def main():
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)
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
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(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_100.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)
print(state)
shutil.rmtree(tempdir)
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 = [
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI607-Guys-1097-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI175-HH-1570-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI385-HH-2078-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI344-Guys-0905-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI409-Guys-0960-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI584-Guys-1129-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI253-HH-1694-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI092-HH-1436-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI574-IOP-1156-T1.nii.gz"
]),
os.sep.join([
"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, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
# define transforms for image
val_transforms = Compose(
[ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
ToTensor()])
# define nifti dataset
val_ds = NiftiDataset(image_files=images,
labels=labels,
transform=val_transforms,
image_only=False)
# create DenseNet121
net = monai.networks.nets.densenet.densenet121(spatial_dims=3,
in_channels=1,
out_channels=2)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch[0], batch[1]), device, non_blocking)
# 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
)
val_stats_handler.attach(evaluator)
# for the array data format, assume the 3rd item of batch data is the meta_data
prediction_saver = ClassificationSaver(
output_dir="tempdir",
batch_transform=lambda batch: batch[2],
output_transform=lambda output: output[0].argmax(1),
)
prediction_saver.attach(evaluator)
# the model was trained by "densenet_training_array" example
CheckpointLoader(load_path="./runs_array/net_checkpoint_20.pth",
load_dict={
"net": net
}).attach(evaluator)
# create a validation data loader
val_loader = DataLoader(val_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
state = evaluator.run(val_loader)
print(state)
# 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(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 = [{
"image": img,
"label": seg
} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
ToTensord(keys=["image", "label"]),
])
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda:0")
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,
).to(device)
val_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
])
val_handlers = [
StatsHandler(output_transform=lambda x: None),
CheckpointLoader(load_path="./runs/net_key_metric=0.9101.pth",
load_dict={"net": net}),
SegmentationSaver(
output_dir="./runs/",
batch_transform=lambda batch: batch["image_meta_dict"],
output_transform=lambda output: output["pred"],
),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SlidingWindowInferer(roi_size=(96, 96, 96),
sw_batch_size=4,
overlap=0.5),
post_transform=val_post_transforms,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=True,
output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"val_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers,
)
evaluator.run()
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 run_inference_test(root_dir, model_file, device=torch.device("cuda:0")):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{
"image": img,
"label": seg
} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
ToTensord(keys=["image", "label"]),
])
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
# 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,
).to(device)
val_post_transforms = Compose([
Activationsd(keys="pred", output_postfix="act", sigmoid=True),
AsDiscreted(keys="pred_act",
output_postfix="dis",
threshold_values=True),
KeepLargestConnectedComponentd(keys="pred_act_dis",
applied_values=[1],
output_postfix=None),
])
val_handlers = [
StatsHandler(output_transform=lambda x: None),
CheckpointLoader(load_path=f"{model_file}", load_dict={"net": net}),
SegmentationSaver(
output_dir=root_dir,
batch_transform=lambda x: {
"filename_or_obj": x["image.filename_or_obj"],
"affine": x["image.affine"],
"original_affine": x["image.original_affine"],
"spatial_shape": x["image.spatial_shape"],
},
output_transform=lambda x: x["pred_act_dis"],
),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SlidingWindowInferer(roi_size=(96, 96, 96),
sw_batch_size=4,
overlap=0.5),
post_transform=val_post_transforms,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=True,
output_transform=lambda x:
(x["pred_act_dis"], x["label"]))
},
additional_metrics={
"val_acc":
Accuracy(
output_transform=lambda x: (x["pred_act_dis"], x["label"]))
},
val_handlers=val_handlers,
)
evaluator.run()
return evaluator.state.best_metric
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)
def evaluate(args):
if args.local_rank == 0 and not os.path.exists(args.dir):
# create 16 random image, mask paris for evaluation
print(f"generating synthetic data to {args.dir} (this may take a while)")
os.makedirs(args.dir)
# set random seed to generate same random data for every node
np.random.seed(seed=0)
for i in range(16):
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(args.dir, f"img{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(args.dir, f"seg{i:d}.nii.gz"))
# initialize the distributed evaluation process, every GPU runs in a process
dist.init_process_group(backend="nccl", init_method="env://")
images = sorted(glob(os.path.join(args.dir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(args.dir, "seg*.nii.gz")))
val_files = [{"image": img, "label": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadNiftid(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
]
)
# create a evaluation data loader
val_ds = Dataset(data=val_files, transform=val_transforms)
# create a evaluation data sampler
val_sampler = DistributedSampler(val_ds, shuffle=False)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=2, pin_memory=True, sampler=val_sampler)
# create UNet, DiceLoss and Adam optimizer
device = torch.device(f"cuda:{args.local_rank}")
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,
).to(device)
# wrap the model with DistributedDataParallel module
net = DistributedDataParallel(net, device_ids=[args.local_rank])
val_post_transforms = Compose(
[
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
]
)
val_handlers = [
CheckpointLoader(
load_path="./runs/checkpoint_epoch=4.pth",
load_dict={"net": net},
# config mapping to expected GPU device
map_location={"cuda:0": f"cuda:{args.local_rank}"},
),
]
if dist.get_rank() == 0:
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
val_handlers.extend(
[
StatsHandler(output_transform=lambda x: None),
SegmentationSaver(
output_dir="./runs/",
batch_transform=lambda batch: batch["image_meta_dict"],
output_transform=lambda output: output["pred"],
),
]
)
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5),
post_transform=val_post_transforms,
key_val_metric={
"val_mean_dice": MeanDice(
include_background=True, output_transform=lambda x: (x["pred"], x["label"]), device=device,
)
},
additional_metrics={"val_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"]), device=device)},
val_handlers=val_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.config.get_torch_version_tuple() >= (1, 6) else False,
)
evaluator.run()
dist.destroy_process_group()
def run_inference_test(root_dir, model_file, device="cuda:0", amp=False, num_workers=4):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{"image": img, "label": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
ToTensord(keys=["image", "label"]),
]
)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=num_workers)
# create UNet, DiceLoss and Adam optimizer
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)
val_postprocessing = Compose(
[
ToTensord(keys=["pred", "label"]),
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold=0.5),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
# test the case that `pred` in `engine.state.output`, while `image_meta_dict` in `engine.state.batch`
SaveImaged(
keys="pred", meta_keys=PostFix.meta("image"), output_dir=root_dir, output_postfix="seg_transform"
),
]
)
val_handlers = [
StatsHandler(iteration_log=False),
CheckpointLoader(load_path=f"{model_file}", load_dict={"net": net}),
SegmentationSaver(
output_dir=root_dir,
output_postfix="seg_handler",
batch_transform=from_engine(PostFix.meta("image")),
output_transform=from_engine("pred"),
),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5),
postprocessing=val_postprocessing,
key_val_metric={
"val_mean_dice": MeanDice(include_background=True, output_transform=from_engine(["pred", "label"]))
},
additional_metrics={"val_acc": Accuracy(output_transform=from_engine(["pred", "label"]))},
val_handlers=val_handlers,
amp=bool(amp),
)
evaluator.run()
return evaluator.state.best_metric
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, 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
)
val_stats_handler.attach(evaluator)
# for the array data format, assume the 3rd item of batch data is the meta_data
prediction_saver = ClassificationSaver(output_dir='tempdir', name='evaluator',
batch_transform=lambda batch: {'filename_or_obj': batch['img.filename_or_obj']},
output_transform=lambda output: output[0].argmax(1))
prediction_saver.attach(evaluator)
# the model was trained by "densenet_training_dict" example
CheckpointLoader(load_path='./runs/net_checkpoint_40.pth', load_dict={'net': net}).attach(evaluator)
# 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())
state = evaluator.run(val_loader)
def main(tempdir):
config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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)
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
imtrans = Compose([ScaleIntensity(), AddChannel(), EnsureType()])
segtrans = Compose([AddChannel(), EnsureType()])
ds = ImageDataset(images, segs, transform=imtrans, seg_transform=segtrans, image_only=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = 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)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
save_image = SaveImage(output_dir="tempdir", output_ext=".nii.gz", output_postfix="seg")
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)
seg_probs = [post_trans(i) for i in decollate_batch(seg_probs)]
val_data = decollate_batch(batch[2])
for seg_prob, data in zip(seg_probs, val_data):
save_image(seg_prob, data)
return seg_probs, val_labels
evaluator = Engine(_sliding_window_processor)
# add evaluation metric to the evaluator engine
MeanDice().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)
# the model was trained by "unet_training_array" example
ckpt_saver = CheckpointLoader(load_path="./runs_array/net_checkpoint_100.pt", 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)
print(state)
