideDataset	Base-class for Ride datasets.
RideClassificationDataset	Base-class for Ride classification datasets.

Functions

_init_subclass(cls)
apply_init_args(fn, self, hparams, *args, **kwargs)

Attributes

logger
DataShape

ride.core.logger

ride.core.DataShape

class ride.core.Configs

Bases: corider.Configs

Configs module for holding project configurations.

This is a wrapper of the Configs found as a stand-alone package in https://github.com/LukasHedegaard/co-rider

static collect(cls: RideModule) → Configs

Collect the configs from all class bases

Returns:

Aggregated configurations

Return type:

Configs

default_values()

ride.core._init_subclass(cls)

ride.core.apply_init_args(fn, self, hparams, *args, **kwargs)

class ride.core.RideModule

Base-class for modules using the Ride ecosystem.

This module should be inherited as the highest-priority parent (first in sequence).

Example:

class MyModule(ride.RideModule, ride.SgdOneCycleOptimizer):
    def __init__(self, hparams):
        ...

It handles proper initialisation of RideMixin parents and adds automatic attribute validation.

If pytorch_lightning.LightningModule is omitted as lowest-priority parent, RideModule will automatically add it.

If training_step, validation_step, and test_step methods are not found, the ride.Lifecycle will be automatically mixed in by this module.

property hparams: pytorch_lightning.utilities.parsing.AttributeDict

classmethod __init_subclass__()

classmethod with_dataset(ds: RideDataset)

class ride.core.RideMixin(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: abc.ABC

Abstract base-class for Ride mixins

on_init_end(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

validate_attributes()

class ride.core.DefaultMethods(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: RideMixin

Abstract base-class for Ride mixins

warm_up(input_shape: Sequence[int], *args, **kwargs)

Warms up the model state with a dummy input of shape input_shape. This method is called prior to model profiling.

Parameters:

input_shape (Sequence[int]) – input shape with which to warm the model up, including batch size.

class ride.core.OptimizerMixin(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: RideMixin

Abstract base-class for Optimizer mixins

class ride.core.RideDataset(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: RideMixin

Base-class for Ride datasets.

If no dataset is specified otherwise, this mixin is automatically add as a base of RideModule childen.

User-specified datasets must inherit from this class, and specify the following: - self.input_shape: Union[int, Sequence[int], Sequence[Sequence[int]]] - self.output_shape: Union[int, Sequence[int], Sequence[Sequence[int]]]

and either the functions: - train_dataloader: Callable[[Any], DataLoader] - val_dataloader: Callable[[Any], DataLoader] - test_dataloader: Callable[[Any], DataLoader]

or: - self.datamodule, which has train_dataloader, val_dataloader, and test_dataloader attributes.

input_shape: DataShape

output_shape: DataShape

validate_attributes()

static configs() → Configs

train_dataloader(*args: Any, **kwargs: Any) → torch.utils.data.DataLoader

The train dataloader

val_dataloader(*args: Any, **kwargs: Any) → Union[torch.utils.data.DataLoader, List[torch.utils.data.DataLoader]]

The val dataloader

test_dataloader(*args: Any, **kwargs: Any) → Union[torch.utils.data.DataLoader, List[torch.utils.data.DataLoader]]

The test dataloader

class ride.core.RideClassificationDataset(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: RideDataset

Base-class for Ride classification datasets.

If no dataset is specified otherwise, this mixin is automatically add as a base of RideModule childen.

User-specified datasets must inherit from this class, and specify the following: - self.input_shape: Union[int, Sequence[int], Sequence[Sequence[int]]] - self.output_shape: Union[int, Sequence[int], Sequence[Sequence[int]]] - self.classes: List[str]

and either the functions: - train_dataloader: Callable[[Any], DataLoader] - val_dataloader: Callable[[Any], DataLoader] - test_dataloader: Callable[[Any], DataLoader]

or: - self.datamodule, which has train_dataloader, val_dataloader, and test_dataloader attributes.

property num_classes: int

classes: List[str]

static configs() → Configs

validate_attributes()

metrics_epoch(preds: torch.Tensor, targets: torch.Tensor, prefix: str = None, *args, **kwargs)