def test_metrics_writer(self):
tempdir = tempfile.mkdtemp()
shutil.rmtree(tempdir, ignore_errors=True)
# set up engine
def _train_func(engine, batch):
return batch + 1.0
engine = Engine(_train_func)
# set up dummy metric
@engine.on(Events.EPOCH_COMPLETED)
def _update_metric(engine):
current_metric = engine.state.metrics.get("acc", 0.1)
engine.state.metrics["acc"] = current_metric + 0.1
# set up testing handler
writer = SummaryWriter(log_dir=tempdir)
stats_handler = TensorBoardStatsHandler(
writer, output_transform=lambda x: {"loss": x * 2.0}, global_epoch_transform=lambda x: x * 3.0
)
stats_handler.attach(engine)
engine.run(range(3), max_epochs=2)
# check logging output
self.assertTrue(os.path.exists(tempdir))
self.assertTrue(len(glob.glob(tempdir)) > 0)
shutil.rmtree(tempdir)
def test_metrics_writer(self):
default_dir = os.path.join('.', 'runs')
shutil.rmtree(default_dir, ignore_errors=True)
with tempfile.TemporaryDirectory() as temp_dir:
# set up engine
def _train_func(engine, batch):
return batch + 1.0
engine = Engine(_train_func)
# set up dummy metric
@engine.on(Events.EPOCH_COMPLETED)
def _update_metric(engine):
current_metric = engine.state.metrics.get('acc', 0.1)
engine.state.metrics['acc'] = current_metric + 0.1
# set up testing handler
writer = SummaryWriter(log_dir=temp_dir)
stats_handler = TensorBoardStatsHandler(
writer,
output_transform=lambda x: {'loss': x * 2.0},
global_epoch_transform=lambda x: x * 3.0)
stats_handler.attach(engine)
engine.run(range(3), max_epochs=2)
# check logging output
self.assertTrue(len(glob.glob(temp_dir)) > 0)
self.assertTrue(not os.path.exists(default_dir))
def test_metrics_writer(self):
with tempfile.TemporaryDirectory() as tempdir:
# set up engine
def _train_func(engine, batch):
return [batch + 1.0]
engine = Engine(_train_func)
# set up dummy metric
@engine.on(Events.EPOCH_COMPLETED)
def _update_metric(engine):
current_metric = engine.state.metrics.get("acc", 0.1)
engine.state.metrics["acc"] = current_metric + 0.1
engine.state.test = current_metric
# set up testing handler
writer = SummaryWriter(log_dir=tempdir)
stats_handler = TensorBoardStatsHandler(
summary_writer=writer,
iteration_log=True,
epoch_log=False,
output_transform=lambda x: {"loss": x[0] * 2.0},
global_epoch_transform=lambda x: x * 3.0,
state_attributes=["test"],
)
stats_handler.attach(engine)
engine.run(range(3), max_epochs=2)
writer.close()
# check logging output
self.assertTrue(len(glob.glob(tempdir)) > 0)
def test_metrics_print(self):
with tempfile.TemporaryDirectory() as tempdir:
# set up engine
def _train_func(engine, batch):
return batch + 1.0
engine = Engine(_train_func)
# set up dummy metric
@engine.on(Events.EPOCH_COMPLETED)
def _update_metric(engine):
current_metric = engine.state.metrics.get("acc", 0.1)
engine.state.metrics["acc"] = current_metric + 0.1
# set up testing handler
stats_handler = TensorBoardStatsHandler(log_dir=tempdir)
stats_handler.attach(engine)
engine.run(range(3), max_epochs=2)
# check logging output
self.assertTrue(len(glob.glob(tempdir)) > 0)
def test_metrics_print(self):
default_dir = os.path.join('.', 'runs')
shutil.rmtree(default_dir, ignore_errors=True)
# set up engine
def _train_func(engine, batch):
return batch + 1.0
engine = Engine(_train_func)
# set up dummy metric
@engine.on(Events.EPOCH_COMPLETED)
def _update_metric(engine):
current_metric = engine.state.metrics.get('acc', 0.1)
engine.state.metrics['acc'] = current_metric + 0.1
# set up testing handler
stats_handler = TensorBoardStatsHandler()
stats_handler.attach(engine)
engine.run(range(3), max_epochs=2)
# check logging output
self.assertTrue(os.path.exists(default_dir))
shutil.rmtree(default_dir)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz",
]
# 2 binary labels for gender classification: man and woman
labels = np.array(
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
train_files = [{
"img": img,
"label": label
} for img, label in zip(images[:10], labels[:10])]
val_files = [{
"img": img,
"label": label
} for img, label in zip(images[-10:], labels[-10:])]
# define transforms for image
train_transforms = Compose([
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]),
ToTensord(keys=["img"]),
])
val_transforms = Compose([
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
ToTensord(keys=["img"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["label"])
# create DenseNet121, CrossEntropyLoss and Adam optimizer
net = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
)
loss = torch.nn.CrossEntropyLoss()
lr = 1e-5
opt = torch.optim.Adam(net.parameters(), lr)
device = torch.device("cuda:0")
# Ignite trainer expects batch=(img, label) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch["img"], batch["label"]), device,
non_blocking)
trainer = create_supervised_trainer(net,
opt,
loss,
device,
False,
prepare_batch=prepare_batch)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs/",
"net",
n_saved=10,
require_empty=False)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
"net": net,
"opt": opt
})
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer")
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler()
train_tensorboard_stats_handler.attach(trainer)
# set parameters for validation
validation_every_n_epochs = 1
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {
metric_name: Accuracy(),
"AUC": ROCAUC(to_onehot_y=True, add_softmax=True)
}
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net,
val_metrics,
device,
True,
prepare_batch=prepare_batch)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(
patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=early_stopper)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
pin_memory=torch.cuda.is_available())
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask paris
tempdir = tempfile.mkdtemp()
print('generating synthetic data to {} (this may take a while)'.format(tempdir))
for i in range(40):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
# define transforms for image and segmentation
train_imtrans = Compose([
ScaleIntensity(),
AddChannel(),
RandSpatialCrop((96, 96, 96), random_size=False),
ToTensor()
])
train_segtrans = Compose([
AddChannel(),
RandSpatialCrop((96, 96, 96), random_size=False),
ToTensor()
])
val_imtrans = Compose([
ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
ToTensor()
])
val_segtrans = Compose([
AddChannel(),
Resize((96, 96, 96)),
ToTensor()
])
# define nifti dataset, data loader
check_ds = NiftiDataset(images, segs, transform=train_imtrans, seg_transform=train_segtrans)
check_loader = DataLoader(check_ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(check_loader)
print(im.shape, seg.shape)
# create a training data loader
train_ds = NiftiDataset(images[:20], segs[:20], transform=train_imtrans, seg_transform=train_segtrans)
train_loader = DataLoader(train_ds, batch_size=5, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = NiftiDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans)
val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available())
# create UNet, DiceLoss and Adam optimizer
net = monai.networks.nets.UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
loss = monai.losses.DiceLoss(do_sigmoid=True)
lr = 1e-3
opt = torch.optim.Adam(net.parameters(), lr)
device = torch.device('cuda:0')
# ignite trainer expects batch=(img, seg) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
trainer = create_supervised_trainer(net, opt, loss, device, False)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={'net': net, 'opt': opt})
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not a loss value
train_stats_handler = StatsHandler(name='trainer')
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler()
train_tensorboard_stats_handler.attach(trainer)
validation_every_n_epochs = 1
# Set parameters for validation
metric_name = 'Mean_Dice'
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: MeanDice(add_sigmoid=True, to_onehot_y=False)}
# ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net, val_metrics, device, True)
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name='evaluator',
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every validation epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add handler to draw the first image and the corresponding label and model output in the last batch
# here we draw the 3D output as GIF format along Depth axis, at every validation epoch
val_tensorboard_image_handler = TensorBoardImageHandler(
batch_transform=lambda batch: (batch[0], batch[1]),
output_transform=lambda output: predict_segmentation(output[0]),
global_iter_transform=lambda x: trainer.state.epoch
)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=val_tensorboard_image_handler)
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
shutil.rmtree(tempdir)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask paris
tempdir = tempfile.mkdtemp()
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_3d(128,
128,
128,
num_seg_classes=1,
channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"img{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
train_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys=["img", "seg"]),
RandCropByPosNegLabeld(keys=["img", "seg"],
label_key="seg",
spatial_size=[96, 96, 96],
pos=1,
neg=1,
num_samples=4),
RandRotate90d(keys=["img", "seg"], prob=0.5, spatial_axes=[0, 2]),
ToTensord(keys=["img", "seg"]),
])
val_transforms = Compose([
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys=["img", "seg"]),
ToTensord(keys=["img", "seg"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["seg"].shape)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = DataLoader(
train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=5,
num_workers=8,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
# create UNet, DiceLoss and Adam optimizer
net = monai.networks.nets.UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
loss = monai.losses.DiceLoss(sigmoid=True)
lr = 1e-3
opt = torch.optim.Adam(net.parameters(), lr)
device = torch.device("cuda:0")
# Ignite trainer expects batch=(img, seg) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch["img"], batch["seg"]), device,
non_blocking)
trainer = create_supervised_trainer(net,
opt,
loss,
device,
False,
prepare_batch=prepare_batch)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs/",
"net",
n_saved=10,
require_empty=False)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
"net": net,
"opt": opt
})
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer")
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler()
train_tensorboard_stats_handler.attach(trainer)
validation_every_n_iters = 5
# set parameters for validation
metric_name = "Mean_Dice"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: MeanDice(sigmoid=True, to_onehot_y=False)}
# Ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net,
val_metrics,
device,
True,
prepare_batch=prepare_batch)
@trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters))
def run_validation(engine):
evaluator.run(val_loader)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(
patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=early_stopper)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every validation epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.iteration,
) # fetch global iteration number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add handler to draw the first image and the corresponding label and model output in the last batch
# here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations.
val_tensorboard_image_handler = TensorBoardImageHandler(
batch_transform=lambda batch: (batch["img"], batch["seg"]),
output_transform=lambda output: predict_segmentation(output[0]),
global_iter_transform=lambda x: trainer.state.epoch,
)
evaluator.add_event_handler(event_name=Events.ITERATION_COMPLETED(every=2),
handler=val_tensorboard_image_handler)
train_epochs = 5
state = trainer.run(train_loader, train_epochs)
print(state)
shutil.rmtree(tempdir)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={'net': net, 'opt': opt})
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name='trainer')
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler()
train_tensorboard_stats_handler.attach(trainer)
# set parameters for validation
validation_every_n_epochs = 1
metric_name = 'Accuracy'
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
# ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net, val_metrics, device, True)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name='evaluator',
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
def main():
"""
Basic UNet as implemented in MONAI for Fetal Brain Segmentation, but using
ignite to manage training and validation loop and checkpointing
:return:
"""
"""
Read input and configuration parameters
"""
parser = argparse.ArgumentParser(
description='Run basic UNet with MONAI - Ignite version.')
parser.add_argument('--config',
dest='config',
metavar='config',
type=str,
help='config file')
args = parser.parse_args()
with open(args.config) as f:
config_info = yaml.load(f, Loader=yaml.FullLoader)
# print to log the parameter setups
print(yaml.dump(config_info))
# GPU params
cuda_device = config_info['device']['cuda_device']
num_workers = config_info['device']['num_workers']
# training and validation params
loss_type = config_info['training']['loss_type']
batch_size_train = config_info['training']['batch_size_train']
batch_size_valid = config_info['training']['batch_size_valid']
lr = float(config_info['training']['lr'])
lr_decay = config_info['training']['lr_decay']
if lr_decay is not None:
lr_decay = float(lr_decay)
nr_train_epochs = config_info['training']['nr_train_epochs']
validation_every_n_epochs = config_info['training'][
'validation_every_n_epochs']
sliding_window_validation = config_info['training'][
'sliding_window_validation']
if 'model_to_load' in config_info['training'].keys():
model_to_load = config_info['training']['model_to_load']
if not os.path.exists(model_to_load):
raise BlockingIOError(
"cannot find model: {}".format(model_to_load))
else:
model_to_load = None
if 'manual_seed' in config_info['training'].keys():
seed = config_info['training']['manual_seed']
else:
seed = None
# data params
data_root = config_info['data']['data_root']
training_list = config_info['data']['training_list']
validation_list = config_info['data']['validation_list']
# model saving
out_model_dir = os.path.join(
config_info['output']['out_model_dir'],
datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_' +
config_info['output']['output_subfix'])
print("Saving to directory ", out_model_dir)
if 'cache_dir' in config_info['output'].keys():
out_cache_dir = config_info['output']['cache_dir']
else:
out_cache_dir = os.path.join(out_model_dir, 'persistent_cache')
max_nr_models_saved = config_info['output']['max_nr_models_saved']
val_image_to_tensorboad = config_info['output']['val_image_to_tensorboad']
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
torch.cuda.set_device(cuda_device)
if seed is not None:
# set manual seed if required (both numpy and torch)
set_determinism(seed=seed)
# # set torch only seed
# torch.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
"""
Data Preparation
"""
# create cache directory to store results for Persistent Dataset
persistent_cache: Path = Path(out_cache_dir)
persistent_cache.mkdir(parents=True, exist_ok=True)
# create training and validation data lists
train_files = create_data_list(data_folder_list=data_root,
subject_list=training_list,
img_postfix='_Image',
label_postfix='_Label')
print(len(train_files))
print(train_files[0])
print(train_files[-1])
val_files = create_data_list(data_folder_list=data_root,
subject_list=validation_list,
img_postfix='_Image',
label_postfix='_Label')
print(len(val_files))
print(val_files[0])
print(val_files[-1])
# data preprocessing for training:
# - convert data to right format [batch, channel, dim, dim, dim]
# - apply whitening
# - resize to (96, 96) in-plane (preserve z-direction)
# - define 2D patches to be extracted
# - add data augmentation (random rotation and random flip)
# - squeeze to 2D
train_transforms = Compose([
LoadNiftid(keys=['img', 'seg']),
AddChanneld(keys=['img', 'seg']),
NormalizeIntensityd(keys=['img']),
Resized(keys=['img', 'seg'],
spatial_size=[96, 96],
interp_order=[1, 0],
anti_aliasing=[True, False]),
RandSpatialCropd(keys=['img', 'seg'],
roi_size=[96, 96, 1],
random_size=False),
RandRotated(keys=['img', 'seg'],
degrees=90,
prob=0.2,
spatial_axes=[0, 1],
interp_order=[1, 0],
reshape=False),
RandFlipd(keys=['img', 'seg'], spatial_axis=[0, 1]),
SqueezeDimd(keys=['img', 'seg'], dim=-1),
ToTensord(keys=['img', 'seg'])
])
# create a training data loader
# train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0,
# num_workers=num_workers)
train_ds = monai.data.PersistentDataset(data=train_files,
transform=train_transforms,
cache_dir=persistent_cache)
train_loader = DataLoader(train_ds,
batch_size=batch_size_train,
shuffle=True,
num_workers=num_workers,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
# check_train_data = monai.utils.misc.first(train_loader)
# print("Training data tensor shapes")
# print(check_train_data['img'].shape, check_train_data['seg'].shape)
# data preprocessing for validation:
# - convert data to right format [batch, channel, dim, dim, dim]
# - apply whitening
# - resize to (96, 96) in-plane (preserve z-direction)
if sliding_window_validation:
val_transforms = Compose([
LoadNiftid(keys=['img', 'seg']),
AddChanneld(keys=['img', 'seg']),
NormalizeIntensityd(keys=['img']),
Resized(keys=['img', 'seg'],
spatial_size=[96, 96],
interp_order=[1, 0],
anti_aliasing=[True, False]),
ToTensord(keys=['img', 'seg'])
])
do_shuffle = False
collate_fn_to_use = None
else:
# - add extraction of 2D slices from validation set to emulate how loss is computed at training
val_transforms = Compose([
LoadNiftid(keys=['img', 'seg']),
AddChanneld(keys=['img', 'seg']),
NormalizeIntensityd(keys=['img']),
Resized(keys=['img', 'seg'],
spatial_size=[96, 96],
interp_order=[1, 0],
anti_aliasing=[True, False]),
RandSpatialCropd(keys=['img', 'seg'],
roi_size=[96, 96, 1],
random_size=False),
SqueezeDimd(keys=['img', 'seg'], dim=-1),
ToTensord(keys=['img', 'seg'])
])
do_shuffle = True
collate_fn_to_use = list_data_collate
# create a validation data loader
# val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0,
# num_workers=num_workers)
val_ds = monai.data.PersistentDataset(data=val_files,
transform=val_transforms,
cache_dir=persistent_cache)
val_loader = DataLoader(val_ds,
batch_size=batch_size_valid,
shuffle=do_shuffle,
collate_fn=collate_fn_to_use,
num_workers=num_workers)
# check_valid_data = monai.utils.misc.first(val_loader)
# print("Validation data tensor shapes")
# print(check_valid_data['img'].shape, check_valid_data['seg'].shape)
"""
Network preparation
"""
# Create UNet, DiceLoss and Adam optimizer.
net = monai.networks.nets.UNet(
dimensions=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
loss_function = monai.losses.DiceLoss(do_sigmoid=True)
opt = torch.optim.Adam(net.parameters(), lr)
device = torch.cuda.current_device()
if lr_decay is not None:
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=opt,
gamma=lr_decay,
last_epoch=-1)
"""
Set ignite trainer
"""
# function to manage batch at training
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch['img'], batch['seg']), device,
non_blocking)
trainer = create_supervised_trainer(model=net,
optimizer=opt,
loss_fn=loss_function,
device=device,
non_blocking=False,
prepare_batch=prepare_batch)
# adding checkpoint handler to save models (network params and optimizer stats) during training
if model_to_load is not None:
checkpoint_handler = CheckpointLoader(load_path=model_to_load,
load_dict={
'net': net,
'opt': opt,
})
checkpoint_handler.attach(trainer)
state = trainer.state_dict()
else:
checkpoint_handler = ModelCheckpoint(out_model_dir,
'net',
n_saved=max_nr_models_saved,
require_empty=False)
# trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=save_params)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
'net': net,
'opt': opt
})
# StatsHandler prints loss at every iteration and print metrics at every epoch
train_stats_handler = StatsHandler(name='trainer')
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
writer_train = SummaryWriter(log_dir=os.path.join(out_model_dir, "train"))
train_tensorboard_stats_handler = TensorBoardStatsHandler(
summary_writer=writer_train)
train_tensorboard_stats_handler.attach(trainer)
if lr_decay is not None:
print("Using Exponential LR decay")
lr_schedule_handler = LrScheduleHandler(lr_scheduler,
print_lr=True,
name="lr_scheduler",
writer=writer_train)
lr_schedule_handler.attach(trainer)
"""
Set ignite evaluator to perform validation at training
"""
# set parameters for validation
metric_name = 'Mean_Dice'
# add evaluation metric to the evaluator engine
val_metrics = {
"Loss": 1.0 - MeanDice(add_sigmoid=True, to_onehot_y=False),
"Mean_Dice": MeanDice(add_sigmoid=True, to_onehot_y=False)
}
def _sliding_window_processor(engine, batch):
net.eval()
with torch.no_grad():
val_images, val_labels = batch['img'].to(device), batch['seg'].to(
device)
roi_size = (96, 96, 1)
seg_probs = sliding_window_inference(val_images, roi_size,
batch_size_valid, net)
return seg_probs, val_labels
if sliding_window_validation:
# use sliding window inference at validation
print("3D evaluator is used")
net.to(device)
evaluator = Engine(_sliding_window_processor)
for name, metric in val_metrics.items():
metric.attach(evaluator, name)
else:
# ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
print("2D evaluator is used")
evaluator = create_supervised_evaluator(model=net,
metrics=val_metrics,
device=device,
non_blocking=True,
prepare_batch=prepare_batch)
epoch_len = len(train_ds) // train_loader.batch_size
validation_every_n_iters = validation_every_n_epochs * epoch_len
@trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters))
def run_validation(engine):
evaluator.run(val_loader)
# add early stopping handler to evaluator
# early_stopper = EarlyStopping(patience=4,
# score_function=stopping_fn_from_metric(metric_name),
# trainer=trainer)
# evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name='evaluator',
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every validation epoch
writer_valid = SummaryWriter(log_dir=os.path.join(out_model_dir, "valid"))
val_tensorboard_stats_handler = TensorBoardStatsHandler(
summary_writer=writer_valid,
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.iteration
) # fetch global iteration number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add handler to draw the first image and the corresponding label and model output in the last batch
# here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations.
if val_image_to_tensorboad:
val_tensorboard_image_handler = TensorBoardImageHandler(
summary_writer=writer_valid,
batch_transform=lambda batch: (batch['img'], batch['seg']),
output_transform=lambda output: predict_segmentation(output[0]),
global_iter_transform=lambda x: trainer.state.epoch)
evaluator.add_event_handler(
event_name=Events.ITERATION_COMPLETED(every=1),
handler=val_tensorboard_image_handler)
"""
Run training
"""
state = trainer.run(train_loader, nr_train_epochs)
print("Done!")
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
# the path of ixi IXI-T1 dataset
data_path = os.sep.join([".", "workspace", "data", "medical", "ixi", "IXI-T1"])
images = [
"IXI314-IOP-0889-T1.nii.gz",
"IXI249-Guys-1072-T1.nii.gz",
"IXI609-HH-2600-T1.nii.gz",
"IXI173-HH-1590-T1.nii.gz",
"IXI020-Guys-0700-T1.nii.gz",
"IXI342-Guys-0909-T1.nii.gz",
"IXI134-Guys-0780-T1.nii.gz",
"IXI577-HH-2661-T1.nii.gz",
"IXI066-Guys-0731-T1.nii.gz",
"IXI130-HH-1528-T1.nii.gz",
"IXI607-Guys-1097-T1.nii.gz",
"IXI175-HH-1570-T1.nii.gz",
"IXI385-HH-2078-T1.nii.gz",
"IXI344-Guys-0905-T1.nii.gz",
"IXI409-Guys-0960-T1.nii.gz",
"IXI584-Guys-1129-T1.nii.gz",
"IXI253-HH-1694-T1.nii.gz",
"IXI092-HH-1436-T1.nii.gz",
"IXI574-IOP-1156-T1.nii.gz",
"IXI585-Guys-1130-T1.nii.gz",
]
images = [os.sep.join([data_path, f]) for f in images]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
# define transforms
train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90(), EnsureType()])
val_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), EnsureType()])
# define image dataset, data loader
check_ds = ImageDataset(image_files=images, labels=labels, transform=train_transforms)
check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
im, label = monai.utils.misc.first(check_loader)
print(type(im), im.shape, label)
# create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
loss = torch.nn.CrossEntropyLoss()
lr = 1e-5
opt = torch.optim.Adam(net.parameters(), lr)
# Ignite trainer expects batch=(img, label) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
trainer = create_supervised_trainer(net, opt, loss, device, False)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs_array/", "net", n_saved=10, require_empty=False)
trainer.add_event_handler(
event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={"net": net, "opt": opt}
)
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer", output_transform=lambda x: x)
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler(output_transform=lambda x: x)
train_tensorboard_stats_handler.attach(trainer)
# set parameters for validation
validation_every_n_epochs = 1
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net, val_metrics, device, True)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
# create a validation data loader
val_ds = ImageDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# create a training data loader
train_ds = ImageDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
print(state)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask pairs
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
# define transforms for image and segmentation
train_imtrans = Compose([
ScaleIntensity(),
AddChannel(),
RandSpatialCrop((96, 96, 96), random_size=False),
EnsureType(),
])
train_segtrans = Compose([
AddChannel(),
RandSpatialCrop((96, 96, 96), random_size=False),
EnsureType()
])
val_imtrans = Compose(
[ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
EnsureType()])
val_segtrans = Compose([AddChannel(), Resize((96, 96, 96)), EnsureType()])
# define image dataset, data loader
check_ds = ImageDataset(images,
segs,
transform=train_imtrans,
seg_transform=train_segtrans)
check_loader = DataLoader(check_ds,
batch_size=10,
num_workers=2,
pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(check_loader)
print(im.shape, seg.shape)
# create a training data loader
train_ds = ImageDataset(images[:20],
segs[:20],
transform=train_imtrans,
seg_transform=train_segtrans)
train_loader = DataLoader(
train_ds,
batch_size=5,
shuffle=True,
num_workers=8,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_ds = ImageDataset(images[-20:],
segs[-20:],
transform=val_imtrans,
seg_transform=val_segtrans)
val_loader = DataLoader(val_ds,
batch_size=5,
num_workers=8,
pin_memory=torch.cuda.is_available())
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = monai.networks.nets.UNet(
spatial_dims=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss = monai.losses.DiceLoss(sigmoid=True)
lr = 1e-3
opt = torch.optim.Adam(net.parameters(), lr)
# Ignite trainer expects batch=(img, seg) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
trainer = create_supervised_trainer(net, opt, loss, device, False)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs_array/",
"net",
n_saved=10,
require_empty=False)
trainer.add_event_handler(
event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
"net": net,
"opt": opt
},
)
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not a loss value
train_stats_handler = StatsHandler(name="trainer",
output_transform=lambda x: x)
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x: x)
train_tensorboard_stats_handler.attach(trainer)
validation_every_n_epochs = 1
# Set parameters for validation
metric_name = "Mean_Dice"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: MeanDice()}
post_pred = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
post_label = Compose([EnsureType(), AsDiscrete(threshold=0.5)])
# Ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(
net,
val_metrics,
device,
True,
output_transform=lambda x, y, y_pred:
([post_pred(i) for i in decollate_batch(y_pred)],
[post_label(i) for i in decollate_batch(y)]),
)
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(
patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=early_stopper)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every validation epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add handler to draw the first image and the corresponding label and model output in the last batch
# here we draw the 3D output as GIF format along Depth axis, at every validation epoch
val_tensorboard_image_handler = TensorBoardImageHandler(
batch_transform=lambda batch: (batch[0], batch[1]),
output_transform=lambda output: output[0],
global_iter_transform=lambda x: trainer.state.epoch,
)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=val_tensorboard_image_handler)
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
print(state)