# 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 Callable, Optional
import torch
from monai.inferers import SimpleInferer
from monai.utils import exact_version, optional_import
from monai.engines.utils import CommonKeys as Keys
from monai.engines.utils import default_prepare_batch
from monai.engines.workflow import Workflow
Engine, _ = optional_import("ignite.engine", "0.3.0", exact_version, "Engine")
Metric, _ = optional_import("ignite.metrics", "0.3.0", exact_version, "Metric")
[docs]class Trainer(Workflow):
"""
Base class for all kinds of trainers, inherits from Workflow.
"""
[docs] def run(self) -> None:
"""
Execute training based on Ignite Engine.
If call this function multiple times, it will continuously run from the previous state.
"""
if self._is_done(self.state):
self.state.iteration = 0 # to avoid creating new State instance in ignite Engine.run
super().run()
def get_train_stats(self):
return {"total_epochs": self.state.max_epochs, "total_iterations": self.state.epoch_length}
[docs]class SupervisedTrainer(Trainer):
"""
Standard supervised training method with image and label, inherits from trainer and Workflow.
Args:
device (torch.device): an object representing the device on which to run.
max_epochs: the total epoch number for engine to run, validator and evaluator have only 1 epoch.
train_data_loader (torch.DataLoader): Ignite engine use data_loader to run, must be torch.DataLoader.
network (Network): to train with this network.
optimizer (Optimizer): the optimizer associated to the network.
loss_function (Loss): the loss function associated to the optimizer.
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 (Inferer): inference method that execute model forward on input data, like: SlidingWindow, etc.
amp: whether to enable auto-mixed-precision training, reserved.
post_transform (Transform): execute additional transformation for the model output data.
Typically, several Tensor based transforms composed by `Compose`.
key_train_metric (ignite.metric): compute metric when every iteration completed, and save average value to
engine.state.metrics when epoch completed. key_train_metric is the main metric to compare and save the
checkpoint into files.
additional_metrics (dict): more Ignite metrics that also attach to Ignite Engine.
train_handlers (list): every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
CheckpointHandler, StatsHandler, SegmentationSaver, etc.
"""
def __init__(
self,
device: torch.device,
max_epochs: int,
train_data_loader,
network,
optimizer,
loss_function,
prepare_batch: Callable = default_prepare_batch,
iteration_update: Optional[Callable] = None,
inferer=SimpleInferer(),
amp: bool = True,
post_transform=None,
key_train_metric: Optional[Metric] = None,
additional_metrics=None,
train_handlers=None,
):
# set up Ignite engine and environments
super().__init__(
device=device,
max_epochs=max_epochs,
amp=amp,
data_loader=train_data_loader,
prepare_batch=prepare_batch,
iteration_update=iteration_update,
key_metric=key_train_metric,
additional_metrics=additional_metrics,
handlers=train_handlers,
post_transform=post_transform,
)
self.network = network
self.optimizer = optimizer
self.loss_function = loss_function
self.inferer = inferer
def _iteration(self, engine: Engine, batchdata):
"""
Callback function for the Supervised Training 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.
- LOSS: loss value computed by loss function.
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
batchdata (dict or array of tensor): input data for this iteration.
Raises:
ValueError: must provide batch data for current iteration.
"""
if batchdata is None:
raise ValueError("must provide batch data for current iteration.")
inputs, targets = self.prepare_batch(batchdata)
inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device)
self.network.train()
self.optimizer.zero_grad()
# execute forward computation
predictions = self.inferer(inputs, self.network)
# compute loss
loss = self.loss_function(predictions, targets).mean()
loss.backward()
self.optimizer.step()
return {Keys.IMAGE: inputs, Keys.LABEL: targets, Keys.PRED: predictions, Keys.LOSS: loss.item()}