ride.core
¶
Module Contents¶
Classes¶
Configs module for holding project configurations. |
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Base-class for modules using the Ride ecosystem. |
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Abstract base-class for Ride mixins |
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Abstract base-class for Ride mixins |
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Abstract base-class for Optimizer mixins |
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Base-class for Ride datasets. |
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Base-class for Ride classification datasets. |
Functions¶
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Attributes¶
- class ride.core.Configs[source]¶
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 [source]¶
Collect the configs from all class bases
- Returns:
Aggregated configurations
- Return type:
- class ride.core.RideModule[source]¶
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.
- classmethod with_dataset(ds: RideDataset)[source]¶
- class ride.core.RideMixin(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)[source]¶
Bases:
abc.ABC
Abstract base-class for Ride mixins
- class ride.core.DefaultMethods(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)[source]¶
Bases:
RideMixin
Abstract base-class for Ride mixins
- class ride.core.OptimizerMixin(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)[source]¶
Bases:
RideMixin
Abstract base-class for Optimizer mixins
- class ride.core.RideDataset(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)[source]¶
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.
- train_dataloader(*args: Any, **kwargs: Any) torch.utils.data.DataLoader [source]¶
The train dataloader
- val_dataloader(*args: Any, **kwargs: Any) Union[torch.utils.data.DataLoader, List[torch.utils.data.DataLoader]] [source]¶
The val dataloader
- test_dataloader(*args: Any, **kwargs: Any) Union[torch.utils.data.DataLoader, List[torch.utils.data.DataLoader]] [source]¶
The test dataloader
- class ride.core.RideClassificationDataset(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)[source]¶
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.
- metrics_epoch(preds: torch.Tensor, targets: torch.Tensor, prefix: str = None, *args, **kwargs)[source]¶