def test_prob_map_generator(self, name, size):
# set up dataset
dataset = TestDataset(name, size)
data_loader = DataLoader(dataset, batch_size=1)
# set up engine
def inference(enging, batch):
pass
engine = Engine(inference)
# add ProbMapGenerator() to evaluator
output_dir = os.path.join(os.path.dirname(__file__), "testing_data")
prob_map_gen = ProbMapProducer(output_dir=output_dir)
evaluator = TestEvaluator(torch.device("cpu:0"), data_loader, size, val_handlers=[prob_map_gen])
# set up validation handler
validation = ValidationHandler(evaluator, interval=1)
validation.attach(engine)
engine.run(data_loader)
prob_map = np.load(os.path.join(output_dir, name + ".npy"))
self.assertListEqual(np.diag(prob_map).astype(int).tolist(), list(range(1, size + 1)))
def train_handlers(self, context: Context):
handlers: List[Any] = []
# LR Scheduler
lr_scheduler = self.lr_scheduler_handler(context)
if lr_scheduler:
handlers.append(lr_scheduler)
if context.local_rank == 0:
handlers.extend([
StatsHandler(tag_name="train_loss",
output_transform=from_engine(["loss"],
first=True)),
TensorBoardStatsHandler(
log_dir=context.events_dir,
tag_name="train_loss",
output_transform=from_engine(["loss"], first=True),
),
])
if context.evaluator:
logger.info(
f"{context.local_rank} - Adding Validation to run every '{self._val_interval}' interval"
)
handlers.append(
ValidationHandler(self._val_interval,
validator=context.evaluator,
epoch_level=True))
return handlers
def test_content(self):
data = [0] * 8
# set up engine
def _train_func(engine, batch):
pass
engine = Engine(_train_func)
# set up testing handler
val_data_loader = torch.utils.data.DataLoader(Dataset(data))
evaluator = TestEvaluator(torch.device("cpu:0"), val_data_loader)
saver = ValidationHandler(interval=2, validator=evaluator)
saver.attach(engine)
engine.run(data, max_epochs=5)
self.assertEqual(evaluator.state.max_epochs, 4)
self.assertEqual(evaluator.state.epoch_length, 8)
def train(args):
# load hyper parameters
task_id = args.task_id
fold = args.fold
val_output_dir = "./runs_{}_fold{}_{}/".format(task_id, fold,
args.expr_name)
log_filename = "nnunet_task{}_fold{}.log".format(task_id, fold)
log_filename = os.path.join(val_output_dir, log_filename)
interval = args.interval
learning_rate = args.learning_rate
max_epochs = args.max_epochs
multi_gpu_flag = args.multi_gpu
amp_flag = args.amp
lr_decay_flag = args.lr_decay
sw_batch_size = args.sw_batch_size
tta_val = args.tta_val
batch_dice = args.batch_dice
window_mode = args.window_mode
eval_overlap = args.eval_overlap
local_rank = args.local_rank
determinism_flag = args.determinism_flag
determinism_seed = args.determinism_seed
if determinism_flag:
set_determinism(seed=determinism_seed)
if local_rank == 0:
print("Using deterministic training.")
# transforms
train_batch_size = data_loader_params[task_id]["batch_size"]
if multi_gpu_flag:
dist.init_process_group(backend="nccl", init_method="env://")
device = torch.device(f"cuda:{local_rank}")
torch.cuda.set_device(device)
else:
device = torch.device("cuda")
properties, val_loader = get_data(args, mode="validation")
_, train_loader = get_data(args, batch_size=train_batch_size, mode="train")
# produce the network
checkpoint = args.checkpoint
net = get_network(properties, task_id, val_output_dir, checkpoint)
net = net.to(device)
if multi_gpu_flag:
net = DistributedDataParallel(module=net,
device_ids=[device],
find_unused_parameters=True)
optimizer = torch.optim.SGD(
net.parameters(),
lr=learning_rate,
momentum=0.99,
weight_decay=3e-5,
nesterov=True,
)
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lambda epoch: (1 - epoch / max_epochs)**0.9)
# produce evaluator
val_handlers = [
StatsHandler(output_transform=lambda x: None),
CheckpointSaver(save_dir=val_output_dir,
save_dict={"net": net},
save_key_metric=True),
]
evaluator = DynUNetEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
n_classes=len(properties["labels"]),
inferer=SlidingWindowInferer(
roi_size=patch_size[task_id],
sw_batch_size=sw_batch_size,
overlap=eval_overlap,
mode=window_mode,
),
post_transform=None,
key_val_metric={
"val_mean_dice":
MeanDice(
include_background=False,
output_transform=lambda x: (x["pred"], x["label"]),
)
},
val_handlers=val_handlers,
amp=amp_flag,
tta_val=tta_val,
)
# produce trainer
loss = DiceCELoss(to_onehot_y=True, softmax=True, batch=batch_dice)
train_handlers = []
if lr_decay_flag:
train_handlers += [
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True)
]
train_handlers += [
ValidationHandler(validator=evaluator,
interval=interval,
epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
]
trainer = DynUNetTrainer(
device=device,
max_epochs=max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=optimizer,
loss_function=loss,
inferer=SimpleInferer(),
post_transform=None,
key_train_metric=None,
train_handlers=train_handlers,
amp=amp_flag,
)
# run
logger = logging.getLogger()
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
# Setup file handler
fhandler = logging.FileHandler(log_filename)
fhandler.setLevel(logging.INFO)
fhandler.setFormatter(formatter)
# Configure stream handler for the cells
chandler = logging.StreamHandler()
chandler.setLevel(logging.INFO)
chandler.setFormatter(formatter)
# Add both handlers
if local_rank == 0:
logger.addHandler(fhandler)
logger.addHandler(chandler)
logger.setLevel(logging.INFO)
trainer.run()
def run_training_test(root_dir, device="cuda:0", amp=False):
images = sorted(glob(os.path.join(root_dir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
train_files = [{
"image": img,
"label": seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
"image": img,
"label": seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
RandCropByPosNegLabeld(keys=["image", "label"],
label_key="label",
spatial_size=[96, 96, 96],
pos=1,
neg=1,
num_samples=4),
RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]),
ToTensord(keys=["image", "label"]),
])
val_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
ToTensord(keys=["image", "label"]),
])
# create a training data loader
train_ds = monai.data.CacheDataset(data=train_files,
transform=train_transforms,
cache_rate=0.5)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = monai.data.DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=4)
# create a validation data loader
val_ds = monai.data.CacheDataset(data=val_files,
transform=val_transforms,
cache_rate=1.0)
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)
loss = monai.losses.DiceLoss(sigmoid=True)
opt = torch.optim.Adam(net.parameters(), 1e-3)
lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)
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),
TensorBoardStatsHandler(log_dir=root_dir,
output_transform=lambda x: None),
TensorBoardImageHandler(log_dir=root_dir,
batch_transform=lambda x:
(x["image"], x["label"]),
output_transform=lambda x: x["pred"]),
CheckpointSaver(save_dir=root_dir,
save_dict={"net": net},
save_key_metric=True),
]
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,
amp=True if amp else False,
)
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
])
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir=root_dir,
tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir=root_dir,
save_dict={
"net": net,
"opt": opt
},
save_interval=2,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
post_transform=train_post_transforms,
key_train_metric={
"train_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"]))
},
train_handlers=train_handlers,
amp=True if amp else False,
)
trainer.run()
return evaluator.state.best_metric
def configure(self):
self.set_device()
network = UNet(
dimensions=3,
in_channels=1,
out_channels=2,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
norm=Norm.BATCH,
).to(self.device)
if self.multi_gpu:
network = DistributedDataParallel(
module=network,
device_ids=[self.device],
find_unused_parameters=False,
)
train_transforms = Compose([
LoadImaged(keys=("image", "label")),
EnsureChannelFirstd(keys=("image", "label")),
Spacingd(keys=("image", "label"),
pixdim=[1.0, 1.0, 1.0],
mode=["bilinear", "nearest"]),
ScaleIntensityRanged(
keys="image",
a_min=-57,
a_max=164,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=("image", "label"), source_key="image"),
RandCropByPosNegLabeld(
keys=("image", "label"),
label_key="label",
spatial_size=(96, 96, 96),
pos=1,
neg=1,
num_samples=4,
image_key="image",
image_threshold=0,
),
RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5),
ToTensord(keys=("image", "label")),
])
train_datalist = load_decathlon_datalist(self.data_list_file_path,
True, "training")
if self.multi_gpu:
train_datalist = partition_dataset(
data=train_datalist,
shuffle=True,
num_partitions=dist.get_world_size(),
even_divisible=True,
)[dist.get_rank()]
train_ds = CacheDataset(
data=train_datalist,
transform=train_transforms,
cache_num=32,
cache_rate=1.0,
num_workers=4,
)
train_data_loader = DataLoader(
train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
)
val_transforms = Compose([
LoadImaged(keys=("image", "label")),
EnsureChannelFirstd(keys=("image", "label")),
ScaleIntensityRanged(
keys="image",
a_min=-57,
a_max=164,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=("image", "label"), source_key="image"),
ToTensord(keys=("image", "label")),
])
val_datalist = load_decathlon_datalist(self.data_list_file_path, True,
"validation")
val_ds = CacheDataset(val_datalist, val_transforms, 9, 0.0, 4)
val_data_loader = DataLoader(
val_ds,
batch_size=1,
shuffle=False,
num_workers=4,
)
post_transform = Compose([
Activationsd(keys="pred", softmax=True),
AsDiscreted(
keys=["pred", "label"],
argmax=[True, False],
to_onehot=True,
n_classes=2,
),
])
# metric
key_val_metric = {
"val_mean_dice":
MeanDice(
include_background=False,
output_transform=lambda x: (x["pred"], x["label"]),
device=self.device,
)
}
val_handlers = [
StatsHandler(output_transform=lambda x: None),
CheckpointSaver(
save_dir=self.ckpt_dir,
save_dict={"model": network},
save_key_metric=True,
),
TensorBoardStatsHandler(log_dir=self.ckpt_dir,
output_transform=lambda x: None),
]
self.eval_engine = SupervisedEvaluator(
device=self.device,
val_data_loader=val_data_loader,
network=network,
inferer=SlidingWindowInferer(
roi_size=[160, 160, 160],
sw_batch_size=4,
overlap=0.5,
),
post_transform=post_transform,
key_val_metric=key_val_metric,
val_handlers=val_handlers,
amp=self.amp,
)
optimizer = torch.optim.Adam(network.parameters(), self.learning_rate)
loss_function = DiceLoss(to_onehot_y=True, softmax=True)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5000,
gamma=0.1)
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=self.eval_engine,
interval=self.val_interval,
epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(
log_dir=self.ckpt_dir,
tag_name="train_loss",
output_transform=lambda x: x["loss"],
),
]
self.train_engine = SupervisedTrainer(
device=self.device,
max_epochs=self.max_epochs,
train_data_loader=train_data_loader,
network=network,
optimizer=optimizer,
loss_function=loss_function,
inferer=SimpleInferer(),
post_transform=post_transform,
key_train_metric=None,
train_handlers=train_handlers,
amp=self.amp,
)
if self.local_rank > 0:
self.train_engine.logger.setLevel(logging.WARNING)
self.eval_engine.logger.setLevel(logging.WARNING)
network=net,
inferer=get_inferer(),
post_transform=val_post_transform,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=False,
output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers,
amp=amp,
)
# %%
# evaluator as an event handler of the trainer
train_handlers = [
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]),
MetricsSaver(save_dir=model_folder, metrics='*')
]
trainer = monai.engines.SupervisedTrainer(
device=device,
max_epochs=max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=DiceCELoss(),
inferer=get_inferer(),
key_train_metric=None,
# key_train_metric={
# "train_mean_dice": MeanDice(include_background=False, output_transform=lambda x: (x["pred"], x["label"]))
# },
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 = [{
Keys.IMAGE: img,
Keys.LABEL: seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
Keys.IMAGE: img,
Keys.LABEL: seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadNiftid(keys=[Keys.IMAGE, Keys.LABEL]),
AsChannelFirstd(keys=[Keys.IMAGE, Keys.LABEL], channel_dim=-1),
ScaleIntensityd(keys=[Keys.IMAGE, Keys.LABEL]),
RandCropByPosNegLabeld(keys=[Keys.IMAGE, Keys.LABEL],
label_key=Keys.LABEL,
size=[96, 96, 96],
pos=1,
neg=1,
num_samples=4),
RandRotate90d(keys=[Keys.IMAGE, Keys.LABEL],
prob=0.5,
spatial_axes=[0, 2]),
ToTensord(keys=[Keys.IMAGE, Keys.LABEL]),
])
val_transforms = Compose([
LoadNiftid(keys=[Keys.IMAGE, Keys.LABEL]),
AsChannelFirstd(keys=[Keys.IMAGE, Keys.LABEL], channel_dim=-1),
ScaleIntensityd(keys=[Keys.IMAGE, Keys.LABEL]),
ToTensord(keys=[Keys.IMAGE, Keys.LABEL]),
])
# 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)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
# 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)
loss = monai.losses.DiceLoss(do_sigmoid=True)
opt = torch.optim.Adam(net.parameters(), 1e-3)
val_handlers = [StatsHandler(output_transform=lambda x: None)]
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),
val_handlers=val_handlers,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=True,
add_sigmoid=True,
output_transform=lambda x: (x[Keys.PRED], x[Keys.LABEL]))
},
additional_metrics=None,
)
train_handlers = [
ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x[Keys.INFO][Keys.LOSS]),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
train_handlers=train_handlers,
amp=False,
key_train_metric=None,
)
trainer.run()
shutil.rmtree(tempdir)
def train(index):
# ---------- Build the nn-Unet network ------------
if opt.resolution is None:
sizes, spacings = opt.patch_size, opt.spacing
else:
sizes, spacings = opt.patch_size, opt.resolution
strides, kernels = [], []
while True:
spacing_ratio = [sp / min(spacings) for sp in spacings]
stride = [
2 if ratio <= 2 and size >= 8 else 1
for (ratio, size) in zip(spacing_ratio, sizes)
]
kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
if all(s == 1 for s in stride):
break
sizes = [i / j for i, j in zip(sizes, stride)]
spacings = [i * j for i, j in zip(spacings, stride)]
kernels.append(kernel)
strides.append(stride)
strides.insert(0, len(spacings) * [1])
kernels.append(len(spacings) * [3])
net = monai.networks.nets.DynUNet(
spatial_dims=3,
in_channels=opt.in_channels,
out_channels=opt.out_channels,
kernel_size=kernels,
strides=strides,
upsample_kernel_size=strides[1:],
res_block=True,
# act=act_type,
# norm=Norm.BATCH,
).to(device)
from torch.autograd import Variable
from torchsummaryX import summary
data = Variable(
torch.randn(int(opt.batch_size), int(opt.in_channels),
int(opt.patch_size[0]), int(opt.patch_size[1]),
int(opt.patch_size[2]))).cuda()
out = net(data)
summary(net, data)
print("out size: {}".format(out.size()))
# if opt.preload is not None:
# net.load_state_dict(torch.load(opt.preload))
# ---------- ------------------------ ------------
optim = torch.optim.Adam(net.parameters(), lr=opt.lr)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optim, lr_lambda=lambda epoch: (1 - epoch / opt.epochs)**0.9)
loss_function = monai.losses.DiceCELoss(sigmoid=True)
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),
CheckpointSaver(save_dir="./runs/",
save_dict={"net": net},
save_key_metric=True),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loaders[index],
network=net,
inferer=SlidingWindowInferer(roi_size=opt.patch_size,
sw_batch_size=opt.batch_size,
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"]),
)
},
val_handlers=val_handlers)
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
# KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
])
train_handlers = [
ValidationHandler(validator=evaluator,
interval=5,
epoch_level=True),
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": optim
},
save_final=True,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=opt.epochs,
train_data_loader=train_loaders[index],
network=net,
optimizer=optim,
loss_function=loss_function,
inferer=SimpleInferer(),
post_transform=train_post_transforms,
amp=False,
train_handlers=train_handlers,
)
trainer.run()
return net
def train(gpu, args):
"""run a training pipeline."""
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
print('Setting up multiple GPUs')
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * args.ngpus_per_node + gpu
print(args.rank)
dist.init_process_group(
backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank,
)
print('Done!')
#========================================
images = sorted(glob.glob(os.path.join(args.data_folder, "*_ct.nii.gz")))
labels = sorted(glob.glob(os.path.join(args.data_folder, "*_seg.nii.gz")))
logging.info(f"training: image/label ({len(images)}) folder: {args.data_folder}")
amp = True # auto. mixed precision
keys = ("image", "label")
#TODO
is_one_hot = False # whether the label has multiple channels to represent multiple class
train_frac, val_frac = 0.8, 0.2
n_train = int(train_frac * len(images)) + 1
n_val = min(len(images) - n_train, int(val_frac * len(images)))
logging.info(f"training: train {n_train} val {n_val}, folder: {args.data_folder}")
train_files = [{keys[0]: img, keys[1]: seg} for img, seg in zip(images[:n_train], labels[:n_train])]
val_files = [{keys[0]: img, keys[1]: seg} for img, seg in zip(images[-n_val:], labels[-n_val:])]
# create a training data loader
logging.info(f"batch size {args.batch_size}")
train_transforms = get_xforms("train", keys)
train_ds = monai.data.CacheDataset(data=train_files,
transform=train_transforms,
cache_rate=args.cache_rate,
num_workers=args.preprocessing_workers)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=args.world_size,
rank=args.rank
)
train_loader = monai.data.DataLoader(
train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=torch.cuda.is_available(),
sampler=train_sampler) #
else:
train_loader = monai.data.DataLoader(
train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=torch.cuda.is_available())
# create a validation data loader
val_transforms = get_xforms("val", keys)
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(
val_ds,
batch_size=1, # image-level batch to the sliding window method, not the window-level batch
num_workers=args.num_workers,
pin_memory=torch.cuda.is_available(),
)
# create BasicUNet, DiceLoss and Adam optimizer
if args.distributed:
print('Setting Up ')
torch.cuda.set_device(args.gpu)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.cuda(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
args.val_batch_size = int(args.val_batch_size / ngpus_per_node)
args.num_workers = int(
(args.num_workers + ngpus_per_node - 1) / ngpus_per_node
)
net = torch.nn.parallel.DistributedDataParallel(
net, device_ids=[args.gpu]
)
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = get_net().to(device)
logging.info(f"epochs {args.max_epochs}, lr {args.lr}, momentum {args.momentum}")
opt = torch.optim.Adam(net.parameters(), lr=args.lr)
# create evaluator (to be used to measure model quality during training
def pred_transform(y_pred):
y_sigmoid = torch.sigmoid(y_pred)
y_sigmoid = (y_sigmoid >= logit_thresh).float()
return y_sigmoid
logit_thresh = 0.5
train_metric = MeanDice(
include_background=False,
device = device,
output_transform=lambda x: (pred_transform(x["pred"]), x["label"]),
)
val_metric = MeanDice(
include_background=False,
device = device,
output_transform=lambda x: (pred_transform(x["pred"]), x["label"]),
)
val_handlers = [
ProgressBar(),
CheckpointSaver(save_dir=args.model_folder, save_dict={'net': net,
'optimizer': opt},
save_key_metric=True, key_metric_n_saved=3),
]
evaluator = monai.engines.SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=get_inferer(),
key_val_metric={"val_mean_dice": val_metric},
val_handlers=val_handlers,
amp=amp,
)
# evaluator as an event handler of the trainer
train_handlers = [
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]),
LrScheduleHandler(BoundingExponentialLR(opt, gamma=args.gamma),
print_lr=True,
name='bounding_lr_scheduler',
epoch_level=True,)
]
trainer = monai.engines.SupervisedTrainer(
device=device,
max_epochs=args.max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=DiceCELoss(),
inferer=get_inferer(),
key_train_metric={'train_mean_dice': train_metric},
train_handlers=train_handlers,
amp=amp,
)
trainer.run()
def train(args):
"""run a training pipeline."""
save_args_to_file(args, 'runs/')
images = sorted(glob.glob(os.path.join(args.data_folder, "*_ct.nii.gz")))
labels = sorted(glob.glob(os.path.join(args.data_folder, "*_seg.nii.gz")))
logging.info(
f"training: image/label ({len(images)}) folder: {args.data_folder}")
amp = True # auto. mixed precision
keys = ("image", "label")
#TODO
is_one_hot = False # whether the label has multiple channels to represent multiple class
train_frac, val_frac = 0.8, 0.2
n_train = int(train_frac * len(images)) + 1
n_val = min(len(images) - n_train, int(val_frac * len(images)))
logging.info(
f"training: train {n_train} val {n_val}, folder: {args.data_folder}")
train_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[:n_train], labels[:n_train])]
val_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[-n_val:], labels[-n_val:])]
# create a training data loader
logging.info(f"batch size {args.batch_size}")
train_transforms = get_xforms(args, "train", keys)
train_ds = monai.data.CacheDataset(data=train_files,
transform=train_transforms,
cache_rate=args.cache_rate,
num_workers=args.preprocessing_workers)
train_loader = monai.data.DataLoader(
train_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_transforms = get_xforms(args, "val", keys)
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(
val_ds,
batch_size=
1, # image-level batch to the sliding window method, not the window-level batch
num_workers=args.num_workers,
pin_memory=torch.cuda.is_available(),
)
# create BasicUNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = get_net(args.n_classes).to(device)
logging.info(
f"epochs {args.max_epochs}, lr {args.lr}, momentum {args.momentum}")
opt = torch.optim.Adam(net.parameters(), lr=args.lr)
# create evaluator (to be used to measure model quality during training
def pred_transform(y_pred):
y_sigmoid = torch.sigmoid(y_pred)
y_sigmoid = (y_sigmoid >= logit_thresh).float()
return y_sigmoid
logit_thresh = 0.5
train_metric = MeanDice(
include_background=False,
device=device,
output_transform=lambda x: (pred_transform(x["pred"]), x["label"]),
)
val_metric = MeanDice(
include_background=False,
device=device,
output_transform=lambda x: (pred_transform(x["pred"]), x["label"]),
)
val_handlers = [
ProgressBar(),
CheckpointSaver(save_dir=args.model_folder,
save_dict={
'net': net,
'optimizer': opt
},
save_key_metric=True,
key_metric_n_saved=3),
]
evaluator = monai.engines.SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=get_inferer(args),
key_val_metric={"val_mean_dice": val_metric},
val_handlers=val_handlers,
amp=amp,
)
# evaluator as an event handler of the trainer
train_handlers = [
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
LrScheduleHandler(
BoundingExponentialLR(opt,
gamma=args.gamma,
min_lr=args.min_lr,
initial_lr=args.lr),
print_lr=True,
name='bounding_lr_scheduler',
epoch_level=True,
)
]
trainer = monai.engines.SupervisedTrainer(
device=device,
max_epochs=args.max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=DiceCELoss(),
inferer=get_inferer(args),
key_train_metric={'train_mean_dice': train_metric},
train_handlers=train_handlers,
amp=amp,
)
trainer.run()
def train(cfg):
log_dir = create_log_dir(cfg)
device = set_device(cfg)
# --------------------------------------------------------------------------
# Data Loading and Preprocessing
# --------------------------------------------------------------------------
# __________________________________________________________________________
# Build MONAI preprocessing
train_preprocess = Compose([
ToTensorD(keys="image"),
TorchVisionD(keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04),
ToNumpyD(keys="image"),
RandFlipD(keys="image", prob=0.5),
RandRotate90D(keys="image", prob=0.5),
CastToTypeD(keys="image", dtype=np.float32),
RandZoomD(keys="image", prob=0.5, min_zoom=0.9, max_zoom=1.1),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
ToTensorD(keys=("image", "label")),
])
valid_preprocess = Compose([
CastToTypeD(keys="image", dtype=np.float32),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
ToTensorD(keys=("image", "label")),
])
# __________________________________________________________________________
# Create MONAI dataset
train_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="training",
base_dir=cfg["data_root"],
)
valid_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="validation",
base_dir=cfg["data_root"],
)
train_dataset = PatchWSIDataset(
train_json_info_list,
cfg["region_size"],
cfg["grid_shape"],
cfg["patch_size"],
train_preprocess,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
valid_dataset = PatchWSIDataset(
valid_json_info_list,
cfg["region_size"],
cfg["grid_shape"],
cfg["patch_size"],
valid_preprocess,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
# __________________________________________________________________________
# DataLoaders
train_dataloader = DataLoader(train_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=True)
valid_dataloader = DataLoader(valid_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=True)
# __________________________________________________________________________
# Get sample batch and some info
first_sample = first(train_dataloader)
if first_sample is None:
raise ValueError("Fist sample is None!")
print("image: ")
print(" shape", first_sample["image"].shape)
print(" type: ", type(first_sample["image"]))
print(" dtype: ", first_sample["image"].dtype)
print("labels: ")
print(" shape", first_sample["label"].shape)
print(" type: ", type(first_sample["label"]))
print(" dtype: ", first_sample["label"].dtype)
print(f"batch size: {cfg['batch_size']}")
print(f"train number of batches: {len(train_dataloader)}")
print(f"valid number of batches: {len(valid_dataloader)}")
# --------------------------------------------------------------------------
# Deep Learning Classification Model
# --------------------------------------------------------------------------
# __________________________________________________________________________
# initialize model
model = TorchVisionFCModel("resnet18",
num_classes=1,
use_conv=True,
pretrained=cfg["pretrain"])
model = model.to(device)
# loss function
loss_func = torch.nn.BCEWithLogitsLoss()
loss_func = loss_func.to(device)
# optimizer
if cfg["novograd"]:
optimizer = Novograd(model.parameters(), cfg["lr"])
else:
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)
# AMP scaler
if cfg["amp"]:
cfg["amp"] = True if monai.utils.get_torch_version_tuple() >= (
1, 6) else False
else:
cfg["amp"] = False
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=cfg["n_epochs"])
# --------------------------------------------
# Ignite Trainer/Evaluator
# --------------------------------------------
# Evaluator
val_handlers = [
CheckpointSaver(save_dir=log_dir,
save_dict={"net": model},
save_key_metric=True),
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir=log_dir,
output_transform=lambda x: None),
]
val_postprocessing = Compose([
ActivationsD(keys="pred", sigmoid=True),
AsDiscreteD(keys="pred", threshold=0.5)
])
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=valid_dataloader,
network=model,
postprocessing=val_postprocessing,
key_val_metric={
"val_acc":
Accuracy(output_transform=from_engine(["pred", "label"]))
},
val_handlers=val_handlers,
amp=cfg["amp"],
)
# Trainer
train_handlers = [
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True),
CheckpointSaver(save_dir=cfg["logdir"],
save_dict={
"net": model,
"opt": optimizer
},
save_interval=1,
epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=from_engine(["loss"], first=True)),
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
TensorBoardStatsHandler(log_dir=cfg["logdir"],
tag_name="train_loss",
output_transform=from_engine(["loss"],
first=True)),
]
train_postprocessing = Compose([
ActivationsD(keys="pred", sigmoid=True),
AsDiscreteD(keys="pred", threshold=0.5)
])
trainer = SupervisedTrainer(
device=device,
max_epochs=cfg["n_epochs"],
train_data_loader=train_dataloader,
network=model,
optimizer=optimizer,
loss_function=loss_func,
postprocessing=train_postprocessing,
key_train_metric={
"train_acc":
Accuracy(output_transform=from_engine(["pred", "label"]))
},
train_handlers=train_handlers,
amp=cfg["amp"],
)
trainer.run()
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
################################ DATASET ################################
# get dataset
train_ds = CacheDataset(data=train_files,
transform=train_transforms,
cache_rate=0.5)
train_loader = DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=4)
val_ds = CacheDataset(data=val_files,
transform=val_transforms,
cache_rate=1.0)
val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)
################################ DATASET ################################
################################ NETWORK ################################
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
################################ NETWORK ################################
################################ LOSS ################################
loss = monai.losses.DiceLoss(sigmoid=True)
################################ LOSS ################################
################################ OPT ################################
opt = torch.optim.Adam(net.parameters(), 1e-3)
################################ OPT ################################
################################ LR ################################
lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)
################################ LR ################################
################################ Evalutaion ################################
val_post_transforms = ...
val_handlers = ...
evaluator = ...
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
])
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir="./runs/",
tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": opt
},
save_interval=2,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
post_transform=train_post_transforms,
key_train_metric={
"train_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"]))
},
train_handlers=train_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
)
trainer.run()
def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4):
images = sorted(glob(os.path.join(root_dir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
train_files = [{"image": img, "label": seg} for img, seg in zip(images[:20], segs[:20])]
val_files = [{"image": img, "label": seg} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
RandCropByPosNegLabeld(
keys=["image", "label"], label_key="label", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4
),
RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]),
ToTensord(keys=["image", "label"]),
]
)
val_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys=["image", "label"]),
ToTensord(keys=["image", "label"]),
]
)
# create a training data loader
train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=num_workers)
# create a validation data loader
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0)
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)
loss = monai.losses.DiceLoss(sigmoid=True)
opt = torch.optim.Adam(net.parameters(), 1e-3)
lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)
summary_writer = SummaryWriter(log_dir=root_dir)
val_postprocessing = Compose(
[
ToTensord(keys=["pred", "label"]),
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold=0.5),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
]
)
class _TestEvalIterEvents:
def attach(self, engine):
engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed)
def _forward_completed(self, engine):
pass
val_handlers = [
StatsHandler(iteration_log=False),
TensorBoardStatsHandler(summary_writer=summary_writer, iteration_log=False),
TensorBoardImageHandler(
log_dir=root_dir, batch_transform=from_engine(["image", "label"]), output_transform=from_engine("pred")
),
CheckpointSaver(save_dir=root_dir, save_dict={"net": net}, save_key_metric=True),
_TestEvalIterEvents(),
]
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"]))},
metric_cmp_fn=lambda cur, prev: cur >= prev, # if greater or equal, treat as new best metric
val_handlers=val_handlers,
amp=bool(amp),
to_kwargs={"memory_format": torch.preserve_format},
amp_kwargs={"dtype": torch.float16 if bool(amp) else torch.float32},
)
train_postprocessing = Compose(
[
ToTensord(keys=["pred", "label"]),
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold=0.5),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
]
)
class _TestTrainIterEvents:
def attach(self, engine):
engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed)
engine.add_event_handler(IterationEvents.LOSS_COMPLETED, self._loss_completed)
engine.add_event_handler(IterationEvents.BACKWARD_COMPLETED, self._backward_completed)
engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self._model_completed)
def _forward_completed(self, engine):
pass
def _loss_completed(self, engine):
pass
def _backward_completed(self, engine):
pass
def _model_completed(self, engine):
pass
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
StatsHandler(tag_name="train_loss", output_transform=from_engine("loss", first=True)),
TensorBoardStatsHandler(
summary_writer=summary_writer, tag_name="train_loss", output_transform=from_engine("loss", first=True)
),
CheckpointSaver(save_dir=root_dir, save_dict={"net": net, "opt": opt}, save_interval=2, epoch_level=True),
_TestTrainIterEvents(),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
postprocessing=train_postprocessing,
key_train_metric={"train_acc": Accuracy(output_transform=from_engine(["pred", "label"]))},
train_handlers=train_handlers,
amp=bool(amp),
optim_set_to_none=True,
to_kwargs={"memory_format": torch.preserve_format},
amp_kwargs={"dtype": torch.float16 if bool(amp) else torch.float32},
)
trainer.run()
return evaluator.state.best_metric
def train(data_folder=".", model_folder="runs"):
"""run a training pipeline."""
images = sorted(glob.glob(os.path.join(data_folder, "*_ct.nii.gz")))
labels = sorted(glob.glob(os.path.join(data_folder, "*_seg.nii.gz")))
logging.info(
f"training: image/label ({len(images)}) folder: {data_folder}")
amp = True # auto. mixed precision
keys = ("image", "label")
train_frac, val_frac = 0.8, 0.2
n_train = int(train_frac * len(images)) + 1
n_val = min(len(images) - n_train, int(val_frac * len(images)))
logging.info(
f"training: train {n_train} val {n_val}, folder: {data_folder}")
train_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[:n_train], labels[:n_train])]
val_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[-n_val:], labels[-n_val:])]
# create a training data loader
batch_size = 2
logging.info(f"batch size {batch_size}")
train_transforms = get_xforms("train", keys)
train_ds = monai.data.CacheDataset(data=train_files,
transform=train_transforms)
train_loader = monai.data.DataLoader(
train_ds,
batch_size=batch_size,
shuffle=True,
num_workers=2,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_transforms = get_xforms("val", keys)
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(
val_ds,
batch_size=
1, # image-level batch to the sliding window method, not the window-level batch
num_workers=2,
pin_memory=torch.cuda.is_available(),
)
# create BasicUNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = get_net().to(device)
max_epochs, lr, momentum = 500, 1e-4, 0.95
logging.info(f"epochs {max_epochs}, lr {lr}, momentum {momentum}")
opt = torch.optim.Adam(net.parameters(), lr=lr)
# create evaluator (to be used to measure model quality during training
val_post_transform = monai.transforms.Compose([
AsDiscreted(keys=("pred", "label"),
argmax=(True, False),
to_onehot=True,
n_classes=2)
])
val_handlers = [
ProgressBar(),
CheckpointSaver(save_dir=model_folder,
save_dict={"net": net},
save_key_metric=True,
key_metric_n_saved=3),
]
evaluator = monai.engines.SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=get_inferer(),
post_transform=val_post_transform,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=False,
output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers,
amp=amp,
)
# evaluator as an event handler of the trainer
train_handlers = [
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
]
trainer = monai.engines.SupervisedTrainer(
device=device,
max_epochs=max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=DiceCELoss(),
inferer=get_inferer(),
key_train_metric=None,
train_handlers=train_handlers,
amp=amp,
)
trainer.run()
def create_trainer(args):
set_determinism(seed=args.seed)
multi_gpu = args.multi_gpu
local_rank = args.local_rank
if multi_gpu:
dist.init_process_group(backend="nccl", init_method="env://")
device = torch.device("cuda:{}".format(local_rank))
torch.cuda.set_device(device)
else:
device = torch.device("cuda" if args.use_gpu else "cpu")
pre_transforms = get_pre_transforms(args.roi_size, args.model_size,
args.dimensions)
click_transforms = get_click_transforms()
post_transform = get_post_transforms()
train_loader, val_loader = get_loaders(args, pre_transforms)
# define training components
network = get_network(args.network, args.channels,
args.dimensions).to(device)
if multi_gpu:
network = torch.nn.parallel.DistributedDataParallel(
network, device_ids=[local_rank], output_device=local_rank)
if args.resume:
logging.info('{}:: Loading Network...'.format(local_rank))
map_location = {"cuda:0": "cuda:{}".format(local_rank)}
network.load_state_dict(
torch.load(args.model_filepath, map_location=map_location))
# define event-handlers for engine
val_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir=args.output,
output_transform=lambda x: None),
DeepgrowStatsHandler(log_dir=args.output,
tag_name='val_dice',
image_interval=args.image_interval),
CheckpointSaver(save_dir=args.output,
save_dict={"net": network},
save_key_metric=True,
save_final=True,
save_interval=args.save_interval,
final_filename='model.pt')
]
val_handlers = val_handlers if local_rank == 0 else None
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=network,
iteration_update=Interaction(
transforms=click_transforms,
max_interactions=args.max_val_interactions,
key_probability='probability',
train=False),
inferer=SimpleInferer(),
post_transform=post_transform,
key_val_metric={
"val_dice":
MeanDice(include_background=False,
output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers)
loss_function = DiceLoss(sigmoid=True, squared_pred=True)
optimizer = torch.optim.Adam(network.parameters(), args.learning_rate)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5000,
gamma=0.1)
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator,
interval=args.val_freq,
epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir=args.output,
tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir=args.output,
save_dict={
"net": network,
"opt": optimizer,
"lr": lr_scheduler
},
save_interval=args.save_interval * 2,
save_final=True,
final_filename='checkpoint.pt'),
]
train_handlers = train_handlers if local_rank == 0 else train_handlers[:2]
trainer = SupervisedTrainer(
device=device,
max_epochs=args.epochs,
train_data_loader=train_loader,
network=network,
iteration_update=Interaction(
transforms=click_transforms,
max_interactions=args.max_train_interactions,
key_probability='probability',
train=True),
optimizer=optimizer,
loss_function=loss_function,
inferer=SimpleInferer(),
post_transform=post_transform,
amp=args.amp,
key_train_metric={
"train_dice":
MeanDice(include_background=False,
output_transform=lambda x: (x["pred"], x["label"]))
},
train_handlers=train_handlers,
)
return trainer
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
################################ DATASET ################################
# 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, 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 = [{"image": img, "label": seg} for img, seg in zip(images[:20], segs[:20])]
val_files = [{"image": img, "label": seg} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys="image"),
RandCropByPosNegLabeld(
keys=["image", "label"], label_key="label", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4
),
RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]),
ToTensord(keys=["image", "label"]),
]
)
val_transforms = Compose(
[
LoadImaged(keys=["image", "label"]),
AsChannelFirstd(keys=["image", "label"], channel_dim=-1),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
]
)
# create a training data loader
train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4)
# create a validation data loader
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0)
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
################################ DATASET ################################
################################ NETWORK ################################
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
################################ NETWORK ################################
################################ LOSS ################################
loss = monai.losses.DiceLoss(sigmoid=True)
################################ LOSS ################################
################################ OPT ################################
opt = torch.optim.Adam(net.parameters(), 1e-3)
################################ OPT ################################
################################ LR ################################
lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)
################################ LR ################################
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),
TensorBoardStatsHandler(log_dir="./runs/", output_transform=lambda x: None),
TensorBoardImageHandler(
log_dir="./runs/",
batch_transform=lambda x: (x["image"], x["label"]),
output_transform=lambda x: x["pred"],
),
CheckpointSaver(save_dir="./runs/", save_dict={"net": net}, save_key_metric=True),
]
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,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
)
train_post_transforms = Compose(
[
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
]
)
train_handlers = [
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir="./runs/", tag_name="train_loss", output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/", save_dict={"net": net, "opt": opt}, save_interval=2, epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
inferer=SimpleInferer(),
post_transform=train_post_transforms,
key_train_metric={"train_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"]))},
train_handlers=train_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training
amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False,
)
trainer.run()
def train(data_folder=".", model_folder="runs", continue_training=False):
"""run a training pipeline."""
#/== files for synthesis
path_parent = Path(
'/content/drive/My Drive/Datasets/covid19/COVID-19-20_augs_cea/')
path_synthesis = Path(
path_parent /
'CeA_BASE_grow=1_bg=-1.00_step=-1.0_scale=-1.0_seed=1.0_ch0_1=-1_ch1_16=-1_ali_thr=0.1'
)
scans_syns = os.listdir(path_synthesis)
decreasing_sequence = get_decreasing_sequence(255, splits=20)
keys2 = ("image", "label", "synthetic_lesion")
# READ THE SYTHETIC HEALTHY TEXTURE
path_synthesis_old = '/content/drive/My Drive/Datasets/covid19/results/cea_synthesis/patient0/'
texture_orig = np.load(f'{path_synthesis_old}texture.npy.npz')
texture_orig = texture_orig.f.arr_0
texture = texture_orig + np.abs(np.min(texture_orig)) + .07
texture = np.pad(texture, ((100, 100), (100, 100)), mode='reflect')
print(f'type(texture) = {type(texture)}, {np.shape(texture)}')
#==/
images = sorted(glob.glob(os.path.join(data_folder,
"*_ct.nii.gz"))[:10]) #OMM
labels = sorted(glob.glob(os.path.join(data_folder,
"*_seg.nii.gz"))[:10]) #OMM
logging.info(
f"training: image/label ({len(images)}) folder: {data_folder}")
amp = True # auto. mixed precision
keys = ("image", "label")
train_frac, val_frac = 0.8, 0.2
n_train = int(train_frac * len(images)) + 1
n_val = min(len(images) - n_train, int(val_frac * len(images)))
logging.info(
f"training: train {n_train} val {n_val}, folder: {data_folder}")
train_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[:n_train], labels[:n_train])]
val_files = [{
keys[0]: img,
keys[1]: seg
} for img, seg in zip(images[-n_val:], labels[-n_val:])]
# create a training data loader
batch_size = 1 # XX was 2
logging.info(f"batch size {batch_size}")
train_transforms = get_xforms("synthesis", keys, keys2, path_synthesis,
decreasing_sequence, scans_syns, texture)
train_ds = monai.data.CacheDataset(data=train_files,
transform=train_transforms)
train_loader = monai.data.DataLoader(
train_ds,
batch_size=batch_size,
shuffle=True,
num_workers=2,
pin_memory=torch.cuda.is_available(),
# collate_fn=pad_list_data_collate,
)
# create a validation data loader
val_transforms = get_xforms("val", keys)
val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms)
val_loader = monai.data.DataLoader(
val_ds,
batch_size=
1, # image-level batch to the sliding window method, not the window-level batch
num_workers=2,
pin_memory=torch.cuda.is_available(),
)
# create BasicUNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = get_net().to(device)
# if continue training
if continue_training:
ckpts = sorted(glob.glob(os.path.join(model_folder, "*.pt")))
ckpt = ckpts[-1]
logging.info(f"continue training using {ckpt}.")
net.load_state_dict(torch.load(ckpt, map_location=device))
# max_epochs, lr, momentum = 500, 1e-4, 0.95
max_epochs, lr, momentum = 20, 1e-4, 0.95 #OMM
logging.info(f"epochs {max_epochs}, lr {lr}, momentum {momentum}")
opt = torch.optim.Adam(net.parameters(), lr=lr)
# create evaluator (to be used to measure model quality during training
val_post_transform = monai.transforms.Compose([
AsDiscreted(keys=("pred", "label"),
argmax=(True, False),
to_onehot=True,
n_classes=2)
])
val_handlers = [
ProgressBar(),
MetricsSaver(save_dir="./metrics_val", metrics="*"),
CheckpointSaver(save_dir=model_folder,
save_dict={"net": net},
save_key_metric=True,
key_metric_n_saved=6),
]
evaluator = monai.engines.SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=get_inferer(),
post_transform=val_post_transform,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=False,
output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers,
amp=amp,
)
# evaluator as an event handler of the trainer
train_handlers = [
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
# MetricsSaver(save_dir="./metrics_train", metrics="*"),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
]
trainer = monai.engines.SupervisedTrainer(
device=device,
max_epochs=max_epochs,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=DiceCELoss(),
inferer=get_inferer(),
key_train_metric=None,
train_handlers=train_handlers,
amp=amp,
)
trainer.run()
def run_training(train_file_list, valid_file_list, config_info):
"""
Pipeline to train a dynUNet segmentation model in MONAI. It is composed of the following main blocks:
* Data Preparation: Extract the filenames and prepare the training/validation processing transforms
* Load Data: Load training and validation data to PyTorch DataLoader
* Network Preparation: Define the network, loss function, optimiser and learning rate scheduler
* MONAI Evaluator: Initialise the dynUNet evaluator, i.e. the class providing utilities to perform validation
during training. Attach handlers to save the best model on the validation set. A 2D sliding window approach
on the 3D volume is used at evaluation. The mean 3D Dice is used as validation metric.
* MONAI Trainer: Initialise the dynUNet trainer, i.e. the class providing utilities to perform the training loop.
* Run training: The MONAI trainer is run, performing training and validation during training.
Args:
train_file_list: .txt or .csv file (with no header) storing two-columns filenames for training:
image filename in the first column and segmentation filename in the second column.
The two columns should be separated by a comma.
See monaifbs/config/mock_train_file_list_for_dynUnet_training.txt for an example of the expected format.
valid_file_list: .txt or .csv file (with no header) storing two-columns filenames for validation:
image filename in the first column and segmentation filename in the second column.
The two columns should be separated by a comma.
See monaifbs/config/mock_valid_file_list_for_dynUnet_training.txt for an example of the expected format.
config_info: dict, contains configuration parameters for sampling, network and training.
See monaifbs/config/monai_dynUnet_training_config.yml for an example of the expected fields.
"""
"""
Read input and configuration parameters
"""
# print MONAI config information
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print_config()
# print to log the parameter setups
print(yaml.dump(config_info))
# extract network parameters, perform checks/set defaults if not present and print them to log
if 'seg_labels' in config_info['training'].keys():
seg_labels = config_info['training']['seg_labels']
else:
seg_labels = [1]
nr_out_channels = len(seg_labels)
print("Considering the following {} labels in the segmentation: {}".format(nr_out_channels, seg_labels))
patch_size = config_info["training"]["inplane_size"] + [1]
print("Considering patch size = {}".format(patch_size))
spacing = config_info["training"]["spacing"]
print("Bringing all images to spacing = {}".format(spacing))
if 'model_to_load' in config_info['training'].keys() and config_info['training']['model_to_load'] is not None:
model_to_load = config_info['training']['model_to_load']
if not os.path.exists(model_to_load):
raise FileNotFoundError("Cannot find model: {}".format(model_to_load))
else:
print("Loading model from {}".format(model_to_load))
else:
model_to_load = None
# set up either GPU or CPU usage
if torch.cuda.is_available():
print("\n#### GPU INFORMATION ###")
print("Using device number: {}, name: {}\n".format(torch.cuda.current_device(), torch.cuda.get_device_name()))
current_device = torch.device("cuda:0")
else:
current_device = torch.device("cpu")
print("Using device: {}".format(current_device))
# set determinism if required
if 'manual_seed' in config_info['training'].keys() and config_info['training']['manual_seed'] is not None:
seed = config_info['training']['manual_seed']
else:
seed = None
if seed is not None:
print("Using determinism with seed = {}\n".format(seed))
set_determinism(seed=seed)
"""
Setup data output directory
"""
out_model_dir = os.path.join(config_info['output']['out_dir'],
datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_' +
config_info['output']['out_postfix'])
print("Saving to directory {}\n".format(out_model_dir))
# create cache directory to store results for Persistent Dataset
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')
persistent_cache: Path = Path(out_cache_dir)
persistent_cache.mkdir(parents=True, exist_ok=True)
"""
Data preparation
"""
# Read the input files for training and validation
print("*** Loading input data for training...")
train_files = create_data_list_of_dictionaries(train_file_list)
print("Number of inputs for training = {}".format(len(train_files)))
val_files = create_data_list_of_dictionaries(valid_file_list)
print("Number of inputs for validation = {}".format(len(val_files)))
# Define MONAI processing transforms for the training data. This includes:
# - Load Nifti files and convert to format Batch x Channel x Dim1 x Dim2 x Dim3
# - CropForegroundd: Reduce the background from the MR image
# - InPlaneSpacingd: Perform in-plane resampling to the desired spacing, but preserve the resolution along the
# last direction (lowest resolution) to avoid introducing motion artefact resampling errors
# - SpatialPadd: Pad the in-plane size to the defined network input patch size [N, M] if needed
# - NormalizeIntensityd: Apply whitening
# - RandSpatialCropd: Crop a random patch from the input with size [B, C, N, M, 1]
# - SqueezeDimd: Convert the 3D patch to a 2D one as input to the network (i.e. bring it to size [B, C, N, M])
# - Apply data augmentation (RandZoomd, RandRotated, RandGaussianNoised, RandGaussianSmoothd, RandScaleIntensityd,
# RandFlipd)
# - ToTensor: convert to pytorch tensor
train_transforms = Compose(
[
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
CropForegroundd(keys=["image", "label"], source_key="image"),
InPlaneSpacingd(
keys=["image", "label"],
pixdim=spacing,
mode=("bilinear", "nearest"),
),
SpatialPadd(keys=["image", "label"], spatial_size=patch_size,
mode=["constant", "edge"]),
NormalizeIntensityd(keys=["image"], nonzero=False, channel_wise=True),
RandSpatialCropd(keys=["image", "label"], roi_size=patch_size, random_size=False),
SqueezeDimd(keys=["image", "label"], dim=-1),
RandZoomd(
keys=["image", "label"],
min_zoom=0.9,
max_zoom=1.2,
mode=("bilinear", "nearest"),
align_corners=(True, None),
prob=0.16,
),
RandRotated(keys=["image", "label"], range_x=90, range_y=90, prob=0.2,
keep_size=True, mode=["bilinear", "nearest"],
padding_mode=["zeros", "border"]),
RandGaussianNoised(keys=["image"], std=0.01, prob=0.15),
RandGaussianSmoothd(
keys=["image"],
sigma_x=(0.5, 1.15),
sigma_y=(0.5, 1.15),
sigma_z=(0.5, 1.15),
prob=0.15,
),
RandScaleIntensityd(keys=["image"], factors=0.3, prob=0.15),
RandFlipd(["image", "label"], spatial_axis=[0, 1], prob=0.5),
ToTensord(keys=["image", "label"]),
]
)
# Define MONAI processing transforms for the validation data
# - Load Nifti files and convert to format Batch x Channel x Dim1 x Dim2 x Dim3
# - CropForegroundd: Reduce the background from the MR image
# - InPlaneSpacingd: Perform in-plane resampling to the desired spacing, but preserve the resolution along the
# last direction (lowest resolution) to avoid introducing motion artefact resampling errors
# - SpatialPadd: Pad the in-plane size to the defined network input patch size [N, M] if needed
# - NormalizeIntensityd: Apply whitening
# - ToTensor: convert to pytorch tensor
# NOTE: The validation data is kept 3D as a 2D sliding window approach is used throughout the volume at inference
val_transforms = Compose(
[
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
CropForegroundd(keys=["image", "label"], source_key="image"),
InPlaneSpacingd(
keys=["image", "label"],
pixdim=spacing,
mode=("bilinear", "nearest"),
),
SpatialPadd(keys=["image", "label"], spatial_size=patch_size, mode=["constant", "edge"]),
NormalizeIntensityd(keys=["image"], nonzero=False, channel_wise=True),
ToTensord(keys=["image", "label"]),
]
)
"""
Load data
"""
# create training data loader
train_ds = PersistentDataset(data=train_files, transform=train_transforms,
cache_dir=persistent_cache)
train_loader = DataLoader(train_ds,
batch_size=config_info['training']['batch_size_train'],
shuffle=True,
num_workers=config_info['device']['num_workers'])
check_train_data = misc.first(train_loader)
print("Training data tensor shapes:")
print("Image = {}; Label = {}".format(check_train_data["image"].shape, check_train_data["label"].shape))
# create validation data loader
if config_info['training']['batch_size_valid'] != 1:
raise Exception("Batch size different from 1 at validation ar currently not supported")
val_ds = PersistentDataset(data=val_files, transform=val_transforms, cache_dir=persistent_cache)
val_loader = DataLoader(val_ds,
batch_size=1,
shuffle=False,
num_workers=config_info['device']['num_workers'])
check_valid_data = misc.first(val_loader)
print("Validation data tensor shapes (Example):")
print("Image = {}; Label = {}\n".format(check_valid_data["image"].shape, check_valid_data["label"].shape))
"""
Network preparation
"""
print("*** Preparing the network ...")
# automatically extracts the strides and kernels based on nnU-Net empirical rules
spacings = spacing[:2]
sizes = patch_size[:2]
strides, kernels = [], []
while True:
spacing_ratio = [sp / min(spacings) for sp in spacings]
stride = [2 if ratio <= 2 and size >= 8 else 1 for (ratio, size) in zip(spacing_ratio, sizes)]
kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
if all(s == 1 for s in stride):
break
sizes = [i / j for i, j in zip(sizes, stride)]
spacings = [i * j for i, j in zip(spacings, stride)]
kernels.append(kernel)
strides.append(stride)
strides.insert(0, len(spacings) * [1])
kernels.append(len(spacings) * [3])
# initialise the network
net = DynUNet(
spatial_dims=2,
in_channels=1,
out_channels=nr_out_channels,
kernel_size=kernels,
strides=strides,
upsample_kernel_size=strides[1:],
norm_name="instance",
deep_supervision=True,
deep_supr_num=2,
res_block=False,
).to(current_device)
print(net)
# define the loss function
loss_function = choose_loss_function(nr_out_channels, config_info)
# define the optimiser and the learning rate scheduler
opt = torch.optim.SGD(net.parameters(), lr=float(config_info['training']['lr']), momentum=0.95)
scheduler = torch.optim.lr_scheduler.LambdaLR(
opt, lr_lambda=lambda epoch: (1 - epoch / config_info['training']['nr_train_epochs']) ** 0.9
)
"""
MONAI evaluator
"""
print("*** Preparing the dynUNet evaluator engine...\n")
# val_post_transforms = Compose(
# [
# Activationsd(keys="pred", sigmoid=True),
# ]
# )
val_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir=os.path.join(out_model_dir, "valid"),
output_transform=lambda x: None,
global_epoch_transform=lambda x: trainer.state.iteration),
CheckpointSaver(save_dir=out_model_dir, save_dict={"net": net, "opt": opt}, save_key_metric=True,
file_prefix='best_valid'),
]
if config_info['output']['val_image_to_tensorboad']:
val_handlers.append(TensorBoardImageHandler(log_dir=os.path.join(out_model_dir, "valid"),
batch_transform=lambda x: (x["image"], x["label"]),
output_transform=lambda x: x["pred"], interval=2))
# Define customized evaluator
class DynUNetEvaluator(SupervisedEvaluator):
def _iteration(self, engine, batchdata):
inputs, targets = self.prepare_batch(batchdata)
inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device)
flip_inputs_1 = torch.flip(inputs, dims=(2,))
flip_inputs_2 = torch.flip(inputs, dims=(3,))
flip_inputs_3 = torch.flip(inputs, dims=(2, 3))
def _compute_pred():
pred = self.inferer(inputs, self.network)
# use random flipping as data augmentation at inference
flip_pred_1 = torch.flip(self.inferer(flip_inputs_1, self.network), dims=(2,))
flip_pred_2 = torch.flip(self.inferer(flip_inputs_2, self.network), dims=(3,))
flip_pred_3 = torch.flip(self.inferer(flip_inputs_3, self.network), dims=(2, 3))
return (pred + flip_pred_1 + flip_pred_2 + flip_pred_3) / 4
# execute forward computation
self.network.eval()
with torch.no_grad():
if self.amp:
with torch.cuda.amp.autocast():
predictions = _compute_pred()
else:
predictions = _compute_pred()
return {"image": inputs, "label": targets, "pred": predictions}
evaluator = DynUNetEvaluator(
device=current_device,
val_data_loader=val_loader,
network=net,
inferer=SlidingWindowInferer2D(roi_size=patch_size, sw_batch_size=4, overlap=0.0),
post_transform=None,
key_val_metric={
"Mean_dice": MeanDice(
include_background=False,
to_onehot_y=True,
mutually_exclusive=True,
output_transform=lambda x: (x["pred"], x["label"]),
)
},
val_handlers=val_handlers,
amp=False,
)
"""
MONAI trainer
"""
print("*** Preparing the dynUNet trainer engine...\n")
# train_post_transforms = Compose(
# [
# Activationsd(keys="pred", sigmoid=True),
# ]
# )
validation_every_n_epochs = config_info['training']['validation_every_n_epochs']
epoch_len = len(train_ds) // train_loader.batch_size
validation_every_n_iters = validation_every_n_epochs * epoch_len
# define event handlers for the trainer
writer_train = SummaryWriter(log_dir=os.path.join(out_model_dir, "train"))
train_handlers = [
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=validation_every_n_iters, epoch_level=False),
StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(summary_writer=writer_train,
log_dir=os.path.join(out_model_dir, "train"), tag_name="Loss",
output_transform=lambda x: x["loss"],
global_epoch_transform=lambda x: trainer.state.iteration),
CheckpointSaver(save_dir=out_model_dir, save_dict={"net": net, "opt": opt},
save_final=True,
save_interval=2, epoch_level=True,
n_saved=config_info['output']['max_nr_models_saved']),
]
if model_to_load is not None:
train_handlers.append(CheckpointLoader(load_path=model_to_load, load_dict={"net": net, "opt": opt}))
# define customized trainer
class DynUNetTrainer(SupervisedTrainer):
def _iteration(self, engine, batchdata):
inputs, targets = self.prepare_batch(batchdata)
inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device)
def _compute_loss(preds, label):
labels = [label] + [interpolate(label, pred.shape[2:]) for pred in preds[1:]]
return sum([0.5 ** i * self.loss_function(p, l) for i, (p, l) in enumerate(zip(preds, labels))])
self.network.train()
self.optimizer.zero_grad()
if self.amp and self.scaler is not None:
with torch.cuda.amp.autocast():
predictions = self.inferer(inputs, self.network)
loss = _compute_loss(predictions, targets)
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
else:
predictions = self.inferer(inputs, self.network)
loss = _compute_loss(predictions, targets).mean()
loss.backward()
self.optimizer.step()
return {"image": inputs, "label": targets, "pred": predictions, "loss": loss.item()}
trainer = DynUNetTrainer(
device=current_device,
max_epochs=config_info['training']['nr_train_epochs'],
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss_function,
inferer=SimpleInferer(),
post_transform=None,
key_train_metric=None,
train_handlers=train_handlers,
amp=False,
)
"""
Run training
"""
print("*** Run training...")
trainer.run()
print("Done!")
def main(config):
now = datetime.now().strftime("%Y%m%d-%H:%M:%S")
# path
csv_path = config['path']['csv_path']
trained_model_path = config['path'][
'trained_model_path'] # if None, trained from scratch
training_model_folder = os.path.join(
config['path']['training_model_folder'], now) # '/path/to/folder'
if not os.path.exists(training_model_folder):
os.makedirs(training_model_folder)
logdir = os.path.join(training_model_folder, 'logs')
if not os.path.exists(logdir):
os.makedirs(logdir)
# PET CT scan params
image_shape = tuple(config['preprocessing']['image_shape']) # (x, y, z)
in_channels = config['preprocessing']['in_channels']
voxel_spacing = tuple(
config['preprocessing']
['voxel_spacing']) # (4.8, 4.8, 4.8) # in millimeter, (x, y, z)
data_augment = config['preprocessing'][
'data_augment'] # True # for training dataset only
resize = config['preprocessing']['resize'] # True # not use yet
origin = config['preprocessing']['origin'] # how to set the new origin
normalize = config['preprocessing'][
'normalize'] # True # whether or not to normalize the inputs
number_class = config['preprocessing']['number_class'] # 2
# CNN params
architecture = config['model']['architecture'] # 'unet' or 'vnet'
cnn_params = config['model'][architecture]['cnn_params']
# transform list to tuple
for key, value in cnn_params.items():
if isinstance(value, list):
cnn_params[key] = tuple(value)
# Training params
epochs = config['training']['epochs']
batch_size = config['training']['batch_size']
shuffle = config['training']['shuffle']
opt_params = config['training']["optimizer"]["opt_params"]
# Get Data
DM = DataManager(csv_path=csv_path)
train_images_paths, val_images_paths, test_images_paths = DM.get_train_val_test(
wrap_with_dict=True)
# Input preprocessing
# use data augmentation for training
train_transforms = Compose([ # read img + meta info
LoadNifti(keys=["pet_img", "ct_img", "mask_img"]),
Roi2Mask(keys=['pet_img', 'mask_img'],
method='otsu',
tval=0.0,
idx_channel=0),
ResampleReshapeAlign(target_shape=image_shape,
target_voxel_spacing=voxel_spacing,
keys=['pet_img', "ct_img", 'mask_img'],
origin='head',
origin_key='pet_img'),
Sitk2Numpy(keys=['pet_img', 'ct_img', 'mask_img']),
# user can also add other random transforms
RandAffined(keys=("pet_img", "ct_img", "mask_img"),
spatial_size=None,
prob=0.4,
rotate_range=(0, np.pi / 30, np.pi / 15),
shear_range=None,
translate_range=(10, 10, 10),
scale_range=(0.1, 0.1, 0.1),
mode=("bilinear", "bilinear", "nearest"),
padding_mode="border"),
# normalize input
ScaleIntensityRanged(
keys=["pet_img"],
a_min=0.0,
a_max=25.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
ScaleIntensityRanged(
keys=["ct_img"],
a_min=-1000.0,
a_max=1000.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
# Prepare for neural network
ConcatModality(keys=['pet_img', 'ct_img']),
AddChanneld(keys=["mask_img"]), # Add channel to the first axis
ToTensord(keys=["image", "mask_img"]),
])
# without data augmentation for validation
val_transforms = Compose([ # read img + meta info
LoadNifti(keys=["pet_img", "ct_img", "mask_img"]),
Roi2Mask(keys=['pet_img', 'mask_img'],
method='otsu',
tval=0.0,
idx_channel=0),
ResampleReshapeAlign(target_shape=image_shape,
target_voxel_spacing=voxel_spacing,
keys=['pet_img', "ct_img", 'mask_img'],
origin='head',
origin_key='pet_img'),
Sitk2Numpy(keys=['pet_img', 'ct_img', 'mask_img']),
# normalize input
ScaleIntensityRanged(
keys=["pet_img"],
a_min=0.0,
a_max=25.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
ScaleIntensityRanged(
keys=["ct_img"],
a_min=-1000.0,
a_max=1000.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
# Prepare for neural network
ConcatModality(keys=['pet_img', 'ct_img']),
AddChanneld(keys=["mask_img"]), # Add channel to the first axis
ToTensord(keys=["image", "mask_img"]),
])
# create a training data loader
train_ds = monai.data.CacheDataset(data=train_images_paths,
transform=train_transforms,
cache_rate=0.5)
# use batch_size=2 to load images to generate 2 x 4 images for network training
train_loader = monai.data.DataLoader(train_ds,
batch_size=batch_size,
shuffle=shuffle,
num_workers=2)
# create a validation data loader
val_ds = monai.data.CacheDataset(data=val_images_paths,
transform=val_transforms,
cache_rate=1.0)
val_loader = monai.data.DataLoader(val_ds,
batch_size=batch_size,
num_workers=2)
# Model
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = UNet(
dimensions=3, # 3D
in_channels=in_channels,
out_channels=1,
kernel_size=5,
channels=(8, 16, 32, 64, 128),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss = monai.losses.DiceLoss(sigmoid=True, squared_pred=True)
opt = torch.optim.Adam(net.parameters(), 1e-3)
# training
val_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
])
val_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir="./runs/",
output_transform=lambda x: None),
# TensorBoardImageHandler(
# log_dir="./runs/",
# batch_transform=lambda x: (x["image"], x["label"]),
# output_transform=lambda x: x["pred"],
# ),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": opt
},
save_key_metric=True),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SimpleInferer(),
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_precision":
Precision(output_transform=lambda x: (x["pred"], x["label"])),
"val_recall":
Recall(output_transform=lambda x: (x["pred"], x["label"]))
},
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,
)
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
])
train_handlers = [
# LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir="./runs/",
tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": opt
},
save_interval=2,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
prepare_batch=lambda x: (x['image'], x['mask_img']),
inferer=SimpleInferer(),
post_transform=train_post_transforms,
key_train_metric={
"train_mean_dice":
MeanDice(include_background=True,
output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"train_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"])),
"train_precision":
Precision(output_transform=lambda x: (x["pred"], x["label"])),
"train_recall":
Recall(output_transform=lambda x: (x["pred"], x["label"]))
},
train_handlers=train_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training
amp=True if monai.config.get_torch_version_tuple() >=
(1, 6) else False,
)
trainer.run()
