def _sliding_window_processor(_engine, batch):
net.eval()
img, seg, meta_data = batch
with torch.no_grad():
seg_probs = sliding_window_inference(img.to(device), roi_size,
sw_batch_size, net)
return predict_segmentation(seg_probs)
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions
val_stats_handler = StatsHandler(
name='evaluator',
output_transform=lambda x:
None # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# for the arrary data format, assume the 3rd item of batch data is the meta_data
file_saver = SegmentationSaver(
output_path='tempdir',
output_ext='.nii.gz',
output_postfix='seg',
name='evaluator',
batch_transform=lambda x: x[2],
output_transform=lambda output: predict_segmentation(output[0]))
file_saver.attach(evaluator)
# the model was trained by "unet_training_array" exmple
ckpt_saver = CheckpointLoader(load_path='./runs/net_checkpoint_50.pth',
load_dict={'net': net})
ckpt_saver.attach(evaluator)
# sliding window inferene need to input 1 image in every iteration
loader = DataLoader(ds,
batch_size=1,
num_workers=1,
pin_memory=torch.cuda.is_available())
state = evaluator.run(loader)
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():
config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
tempdir = tempfile.mkdtemp()
print('generating synthetic data to {} (this may take a while)'.format(tempdir))
for i in range(5):
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
imtrans = Compose([ScaleIntensity(), AddChannel(), ToTensor()])
segtrans = Compose([AddChannel(), ToTensor()])
ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans, image_only=False)
device = torch.device('cuda:0')
net = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
net.to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
def _sliding_window_processor(engine, batch):
net.eval()
with torch.no_grad():
val_images, val_labels = batch[0].to(device), batch[1].to(device)
seg_probs = sliding_window_inference(val_images, roi_size, sw_batch_size, net)
return seg_probs, val_labels
evaluator = Engine(_sliding_window_processor)
# add evaluation metric to the evaluator engine
MeanDice(add_sigmoid=True, to_onehot_y=False).attach(evaluator, 'Mean_Dice')
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions
val_stats_handler = StatsHandler(
name='evaluator',
output_transform=lambda x: None # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# for the array data format, assume the 3rd item of batch data is the meta_data
file_saver = SegmentationSaver(
output_dir='tempdir', output_ext='.nii.gz', output_postfix='seg', name='evaluator',
batch_transform=lambda x: x[2], output_transform=lambda output: predict_segmentation(output[0]))
file_saver.attach(evaluator)
# the model was trained by "unet_training_array" example
ckpt_saver = CheckpointLoader(load_path='./runs/net_checkpoint_50.pth', load_dict={'net': net})
ckpt_saver.attach(evaluator)
# sliding window inference for one image at every iteration
loader = DataLoader(ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())
state = evaluator.run(loader)
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",
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(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.
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(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,
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)
shutil.rmtree(tempdir)
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 forward(self, x):
x = self.model(x)
return x, predict_segmentation(x)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
tempdir = tempfile.mkdtemp()
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
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"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")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys=["img", "seg"]),
ToTensord(keys=["img", "seg"]),
]
)
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
device = torch.device("cuda:0")
net = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
net.to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
def _sliding_window_processor(engine, batch):
net.eval()
with torch.no_grad():
val_images, val_labels = batch["img"].to(device), batch["seg"].to(device)
seg_probs = sliding_window_inference(val_images, roi_size, sw_batch_size, net)
return seg_probs, val_labels
evaluator = Engine(_sliding_window_processor)
# add evaluation metric to the evaluator engine
MeanDice(sigmoid=True, to_onehot_y=False).attach(evaluator, "Mean_Dice")
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# convert the necessary metadata from batch data
SegmentationSaver(
output_dir="tempdir",
output_ext=".nii.gz",
output_postfix="seg",
name="evaluator",
batch_transform=lambda batch: {"filename_or_obj": batch["img.filename_or_obj"], "affine": batch["img.affine"]},
output_transform=lambda output: predict_segmentation(output[0]),
).attach(evaluator)
# the model was trained by "unet_training_dict" example
CheckpointLoader(load_path="./runs/net_checkpoint_50.pth", load_dict={"net": net}).attach(evaluator)
# sliding window inference for one image at every iteration
val_loader = DataLoader(
val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()
)
state = evaluator.run(val_loader)
print(state)
shutil.rmtree(tempdir)
