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| from __future__ import annotations |
|
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| from typing import TYPE_CHECKING |
|
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| from monai.config import IgniteInfo |
| from monai.engines.evaluator import Evaluator |
| from monai.utils import min_version, optional_import |
|
|
| Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events") |
| if TYPE_CHECKING: |
| from ignite.engine import Engine |
| else: |
| Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine") |
|
|
|
|
| class ValidationHandler: |
| """ |
| Attach validator to the trainer engine in Ignite. |
| It can support to execute validation every N epochs or every N iterations. |
| |
| """ |
|
|
| def __init__( |
| self, interval: int, validator: Evaluator | None = None, epoch_level: bool = True, exec_at_start: bool = False |
| ) -> None: |
| """ |
| Args: |
| interval: do validation every N epochs or every N iterations during training. |
| validator: run the validator when trigger validation, suppose to be Evaluator. |
| if None, should call `set_validator()` before training. |
| epoch_level: execute validation every N epochs or N iterations. |
| `True` is epoch level, `False` is iteration level. |
| exec_at_start: whether to execute a validation first when starting the training. |
| default to `False`. It can be useful especially for some transfer-learning cases |
| to validate the initial model before training. |
| |
| Raises: |
| TypeError: When ``validator`` is not a ``monai.engines.evaluator.Evaluator``. |
| |
| """ |
| if validator is not None and not isinstance(validator, Evaluator): |
| raise TypeError(f"validator must be a monai.engines.evaluator.Evaluator but is {type(validator).__name__}.") |
| self.validator = validator |
| self.interval = interval |
| self.epoch_level = epoch_level |
| self.exec_at_start = exec_at_start |
|
|
| def set_validator(self, validator: Evaluator) -> None: |
| """ |
| Set validator if not setting in the __init__(). |
| """ |
| if not isinstance(validator, Evaluator): |
| raise TypeError(f"validator must be a monai.engines.evaluator.Evaluator but is {type(validator).__name__}.") |
| self.validator = validator |
|
|
| def attach(self, engine: Engine) -> None: |
| """ |
| Args: |
| engine: Ignite Engine, it can be a trainer, validator or evaluator. |
| """ |
| if self.epoch_level: |
| engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.interval), self) |
| else: |
| engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.interval), self) |
| if self.exec_at_start: |
| engine.add_event_handler(Events.STARTED, self) |
|
|
| def __call__(self, engine: Engine) -> None: |
| """ |
| Args: |
| engine: Ignite Engine, it can be a trainer, validator or evaluator. |
| """ |
| if self.validator is None: |
| raise RuntimeError("please set validator in __init__() or call `set_validator()` before training.") |
| self.validator.run(engine.state.epoch) |
|
|
