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# 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, Union
import torch
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from monai.engines.utils import CommonKeys as Keys
from monai.engines.utils import GanKeys, default_make_latent, default_prepare_batch
from monai.engines.workflow import Workflow
from monai.inferers import Inferer, SimpleInferer
from monai.transforms import Transform
from monai.utils import exact_version, optional_import
if TYPE_CHECKING:
from ignite.engine import Engine
from ignite.metrics import Metric
else:
Engine, _ = optional_import("ignite.engine", "0.3.0", exact_version, "Engine")
Metric, _ = optional_import("ignite.metrics", "0.3.0", exact_version, "Metric")
class Trainer(Workflow):
"""
Base class for all kinds of trainers, inherits from Workflow.
"""
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
self.scaler = torch.cuda.amp.GradScaler() if self.amp else None
super().run()
def get_train_stats(self) -> Dict[str, float]:
return {"total_epochs": self.state.max_epochs, "total_iterations": self.state.epoch_length}
class SupervisedTrainer(Trainer):
"""
Standard supervised training method with image and label, inherits from ``Trainer`` and ``Workflow``.
Args:
device: an object representing the device on which to run.
max_epochs: the total epoch number for trainer to run.
train_data_loader: Ignite engine use data_loader to run, must be torch.DataLoader.
network: to train with this network.
optimizer: the optimizer associated to the network.
loss_function: 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: 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_train_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: more Ignite metrics that also attach to Ignite Engine.
train_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 training, default is False.
"""
def __init__(
self,
device: torch.device,
max_epochs: int,
train_data_loader: DataLoader,
network: torch.nn.Module,
optimizer: Optimizer,
loss_function: Callable,
prepare_batch: Callable = default_prepare_batch,
iteration_update: Optional[Callable] = None,
inferer: Inferer = SimpleInferer(),
post_transform: Optional[Transform] = None,
key_train_metric: Optional[Dict[str, Metric]] = None,
additional_metrics: Optional[Dict[str, Metric]] = None,
train_handlers: Optional[Sequence] = None,
amp: bool = False,
) -> None:
# set up Ignite engine and environments
super().__init__(
device=device,
max_epochs=max_epochs,
data_loader=train_data_loader,
prepare_batch=prepare_batch,
iteration_update=iteration_update,
post_transform=post_transform,
key_metric=key_train_metric,
additional_metrics=additional_metrics,
handlers=train_handlers,
amp=amp,
)
self.network = network
self.optimizer = optimizer
self.loss_function = loss_function
self.inferer = inferer
def _iteration(self, engine: Engine, batchdata: Dict[str, torch.Tensor]):
"""
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: 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.")
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()
if self.amp and self.scaler is not None:
with torch.cuda.amp.autocast():
predictions = self.inferer(inputs, self.network)
loss = self.loss_function(predictions, targets).mean()
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
else:
predictions = self.inferer(inputs, self.network)
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()}
class GanTrainer(Trainer):
"""
Generative adversarial network training based on Goodfellow et al. 2014 https://arxiv.org/abs/1406.266,
inherits from ``Trainer`` and ``Workflow``.
Training Loop: for each batch of data size `m`
1. Generate `m` fakes from random latent codes.
2. Update discriminator with these fakes and current batch reals, repeated d_train_steps times.
3. If g_update_latents, generate `m` fakes from new random latent codes.
4. Update generator with these fakes using discriminator feedback.
Args:
device: an object representing the device on which to run.
max_epochs: the total epoch number for engine to run.
train_data_loader: Core ignite engines uses `DataLoader` for training loop batchdata.
g_network: generator (G) network architecture.
g_optimizer: G optimizer function.
g_loss_function: G loss function for optimizer.
d_network: discriminator (D) network architecture.
d_optimizer: D optimizer function.
d_loss_function: D loss function for optimizer.
g_inferer: inference method to execute G model forward. Defaults to ``SimpleInferer()``.
d_inferer: inference method to execute D model forward. Defaults to ``SimpleInferer()``.
d_train_steps: number of times to update D with real data minibatch. Defaults to ``1``.
latent_shape: size of G input latent code. Defaults to ``64``.
d_prepare_batch: callback function to prepare batchdata for D inferer.
Defaults to return ``GanKeys.REALS`` in batchdata dict.
g_prepare_batch: callback function to create batch of latent input for G inferer.
Defaults to return random latents.
g_update_latents: Calculate G loss with new latent codes. Defaults to ``True``.
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_train_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: more Ignite metrics that also attach to Ignite Engine.
train_handlers: 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: DataLoader,
g_network: torch.nn.Module,
g_optimizer: Optimizer,
g_loss_function: Callable,
d_network: torch.nn.Module,
d_optimizer: Optimizer,
d_loss_function: Callable,
g_inferer: Inferer = SimpleInferer(),
d_inferer: Inferer = SimpleInferer(),
d_train_steps: int = 1,
latent_shape: int = 64,
d_prepare_batch: Callable = default_prepare_batch,
g_prepare_batch: Callable = default_make_latent,
g_update_latents: bool = True,
iteration_update: Optional[Callable] = None,
post_transform: Optional[Transform] = None,
key_train_metric: Optional[Dict[str, Metric]] = None,
additional_metrics: Optional[Dict[str, Metric]] = None,
train_handlers: Optional[Sequence] = None,
):
# set up Ignite engine and environments
super().__init__(
device=device,
max_epochs=max_epochs,
data_loader=train_data_loader,
prepare_batch=d_prepare_batch,
iteration_update=iteration_update,
key_metric=key_train_metric,
additional_metrics=additional_metrics,
handlers=train_handlers,
post_transform=post_transform,
)
self.g_network = g_network
self.g_optimizer = g_optimizer
self.g_loss_function = g_loss_function
self.g_inferer = g_inferer
self.d_network = d_network
self.d_optimizer = d_optimizer
self.d_loss_function = d_loss_function
self.d_inferer = d_inferer
self.d_train_steps = d_train_steps
self.latent_shape = latent_shape
self.g_prepare_batch = g_prepare_batch
self.g_update_latents = g_update_latents
def _iteration(
self, engine: Engine, batchdata: Union[Dict, Sequence]
) -> Dict[str, Union[torch.Tensor, int, float, bool]]:
"""
Callback function for Adversarial Training processing logic of 1 iteration in Ignite Engine.
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: must provide batch data for current iteration.
"""
if batchdata is None:
raise ValueError("must provide batch data for current iteration.")
d_input = self.prepare_batch(batchdata).to(engine.state.device)
batch_size = self.data_loader.batch_size
g_input = self.g_prepare_batch(batch_size, self.latent_shape, batchdata).to(engine.state.device)
g_output = self.g_inferer(g_input, self.g_network)
# Train Discriminator
d_total_loss = torch.zeros(
1,
)
for _ in range(self.d_train_steps):
self.d_optimizer.zero_grad()
dloss = self.d_loss_function(g_output, d_input)
dloss.backward()
self.d_optimizer.step()
d_total_loss += dloss.item()
# Train Generator
if self.g_update_latents:
g_input = self.g_prepare_batch(batch_size, self.latent_shape, batchdata).to(engine.state.device)
g_output = self.g_inferer(g_input, self.g_network)
self.g_optimizer.zero_grad()
g_loss = self.g_loss_function(g_output)
g_loss.backward()
self.g_optimizer.step()
return {
GanKeys.REALS: d_input,
GanKeys.FAKES: g_output,
GanKeys.LATENTS: g_input,
GanKeys.GLOSS: g_loss.item(),
GanKeys.DLOSS: d_total_loss.item(),
}
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