def test_compute(self, input_params, expected):
data = [
{
"image": torch.tensor([[[[2.0], [3.0]]]]),
"filename": "test1"
},
{
"image": torch.tensor([[[[6.0], [8.0]]]]),
"filename": "test2"
},
]
# set up engine, PostProcessing handler works together with post_transform of engine
engine = SupervisedEvaluator(
device=torch.device("cpu:0"),
val_data_loader=data,
epoch_length=2,
network=torch.nn.PReLU(),
post_transform=Compose([Activationsd(keys="pred", sigmoid=True)]),
val_handlers=[PostProcessing(**input_params)],
)
engine.run()
torch.testing.assert_allclose(engine.state.output["pred"], expected)
filename = engine.state.output.get("filename_bak")
if filename is not None:
self.assertEqual(filename, "test2")
def test_content(self, input_args, expected_value):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataloader = [{
"image": torch.tensor([1, 2]),
"label": torch.tensor([3, 4]),
"extra1": torch.tensor([5, 6]),
"extra2": 16,
"extra3": "test",
}]
# set up engine
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=dataloader,
epoch_length=1,
network=TestNet(),
non_blocking=True,
prepare_batch=PrepareBatchExtraInput(**input_args),
decollate=False,
)
evaluator.run()
output = evaluator.state.output
assert_allclose(output["image"], torch.tensor([1, 2], device=device))
assert_allclose(output["label"], torch.tensor([3, 4], device=device))
for k, v in output["pred"].items():
if isinstance(v, torch.Tensor):
assert_allclose(v, expected_value[k].to(device))
else:
self.assertEqual(v, expected_value[k])
def test_compute(self, input_params, decollate, expected):
data = [
{"image": torch.tensor([[[[2.0], [3.0]]]]), "filename": ["test1"]},
{"image": torch.tensor([[[[6.0], [8.0]]]]), "filename": ["test2"]},
]
# set up engine, PostProcessing handler works together with postprocessing transforms of engine
engine = SupervisedEvaluator(
device=torch.device("cpu:0"),
val_data_loader=data,
epoch_length=2,
network=torch.nn.PReLU(),
postprocessing=Compose([Activationsd(keys="pred", sigmoid=True)]),
val_handlers=[PostProcessing(**input_params)],
decollate=decollate,
)
engine.run()
if isinstance(engine.state.output, list):
# test decollated list items
for o, e in zip(engine.state.output, expected):
torch.testing.assert_allclose(o["pred"], e)
filename = o.get("filename_bak")
if filename is not None:
self.assertEqual(filename, "test2")
else:
# test batch data
torch.testing.assert_allclose(engine.state.output["pred"], expected)
def test_empty_data(self):
dataloader = []
evaluator = SupervisedEvaluator(
val_data_loader=dataloader,
device=torch.device("cpu"),
epoch_length=0,
network=TestNet(),
non_blocking=False,
prepare_batch=PrepareBatchDefault(),
decollate=False,
)
evaluator.run()
def test_compute(self, data, expected):
# Set up handlers
handlers = [
# Mark with Ignite Event
MarkHandler(Events.STARTED),
# Mark with literal
MarkHandler("EPOCH_STARTED"),
# Mark with literal and providing the message
MarkHandler("EPOCH_STARTED", "Start of the epoch"),
# Define a range using one prefix (between BATCH_STARTED and BATCH_COMPLETED)
RangeHandler("Batch"),
# Define a range using a pair of events
RangeHandler((Events.STARTED, Events.COMPLETED)),
# Define a range using a pair of literals
RangeHandler(("GET_BATCH_STARTED", "GET_BATCH_COMPLETED"),
msg="Batching!"),
# Define a range using a pair of literal and events
RangeHandler(("GET_BATCH_STARTED", Events.COMPLETED)),
# Define the start of range using literal
RangePushHandler("ITERATION_STARTED"),
# Define the start of range using event
RangePushHandler(Events.ITERATION_STARTED, "Iteration 2"),
# Define the start of range using literals and providing message
RangePushHandler("EPOCH_STARTED", "Epoch 2"),
# Define the end of range using Ignite Event
RangePopHandler(Events.ITERATION_COMPLETED),
RangePopHandler(Events.EPOCH_COMPLETED),
# Define the end of range using literal
RangePopHandler("ITERATION_COMPLETED"),
# Other handlers
StatsHandler(tag_name="train",
output_transform=from_engine(["label"], first=True)),
]
# Set up an engine
engine = SupervisedEvaluator(
device=torch.device("cpu:0"),
val_data_loader=data,
epoch_length=1,
network=torch.nn.PReLU(),
postprocessing=lambda x: dict(pred=x["pred"] + 1.0),
decollate=True,
val_handlers=handlers,
)
# Run the engine
engine.run()
# Get the output from the engine
output = engine.state.output[0]
torch.testing.assert_allclose(output["pred"], expected)
def _create_evaluator(self, context: Context):
evaluator = None
if context.val_datalist and len(context.val_datalist) > 0:
val_hanlders: List = self.val_handlers(context)
if context.local_rank == 0:
val_hanlders.append(
CheckpointSaver(
save_dir=context.output_dir,
save_dict={self._model_dict_key: context.network},
save_key_metric=True,
key_metric_filename=self._key_metric_filename,
n_saved=5,
))
evaluator = SupervisedEvaluator(
device=context.device,
val_data_loader=self.val_data_loader(context),
network=context.network,
inferer=self.val_inferer(context),
postprocessing=self._validate_transforms(
self.val_post_transforms(context), "Validation", "post"),
key_val_metric=self.val_key_metric(context),
additional_metrics=self.val_additional_metrics(context),
val_handlers=val_hanlders,
iteration_update=self.val_iteration_update(context),
event_names=self.event_names(context),
)
return evaluator
def test_compute(self, dataloaders):
device = torch.device(f"cuda:{dist.get_rank()}" if torch.cuda.is_available() else "cpu")
dataloader = dataloaders[dist.get_rank()]
# set up engine
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=dataloader,
epoch_length=len(dataloader),
network=TestNet(),
non_blocking=False,
prepare_batch=PrepareBatchDefault(),
decollate=False,
)
evaluator.run()
output = evaluator.state.output
if len(dataloader) > 0:
assert_allclose(output["image"], dataloader[-1]["image"].to(device=device))
assert_allclose(output["label"], dataloader[-1]["label"].to(device=device))
def test_compute(self):
data = [
{
"image": torch.tensor([[[[2.0], [3.0]]]]),
"filename": ["test1"]
},
{
"image": torch.tensor([[[[6.0], [8.0]]]]),
"filename": ["test2"]
},
]
handlers = [
DecollateBatch(event="MODEL_COMPLETED"),
PostProcessing(transform=Compose([
Activationsd(keys="pred", sigmoid=True),
CopyItemsd(keys="filename", times=1, names="filename_bak"),
AsDiscreted(keys="pred",
threshold_values=True,
to_onehot=True,
num_classes=2),
])),
]
# set up engine, PostProcessing handler works together with postprocessing transforms of engine
engine = SupervisedEvaluator(
device=torch.device("cpu:0"),
val_data_loader=data,
epoch_length=2,
network=torch.nn.PReLU(),
# set decollate=False and execute some postprocessing first, then decollate in handlers
postprocessing=lambda x: dict(pred=x["pred"] + 1.0),
decollate=False,
val_handlers=handlers,
)
engine.run()
expected = torch.tensor([[[[1.0], [1.0]], [[0.0], [0.0]]]])
for o, e in zip(engine.state.output, expected):
torch.testing.assert_allclose(o["pred"], e)
filename = o.get("filename_bak")
if filename is not None:
self.assertEqual(filename, "test2")
def test_content(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataloader = [{
"image": torch.tensor([1, 2]),
"label": torch.tensor([3, 4]),
"extra1": torch.tensor([5, 6]),
"extra2": 16,
"extra3": "test",
}]
# set up engine
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=dataloader,
epoch_length=1,
network=TestNet(),
non_blocking=False,
prepare_batch=PrepareBatchDefault(),
decollate=False,
)
evaluator.run()
output = evaluator.state.output
assert_allclose(output["image"], torch.tensor([1, 2], device=device))
assert_allclose(output["label"], torch.tensor([3, 4], device=device))
def train(self,
train_info,
valid_info,
hyperparameters,
run_data_check=False):
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
if not run_data_check:
start_dt = datetime.datetime.now()
start_dt_string = start_dt.strftime('%d/%m/%Y %H:%M:%S')
print(f'Training started: {start_dt_string}')
# 1. Create folders to save the model
timedate_info = str(
datetime.datetime.now()).split(' ')[0] + '_' + str(
datetime.datetime.now().strftime("%H:%M:%S")).replace(
':', '-')
path_to_model = os.path.join(
self.out_dir, 'trained_models',
self.unique_name + '_' + timedate_info)
os.mkdir(path_to_model)
# 2. Load hyperparameters
learning_rate = hyperparameters['learning_rate']
weight_decay = hyperparameters['weight_decay']
total_epoch = hyperparameters['total_epoch']
multiplicator = hyperparameters['multiplicator']
batch_size = hyperparameters['batch_size']
validation_epoch = hyperparameters['validation_epoch']
validation_interval = hyperparameters['validation_interval']
H = hyperparameters['H']
L = hyperparameters['L']
# 3. Consider class imbalance
negative, positive = 0, 0
for _, label in train_info:
if int(label) == 0:
negative += 1
elif int(label) == 1:
positive += 1
pos_weight = torch.Tensor([(negative / positive)]).to(self.device)
# 4. Create train and validation loaders, batch_size = 10 for validation loader (10 central slices)
train_data = get_data_from_info(self.image_data_dir, self.seg_data_dir,
train_info)
valid_data = get_data_from_info(self.image_data_dir, self.seg_data_dir,
valid_info)
large_image_splitter(train_data, self.cache_dir)
set_determinism(seed=100)
train_trans, valid_trans = self.transformations(H, L)
train_dataset = PersistentDataset(
data=train_data[:],
transform=train_trans,
cache_dir=self.persistent_dataset_dir)
valid_dataset = PersistentDataset(
data=valid_data[:],
transform=valid_trans,
cache_dir=self.persistent_dataset_dir)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=self.pin_memory,
num_workers=self.num_workers,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
valid_loader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=self.pin_memory,
num_workers=self.num_workers,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
# Perform data checks
if run_data_check:
check_data = monai.utils.misc.first(train_loader)
print(check_data["image"].shape, check_data["label"])
for i in range(batch_size):
multi_slice_viewer(
check_data["image"][i, 0, :, :, :],
check_data["image_meta_dict"]["filename_or_obj"][i])
exit()
"""c = 1
for d in train_loader:
img = d["image"]
seg = d["seg"][0]
seg, _ = nrrd.read(seg)
img_name = d["image_meta_dict"]["filename_or_obj"][0]
print(c, "Name:", img_name, "Size:", img.nelement()*img.element_size()/1024/1024, "MB", "shape:", img.shape)
multi_slice_viewer(img[0, 0, :, :, :], d["image_meta_dict"]["filename_or_obj"][0])
#multi_slice_viewer(seg, d["image_meta_dict"]["filename_or_obj"][0])
c += 1
exit()"""
# 5. Prepare model
model = ModelCT().to(self.device)
# 6. Define loss function, optimizer and scheduler
loss_function = torch.nn.BCEWithLogitsLoss(
pos_weight) # pos_weight for class imbalance
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,
multiplicator,
last_epoch=-1)
# 7. Create post validation transforms and handlers
path_to_tensorboard = os.path.join(self.out_dir, 'tensorboard')
writer = SummaryWriter(log_dir=path_to_tensorboard)
valid_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
])
valid_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(summary_writer=writer,
output_transform=lambda x: None),
CheckpointSaver(save_dir=path_to_model,
save_dict={"model": model},
save_key_metric=True),
MetricsSaver(save_dir=path_to_model,
metrics=['Valid_AUC', 'Valid_ACC']),
]
# 8. Create validatior
discrete = AsDiscrete(threshold_values=True)
evaluator = SupervisedEvaluator(
device=self.device,
val_data_loader=valid_loader,
network=model,
post_transform=valid_post_transforms,
key_val_metric={
"Valid_AUC":
ROCAUC(output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"Valid_Accuracy":
Accuracy(output_transform=lambda x:
(discrete(x["pred"]), x["label"]))
},
val_handlers=valid_handlers,
amp=self.amp,
)
# 9. Create trainer
# Loss function does the last sigmoid, so we dont need it here.
train_post_transforms = Compose([
# Empty
])
logger = MetricLogger(evaluator=evaluator)
train_handlers = [
logger,
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True),
ValidationHandlerCT(validator=evaluator,
start=validation_epoch,
interval=validation_interval,
epoch_level=True),
StatsHandler(tag_name="loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(summary_writer=writer,
tag_name="Train_Loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir=path_to_model,
save_dict={
"model": model,
"opt": optimizer
},
save_interval=1,
n_saved=1),
]
trainer = SupervisedTrainer(
device=self.device,
max_epochs=total_epoch,
train_data_loader=train_loader,
network=model,
optimizer=optimizer,
loss_function=loss_function,
post_transform=train_post_transforms,
train_handlers=train_handlers,
amp=self.amp,
)
# 10. Run trainer
trainer.run()
# 11. Save results
np.save(path_to_model + '/AUCS.npy',
np.array(logger.metrics['Valid_AUC']))
np.save(path_to_model + '/ACCS.npy',
np.array(logger.metrics['Valid_ACC']))
np.save(path_to_model + '/LOSSES.npy', np.array(logger.loss))
np.save(path_to_model + '/PARAMETERS.npy', np.array(hyperparameters))
return path_to_model
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 run_inference_test(root_dir, model_file, device="cuda:0", amp=False):
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", 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=f"{model_file}", load_dict={"net": net}),
SegmentationSaver(
output_dir=root_dir,
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,
amp=True if amp else False,
)
evaluator.run()
return evaluator.state.best_metric
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
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 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)
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", 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="./runs/net_key_metric=0.9101.pth",
load_dict={"net": net}),
SegmentationSaver(
output_dir="./runs/",
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()
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(
[
LoadImaged(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}")
torch.cuda.set_device(device)
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=[device])
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.pt",
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 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(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(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():
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 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)
# 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(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)]
# model file path
model_file = glob("./runs/net_key_metric*")[0]
# 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 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" 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)
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=model_file, 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,
# 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()
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()
