DetectionLearner
- class easypl.learners.detection.DetectionLearner(model: Optional[Union[Module, List[Module]]] = None, loss: Optional[Union[Module, List[Module]]] = None, optimizer: Optional[Union[WrapperOptimizer, List[WrapperOptimizer]]] = None, lr_scheduler: Optional[Union[WrapperScheduler, List[WrapperScheduler]]] = None, train_metrics: Optional[List[Metric]] = None, val_metrics: Optional[List[Metric]] = None, test_metrics: Optional[List[Metric]] = None, data_keys: Optional[List[str]] = None, target_keys: Optional[List[str]] = None, image_info_key: Optional[str] = None, postprocessing: Optional[BasePostprocessing] = None)
Detection learner.
- model
torch.nn.Module model.
- Type:
Optional[Union[torch.nn.Module, List[torch.nn.Module]]]
- loss
torch.nn.Module loss function.
- Type:
Optional[Union[torch.nn.Module, List[torch.nn.Module]]]
- optimizer
Optimizer wrapper object.
- Type:
Optional[Union[WrapperOptimizer, List[WrapperOptimizer]]]
- lr_scheduler
Scheduler object for lr scheduling.
- Type:
Optional[Union[WrapperScheduler, List[WrapperScheduler]]]
- train_metrics
List of train metrics.
- Type:
Optional[List[Metric]]
- val_metrics
List of validation metrics.
- Type:
Optional[List[Metric]]
- test_metrics
List of test metrics.
- Type:
Optional[List[Metric]]
- data_keys
List of data keys
- Type:
Optional[List[str]]
- target_keys
List of target keys
- Type:
Optional[List[str]]
- image_info_key
Key of image info for postprocessing function
- Type:
Optional[str]
- postprocessing
If postprocessing is not None then this
- Type:
Optional
- forward(samples: Tensor) Tensor
Standart method for forwarding model. .. attribute:: samples
Image tensor.
- type:
torch.Tensor
- Returns:
Output from model.
- Return type:
torch.Tensor
- get_outputs(batch: Dict, optimizer_idx: int = 0) Dict
Abtract method for selecting and preprocessing outputs from batch
- batch
Batch in step
- Type:
Dict
- optimizer_idx
Index of optimizer
- Type:
int
- Returns:
Dict with keys: [“loss”, “metric”, “log”]
- Return type:
Dict