File size: 3,698 Bytes
b4d7ac8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING
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)
|