# Copyright 2020 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 typing import TYPE_CHECKING, Callable, Dict, Optional, Sequence, Tuple
import torch
from torch.utils.data import DataLoader
from monai.engines.utils import CommonKeys as Keys
from monai.engines.utils import default_prepare_batch
from monai.engines.workflow import Workflow
from monai.inferers import Inferer, SimpleInferer
from monai.transforms import Transform
from monai.utils import ensure_tuple, exact_version, optional_import
if TYPE_CHECKING:
from ignite.engine import Engine
from ignite.metrics import Metric
else:
Engine, _ = optional_import("ignite.engine", "0.4.2", exact_version, "Engine")
Metric, _ = optional_import("ignite.metrics", "0.4.2", exact_version, "Metric")
__all__ = ["Evaluator", "SupervisedEvaluator", "EnsembleEvaluator"]
[docs]class Evaluator(Workflow):
"""
Base class for all kinds of evaluators, inherits from Workflow.
Args:
device: an object representing the device on which to run.
val_data_loader: Ignite engine use data_loader to run, must be torch.DataLoader.
epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.
non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously
with respect to the host. For other cases, this argument has no effect.
prepare_batch: function to parse image and label for current iteration.
iteration_update: the callable function for every iteration, expect to accept `engine`
and `batchdata` as input parameters. if not provided, use `self._iteration()` instead.
post_transform: execute additional transformation for the model output data.
Typically, several Tensor based transforms composed by `Compose`.
key_val_metric: compute metric when every iteration completed, and save average value to
engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the
checkpoint into files.
additional_metrics: more Ignite metrics that also attach to Ignite Engine.
val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
CheckpointHandler, StatsHandler, SegmentationSaver, etc.
amp: whether to enable auto-mixed-precision evaluation, default is False.
"""
def __init__(
self,
device: torch.device,
val_data_loader: DataLoader,
epoch_length: Optional[int] = None,
non_blocking: bool = False,
prepare_batch: Callable = default_prepare_batch,
iteration_update: Optional[Callable] = None,
post_transform: Optional[Transform] = None,
key_val_metric: Optional[Dict[str, Metric]] = None,
additional_metrics: Optional[Dict[str, Metric]] = None,
val_handlers: Optional[Sequence] = None,
amp: bool = False,
) -> None:
super().__init__(
device=device,
max_epochs=1,
data_loader=val_data_loader,
epoch_length=epoch_length,
non_blocking=non_blocking,
prepare_batch=prepare_batch,
iteration_update=iteration_update,
post_transform=post_transform,
key_metric=key_val_metric,
additional_metrics=additional_metrics,
handlers=val_handlers,
amp=amp,
)
[docs] def run(self, global_epoch: int = 1) -> None:
"""
Execute validation/evaluation based on Ignite Engine.
Args:
global_epoch: the overall epoch if during a training. evaluator engine can get it from trainer.
"""
# init env value for current validation process
self.state.max_epochs = global_epoch
self.state.epoch = global_epoch - 1
self.state.iteration = 0
super().run()
def get_validation_stats(self) -> Dict[str, float]:
return {"best_validation_metric": self.state.best_metric, "best_validation_epoch": self.state.best_metric_epoch}
[docs]class SupervisedEvaluator(Evaluator):
"""
Standard supervised evaluation method with image and label(optional), inherits from evaluator and Workflow.
Args:
device: an object representing the device on which to run.
val_data_loader: Ignite engine use data_loader to run, must be torch.DataLoader.
network: use the network to run model forward.
epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.
non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously
with respect to the host. For other cases, this argument has no effect.
prepare_batch: function to parse image and label for current iteration.
iteration_update: the callable function for every iteration, expect to accept `engine`
and `batchdata` as input parameters. if not provided, use `self._iteration()` instead.
inferer: inference method that execute model forward on input data, like: SlidingWindow, etc.
post_transform: execute additional transformation for the model output data.
Typically, several Tensor based transforms composed by `Compose`.
key_val_metric: compute metric when every iteration completed, and save average value to
engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the
checkpoint into files.
additional_metrics: more Ignite metrics that also attach to Ignite Engine.
val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
CheckpointHandler, StatsHandler, SegmentationSaver, etc.
amp: whether to enable auto-mixed-precision evaluation, default is False.
"""
def __init__(
self,
device: torch.device,
val_data_loader: DataLoader,
network: torch.nn.Module,
epoch_length: Optional[int] = None,
non_blocking: bool = False,
prepare_batch: Callable = default_prepare_batch,
iteration_update: Optional[Callable] = None,
inferer: Optional[Inferer] = None,
post_transform: Optional[Transform] = None,
key_val_metric: Optional[Dict[str, Metric]] = None,
additional_metrics: Optional[Dict[str, Metric]] = None,
val_handlers: Optional[Sequence] = None,
amp: bool = False,
) -> None:
super().__init__(
device=device,
val_data_loader=val_data_loader,
epoch_length=epoch_length,
non_blocking=non_blocking,
prepare_batch=prepare_batch,
iteration_update=iteration_update,
post_transform=post_transform,
key_val_metric=key_val_metric,
additional_metrics=additional_metrics,
val_handlers=val_handlers,
amp=amp,
)
self.network = network
self.inferer = SimpleInferer() if inferer is None else inferer
def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine.
Return below items in a dictionary:
- IMAGE: image Tensor data for model input, already moved to device.
- LABEL: label Tensor data corresponding to the image, already moved to device.
- PRED: prediction result of model.
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.
Raises:
ValueError: When ``batchdata`` is None.
"""
if batchdata is None:
raise ValueError("Must provide batch data for current iteration.")
batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)
if len(batch) == 2:
inputs, targets = batch
args: Tuple = tuple()
kwargs: Dict = dict()
else:
inputs, targets, args, kwargs = batch
# execute forward computation
self.network.eval()
with torch.no_grad():
if self.amp:
with torch.cuda.amp.autocast():
predictions = self.inferer(inputs, self.network, *args, **kwargs)
else:
predictions = self.inferer(inputs, self.network, *args, **kwargs)
return {Keys.IMAGE: inputs, Keys.LABEL: targets, Keys.PRED: predictions}
[docs]class EnsembleEvaluator(Evaluator):
"""
Ensemble evaluation for multiple models, inherits from evaluator and Workflow.
It accepts a list of models for inference and outputs a list of predictions for further operations.
Args:
device: an object representing the device on which to run.
val_data_loader: Ignite engine use data_loader to run, must be torch.DataLoader.
epoch_length: number of iterations for one epoch, default to `len(val_data_loader)`.
networks: use the networks to run model forward in order.
pred_keys: the keys to store every prediction data.
the length must exactly match the number of networks.
non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously
with respect to the host. For other cases, this argument has no effect.
prepare_batch: function to parse image and label for current iteration.
iteration_update: the callable function for every iteration, expect to accept `engine`
and `batchdata` as input parameters. if not provided, use `self._iteration()` instead.
inferer: inference method that execute model forward on input data, like: SlidingWindow, etc.
post_transform: execute additional transformation for the model output data.
Typically, several Tensor based transforms composed by `Compose`.
key_val_metric: compute metric when every iteration completed, and save average value to
engine.state.metrics when epoch completed. key_val_metric is the main metric to compare and save the
checkpoint into files.
additional_metrics: more Ignite metrics that also attach to Ignite Engine.
val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
CheckpointHandler, StatsHandler, SegmentationSaver, etc.
amp: whether to enable auto-mixed-precision evaluation, default is False.
"""
def __init__(
self,
device: torch.device,
val_data_loader: DataLoader,
networks: Sequence[torch.nn.Module],
pred_keys: Sequence[str],
epoch_length: Optional[int] = None,
non_blocking: bool = False,
prepare_batch: Callable = default_prepare_batch,
iteration_update: Optional[Callable] = None,
inferer: Optional[Inferer] = None,
post_transform: Optional[Transform] = None,
key_val_metric: Optional[Dict[str, Metric]] = None,
additional_metrics: Optional[Dict[str, Metric]] = None,
val_handlers: Optional[Sequence] = None,
amp: bool = False,
) -> None:
super().__init__(
device=device,
val_data_loader=val_data_loader,
epoch_length=epoch_length,
non_blocking=non_blocking,
prepare_batch=prepare_batch,
iteration_update=iteration_update,
post_transform=post_transform,
key_val_metric=key_val_metric,
additional_metrics=additional_metrics,
val_handlers=val_handlers,
amp=amp,
)
self.networks = ensure_tuple(networks)
self.pred_keys = ensure_tuple(pred_keys)
self.inferer = SimpleInferer() if inferer is None else inferer
def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
callback function for the Supervised Evaluation processing logic of 1 iteration in Ignite Engine.
Return below items in a dictionary:
- IMAGE: image Tensor data for model input, already moved to device.
- LABEL: label Tensor data corresponding to the image, already moved to device.
- pred_keys[0]: prediction result of network 0.
- pred_keys[1]: prediction result of network 1.
- ... ...
- pred_keys[N]: prediction result of network N.
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
batchdata: input data for this iteration, usually can be dictionary or tuple of Tensor data.
Raises:
ValueError: When ``batchdata`` is None.
"""
if batchdata is None:
raise ValueError("Must provide batch data for current iteration.")
batch = self.prepare_batch(batchdata, engine.state.device, engine.non_blocking)
if len(batch) == 2:
inputs, targets = batch
args: Tuple = tuple()
kwargs: Dict = dict()
else:
inputs, targets, args, kwargs = batch
# execute forward computation
predictions = {Keys.IMAGE: inputs, Keys.LABEL: targets}
for idx, network in enumerate(self.networks):
network.eval()
with torch.no_grad():
if self.amp:
with torch.cuda.amp.autocast():
predictions.update({self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)})
else:
predictions.update({self.pred_keys[idx]: self.inferer(inputs, network, *args, **kwargs)})
return predictions