def test_train_timing(self):
images = sorted(glob(os.path.join(self.data_dir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(self.data_dir, "seg*.nii.gz")))
train_files = [{
"image": img,
"label": seg
} for img, seg in zip(images[:32], segs[:32])]
val_files = [{
"image": img,
"label": seg
} for img, seg in zip(images[-9:], segs[-9:])]
device = torch.device("cuda:0")
# define transforms for train and validation
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")),
ScaleIntensityd(keys="image"),
CropForegroundd(keys=["image", "label"], source_key="image"),
# pre-compute foreground and background indexes
# and cache them to accelerate training
FgBgToIndicesd(keys="label", fg_postfix="_fg", bg_postfix="_bg"),
# change to execute transforms with Tensor data
EnsureTyped(keys=["image", "label"]),
# move the data to GPU and cache to avoid CPU -> GPU sync in every epoch
ToDeviced(keys=["image", "label"], device=device),
# randomly crop out patch samples from big
# image based on pos / neg ratio
# the image centers of negative samples
# must be in valid image area
RandCropByPosNegLabeld(
keys=["image", "label"],
label_key="label",
spatial_size=(64, 64, 64),
pos=1,
neg=1,
num_samples=4,
fg_indices_key="label_fg",
bg_indices_key="label_bg",
),
RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=[1, 2]),
RandAxisFlipd(keys=["image", "label"], prob=0.5),
RandRotate90d(keys=["image", "label"],
prob=0.5,
spatial_axes=(1, 2)),
RandZoomd(keys=["image", "label"],
prob=0.5,
min_zoom=0.8,
max_zoom=1.2,
keep_size=True),
RandRotated(
keys=["image", "label"],
prob=0.5,
range_x=np.pi / 4,
mode=("bilinear", "nearest"),
align_corners=True,
dtype=np.float64,
),
RandAffined(keys=["image", "label"],
prob=0.5,
rotate_range=np.pi / 2,
mode=("bilinear", "nearest")),
RandGaussianNoised(keys="image", prob=0.5),
RandStdShiftIntensityd(keys="image",
prob=0.5,
factors=0.05,
nonzero=True),
])
val_transforms = Compose([
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Spacingd(keys=["image", "label"],
pixdim=(1.0, 1.0, 1.0),
mode=("bilinear", "nearest")),
ScaleIntensityd(keys="image"),
CropForegroundd(keys=["image", "label"], source_key="image"),
EnsureTyped(keys=["image", "label"]),
# move the data to GPU and cache to avoid CPU -> GPU sync in every epoch
ToDeviced(keys=["image", "label"], device=device),
])
max_epochs = 5
learning_rate = 2e-4
val_interval = 1 # do validation for every epoch
# set CacheDataset, ThreadDataLoader and DiceCE loss for MONAI fast training
train_ds = CacheDataset(data=train_files,
transform=train_transforms,
cache_rate=1.0,
num_workers=8)
val_ds = CacheDataset(data=val_files,
transform=val_transforms,
cache_rate=1.0,
num_workers=5)
# disable multi-workers because `ThreadDataLoader` works with multi-threads
train_loader = ThreadDataLoader(train_ds,
num_workers=0,
batch_size=4,
shuffle=True)
val_loader = ThreadDataLoader(val_ds, num_workers=0, batch_size=1)
loss_function = DiceCELoss(to_onehot_y=True,
softmax=True,
squared_pred=True,
batch=True)
model = UNet(
spatial_dims=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(device)
# Novograd paper suggests to use a bigger LR than Adam,
# because Adam does normalization by element-wise second moments
optimizer = Novograd(model.parameters(), learning_rate * 10)
scaler = torch.cuda.amp.GradScaler()
post_pred = Compose(
[EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
best_metric = -1
total_start = time.time()
for epoch in range(max_epochs):
epoch_start = time.time()
print("-" * 10)
print(f"epoch {epoch + 1}/{max_epochs}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step_start = time.time()
step += 1
optimizer.zero_grad()
# set AMP for training
with torch.cuda.amp.autocast():
outputs = model(batch_data["image"])
loss = loss_function(outputs, batch_data["label"])
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
epoch_loss += loss.item()
epoch_len = math.ceil(len(train_ds) / train_loader.batch_size)
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}"
f" step time: {(time.time() - step_start):.4f}")
epoch_loss /= step
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
for val_data in val_loader:
roi_size = (96, 96, 96)
sw_batch_size = 4
# set AMP for validation
with torch.cuda.amp.autocast():
val_outputs = sliding_window_inference(
val_data["image"], roi_size, sw_batch_size,
model)
val_outputs = [
post_pred(i) for i in decollate_batch(val_outputs)
]
val_labels = [
post_label(i)
for i in decollate_batch(val_data["label"])
]
dice_metric(y_pred=val_outputs, y=val_labels)
metric = dice_metric.aggregate().item()
dice_metric.reset()
if metric > best_metric:
best_metric = metric
print(
f"epoch: {epoch + 1} current mean dice: {metric:.4f}, best mean dice: {best_metric:.4f}"
)
print(
f"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}"
)
total_time = time.time() - total_start
print(
f"train completed, best_metric: {best_metric:.4f} total time: {total_time:.4f}"
)
# test expected metrics
self.assertGreater(best_metric, 0.95)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
]
)
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = UNet(
spatial_dims=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
save_image = SaveImage(output_dir="tempdir", output_ext=".nii.gz", output_postfix="seg")
def _sliding_window_processor(engine, batch):
net.eval()
with torch.no_grad():
val_images, val_labels = batch["img"].to(device), batch["seg"].to(device)
seg_probs = sliding_window_inference(val_images, roi_size, sw_batch_size, net)
seg_probs = [post_trans(i) for i in decollate_batch(seg_probs)]
val_data = decollate_batch(batch["img_meta_dict"])
for seg_prob, data in zip(seg_probs, val_data):
save_image(seg_prob, data)
return seg_probs, val_labels
evaluator = Engine(_sliding_window_processor)
# add evaluation metric to the evaluator engine
MeanDice().attach(evaluator, "Mean_Dice")
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't need to print loss for evaluator, so just print metrics, user can also customize print functions
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# the model was trained by "unet_training_dict" example
CheckpointLoader(load_path="./runs_dict/net_checkpoint_50.pt", load_dict={"net": net}).attach(evaluator)
# sliding window inference for one image at every iteration
val_loader = DataLoader(
val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()
)
state = evaluator.run(val_loader)
print(state)
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 = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
])
# create a evaluation data loader
val_ds = Dataset(data=val_files, transform=val_transforms)
# create a evaluation data sampler
val_sampler = DistributedSampler(dataset=val_ds,
even_divisible=False,
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)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
# create UNet, DiceLoss and Adam optimizer
device = torch.device(f"cuda:{args.local_rank}")
torch.cuda.set_device(device)
model = 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)
# wrap the model with DistributedDataParallel module
model = DistributedDataParallel(model, device_ids=[device])
# config mapping to expected GPU device
map_location = {"cuda:0": f"cuda:{args.local_rank}"}
# load model parameters to GPU device
model.load_state_dict(
torch.load("final_model.pth", map_location=map_location))
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
dice_metric(y_pred=val_outputs, y=val_labels)
metric = dice_metric.aggregate().item()
dice_metric.reset()
if dist.get_rank() == 0:
print("evaluation metric:", metric)
dist.destroy_process_group()
def main_worker(args):
# disable logging for processes except 0 on every node
if args.local_rank != 0:
f = open(os.devnull, "w")
sys.stdout = sys.stderr = f
if not os.path.exists(args.dir):
raise FileNotFoundError(f"missing directory {args.dir}")
# initialize the distributed training process, every GPU runs in a process
dist.init_process_group(backend="nccl", init_method="env://")
device = torch.device(f"cuda:{args.local_rank}")
torch.cuda.set_device(device)
# use amp to accelerate training
scaler = torch.cuda.amp.GradScaler()
torch.backends.cudnn.benchmark = True
total_start = time.time()
train_transforms = Compose([
# load 4 Nifti images and stack them together
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys="image"),
ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=(1.0, 1.0, 1.0),
mode=("bilinear", "nearest"),
),
EnsureTyped(keys=["image", "label"]),
ToDeviced(keys=["image", "label"], device=device),
RandSpatialCropd(keys=["image", "label"],
roi_size=[224, 224, 144],
random_size=False),
RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=0),
RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=1),
RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=2),
NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
RandScaleIntensityd(keys="image", factors=0.1, prob=0.5),
RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5),
])
# create a training data loader
train_ds = BratsCacheDataset(
root_dir=args.dir,
transform=train_transforms,
section="training",
num_workers=4,
cache_rate=args.cache_rate,
shuffle=True,
)
# ThreadDataLoader can be faster if no IO operations when caching all the data in memory
train_loader = ThreadDataLoader(train_ds,
num_workers=0,
batch_size=args.batch_size,
shuffle=True)
# validation transforms and dataset
val_transforms = Compose([
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys="image"),
ConvertToMultiChannelBasedOnBratsClassesd(keys="label"),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=(1.0, 1.0, 1.0),
mode=("bilinear", "nearest"),
),
NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True),
EnsureTyped(keys=["image", "label"]),
ToDeviced(keys=["image", "label"], device=device),
])
val_ds = BratsCacheDataset(
root_dir=args.dir,
transform=val_transforms,
section="validation",
num_workers=4,
cache_rate=args.cache_rate,
shuffle=False,
)
# ThreadDataLoader can be faster if no IO operations when caching all the data in memory
val_loader = ThreadDataLoader(val_ds,
num_workers=0,
batch_size=args.batch_size,
shuffle=False)
# create network, loss function and optimizer
if args.network == "SegResNet":
model = SegResNet(
blocks_down=[1, 2, 2, 4],
blocks_up=[1, 1, 1],
init_filters=16,
in_channels=4,
out_channels=3,
dropout_prob=0.0,
).to(device)
else:
model = UNet(
spatial_dims=3,
in_channels=4,
out_channels=3,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss_function = DiceFocalLoss(
smooth_nr=1e-5,
smooth_dr=1e-5,
squared_pred=True,
to_onehot_y=False,
sigmoid=True,
batch=True,
)
optimizer = Novograd(model.parameters(), lr=args.lr)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=args.epochs)
# wrap the model with DistributedDataParallel module
model = DistributedDataParallel(model, device_ids=[device])
dice_metric = DiceMetric(include_background=True, reduction="mean")
dice_metric_batch = DiceMetric(include_background=True,
reduction="mean_batch")
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
# start a typical PyTorch training
best_metric = -1
best_metric_epoch = -1
print(f"time elapsed before training: {time.time() - total_start}")
train_start = time.time()
for epoch in range(args.epochs):
epoch_start = time.time()
print("-" * 10)
print(f"epoch {epoch + 1}/{args.epochs}")
epoch_loss = train(train_loader, model, loss_function, optimizer,
lr_scheduler, scaler)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % args.val_interval == 0:
metric, metric_tc, metric_wt, metric_et = evaluate(
model, val_loader, dice_metric, dice_metric_batch, post_trans)
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
if dist.get_rank() == 0:
torch.save(model.state_dict(), "best_metric_model.pth")
print(
f"current epoch: {epoch + 1} current mean dice: {metric:.4f}"
f" tc: {metric_tc:.4f} wt: {metric_wt:.4f} et: {metric_et:.4f}"
f"\nbest mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
print(
f"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}"
)
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch},"
f" total train time: {(time.time() - train_start):.4f}")
dist.destroy_process_group()
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_2d(128, 128, num_seg_classes=1)
Image.fromarray((im * 255).astype("uint8")).save(
os.path.join(tempdir, f"img{i:d}.png"))
Image.fromarray((seg * 255).astype("uint8")).save(
os.path.join(tempdir, f"seg{i:d}.png"))
images = sorted(glob(os.path.join(tempdir, "img*.png")))
segs = sorted(glob(os.path.join(tempdir, "seg*.png")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AddChanneld(keys=["img", "seg"]),
ScaleIntensityd(keys=["img", "seg"]),
EnsureTyped(keys=["img", "seg"]),
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
saver = SaveImage(output_dir="./output",
output_ext=".png",
output_postfix="seg")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(
spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model.load_state_dict(
torch.load("best_metric_model_segmentation2d_dict.pth"))
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
val_labels = decollate_batch(val_labels)
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
for val_output in val_outputs:
saver(val_output)
# aggregate the final mean dice result
print("evaluation metric:", dice_metric.aggregate().item())
# reset the status
dice_metric.reset()
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
norm=Norm.BATCH,
).to(device)
loss_function = DiceCELoss(
to_onehot_y=True, softmax=True, squared_pred=True, batch=True
)
optimizer = Novograd(model.parameters(), learning_rate * 10)
scaler = torch.cuda.amp.GradScaler()
dice_metric = DiceMetric(
include_background=True, reduction="mean", get_not_nans=False
)
post_pred = Compose(
[EnsureType(), AsDiscrete(argmax=True, to_onehot=2)]
)
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])
best_metric = -1
best_metric_epoch = -1
best_metrics_epochs_and_time = [[], [], []]
epoch_loss_values = []
metric_values = []
epoch_times = []
total_start = time.time()
writer = SummaryWriter(log_dir=out_dir)
with torch.autograd.profiler.emit_nvtx():
for epoch in range(max_epochs):
epoch_start = time.time()
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
# the path of ixi IXI-T1 dataset
data_path = os.sep.join(
[".", "workspace", "data", "medical", "ixi", "IXI-T1"])
images = [
"IXI607-Guys-1097-T1.nii.gz",
"IXI175-HH-1570-T1.nii.gz",
"IXI385-HH-2078-T1.nii.gz",
"IXI344-Guys-0905-T1.nii.gz",
"IXI409-Guys-0960-T1.nii.gz",
"IXI584-Guys-1129-T1.nii.gz",
"IXI253-HH-1694-T1.nii.gz",
"IXI092-HH-1436-T1.nii.gz",
"IXI574-IOP-1156-T1.nii.gz",
"IXI585-Guys-1130-T1.nii.gz",
]
images = [os.sep.join([data_path, f]) for f in images]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
# define transforms for image
val_transforms = Compose(
[ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
EnsureType()])
# define image dataset
val_ds = ImageDataset(image_files=images,
labels=labels,
transform=val_transforms,
image_only=False)
# create DenseNet121
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = monai.networks.nets.DenseNet121(spatial_dims=3,
in_channels=1,
out_channels=2).to(device)
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch[0], batch[1]), device, non_blocking)
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net,
val_metrics,
device,
True,
prepare_batch=prepare_batch)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# for the array data format, assume the 3rd item of batch data is the meta_data
prediction_saver = ClassificationSaver(
output_dir="tempdir",
batch_transform=lambda batch: batch[2],
output_transform=lambda output: output[0].argmax(1),
)
prediction_saver.attach(evaluator)
# the model was trained by "densenet_training_array" example
CheckpointLoader(load_path="./runs_array/net_checkpoint_20.pt",
load_dict={
"net": net
}).attach(evaluator)
# create a validation data loader
val_loader = DataLoader(val_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
state = evaluator.run(val_loader)
print(state)
def evaluate(args):
# initialize Horovod library
hvd.init()
# Horovod limits CPU threads to be used per worker
torch.set_num_threads(1)
if hvd.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"))
images = sorted(glob(os.path.join(args.dir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(args.dir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
]
)
# 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, num_replicas=hvd.size(), rank=hvd.rank())
# when supported, use "forkserver" to spawn dataloader workers instead of "fork" to prevent
# issues with Infiniband implementations that are not fork-safe
multiprocessing_context = None
if hasattr(mp, "_supports_context") and mp._supports_context and "forkserver" in mp.get_all_start_methods():
multiprocessing_context = "forkserver"
# 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,
multiprocessing_context=multiprocessing_context,
)
dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
# create UNet, DiceLoss and Adam optimizer
device = torch.device(f"cuda:{hvd.local_rank()}")
torch.cuda.set_device(device)
model = 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)
if hvd.rank() == 0:
# load model parameters for evaluation
model.load_state_dict(torch.load("final_model.pth"))
# Horovod broadcasts parameters
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(device), val_data["seg"].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
dice_metric(y_pred=val_outputs, y=val_labels)
metric = dice_metric.aggregate().item()
dice_metric.reset()
if hvd.rank() == 0:
print("evaluation metric:", metric)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask pairs
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_3d(128,
128,
128,
num_seg_classes=1,
channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"img{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
train_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
RandCropByPosNegLabeld(keys=["img", "seg"],
label_key="seg",
spatial_size=[96, 96, 96],
pos=1,
neg=1,
num_samples=4),
RandRotate90d(keys=["img", "seg"], prob=0.5, spatial_axes=[0, 2]),
EnsureTyped(keys=["img", "seg"]),
])
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
collate_fn=list_data_collate)
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["seg"].shape)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = DataLoader(
train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = 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_function = monai.losses.DiceLoss(sigmoid=True)
optimizer = torch.optim.Adam(model.parameters(), 1e-3)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
for epoch in range(5):
print("-" * 10)
print(f"epoch {epoch + 1}/{5}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["img"].to(
device), batch_data["seg"].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
val_images = None
val_labels = None
val_outputs = None
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, model)
val_outputs = [
post_trans(i) for i in decollate_batch(val_outputs)
]
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
# aggregate the final mean dice result
metric = dice_metric.aggregate().item()
# reset the status for next validation round
dice_metric.reset()
metric_values.append(metric)
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(),
"best_metric_model_segmentation3d_dict.pth")
print("saved new best metric model")
print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}"
.format(epoch + 1, metric, best_metric, best_metric_epoch))
writer.add_scalar("val_mean_dice", metric, epoch + 1)
# plot the last model output as GIF image in TensorBoard with the corresponding image and label
plot_2d_or_3d_image(val_images,
epoch + 1,
writer,
index=0,
tag="image")
plot_2d_or_3d_image(val_labels,
epoch + 1,
writer,
index=0,
tag="label")
plot_2d_or_3d_image(val_outputs,
epoch + 1,
writer,
index=0,
tag="output")
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
def main():
parser = argparse.ArgumentParser(description="training")
parser.add_argument(
"--checkpoint",
type=str,
default=None,
help="checkpoint full path",
)
parser.add_argument(
"--factor_ram_cost",
default=0.0,
type=float,
help="factor to determine RAM cost in the searched architecture",
)
parser.add_argument(
"--fold",
action="store",
required=True,
help="fold index in N-fold cross-validation",
)
parser.add_argument(
"--json",
action="store",
required=True,
help="full path of .json file",
)
parser.add_argument(
"--json_key",
action="store",
required=True,
help="selected key in .json data list",
)
parser.add_argument(
"--local_rank",
required=int,
help="local process rank",
)
parser.add_argument(
"--num_folds",
action="store",
required=True,
help="number of folds in cross-validation",
)
parser.add_argument(
"--output_root",
action="store",
required=True,
help="output root",
)
parser.add_argument(
"--root",
action="store",
required=True,
help="data root",
)
args = parser.parse_args()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
if not os.path.exists(args.output_root):
os.makedirs(args.output_root, exist_ok=True)
amp = True
determ = True
factor_ram_cost = args.factor_ram_cost
fold = int(args.fold)
input_channels = 1
learning_rate = 0.025
learning_rate_arch = 0.001
learning_rate_milestones = np.array([0.4, 0.8])
num_images_per_batch = 1
num_epochs = 1430 # around 20k iteration
num_epochs_per_validation = 100
num_epochs_warmup = 715
num_folds = int(args.num_folds)
num_patches_per_image = 1
num_sw_batch_size = 6
output_classes = 3
overlap_ratio = 0.625
patch_size = (96, 96, 96)
patch_size_valid = (96, 96, 96)
spacing = [1.0, 1.0, 1.0]
print("factor_ram_cost", factor_ram_cost)
# deterministic training
if determ:
set_determinism(seed=0)
# initialize the distributed training process, every GPU runs in a process
dist.init_process_group(backend="nccl", init_method="env://")
# dist.barrier()
world_size = dist.get_world_size()
with open(args.json, "r") as f:
json_data = json.load(f)
split = len(json_data[args.json_key]) // num_folds
list_train = json_data[args.json_key][:(
split * fold)] + json_data[args.json_key][(split * (fold + 1)):]
list_valid = json_data[args.json_key][(split * fold):(split * (fold + 1))]
# training data
files = []
for _i in range(len(list_train)):
str_img = os.path.join(args.root, list_train[_i]["image"])
str_seg = os.path.join(args.root, list_train[_i]["label"])
if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
continue
files.append({"image": str_img, "label": str_seg})
train_files = files
random.shuffle(train_files)
train_files_w = train_files[:len(train_files) // 2]
train_files_w = partition_dataset(data=train_files_w,
shuffle=True,
num_partitions=world_size,
even_divisible=True)[dist.get_rank()]
print("train_files_w:", len(train_files_w))
train_files_a = train_files[len(train_files) // 2:]
train_files_a = partition_dataset(data=train_files_a,
shuffle=True,
num_partitions=world_size,
even_divisible=True)[dist.get_rank()]
print("train_files_a:", len(train_files_a))
# validation data
files = []
for _i in range(len(list_valid)):
str_img = os.path.join(args.root, list_valid[_i]["image"])
str_seg = os.path.join(args.root, list_valid[_i]["label"])
if (not os.path.exists(str_img)) or (not os.path.exists(str_seg)):
continue
files.append({"image": str_img, "label": str_seg})
val_files = files
val_files = partition_dataset(data=val_files,
shuffle=False,
num_partitions=world_size,
even_divisible=False)[dist.get_rank()]
print("val_files:", len(val_files))
# network architecture
device = torch.device(f"cuda:{args.local_rank}")
torch.cuda.set_device(device)
train_transforms = Compose([
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(keys=["image", "label"],
pixdim=spacing,
mode=("bilinear", "nearest"),
align_corners=(True, True)),
CastToTyped(keys=["image"], dtype=(torch.float32)),
ScaleIntensityRanged(keys=["image"],
a_min=-87.0,
a_max=199.0,
b_min=0.0,
b_max=1.0,
clip=True),
CastToTyped(keys=["image", "label"], dtype=(np.float16, np.uint8)),
CopyItemsd(keys=["label"], times=1, names=["label4crop"]),
Lambdad(
keys=["label4crop"],
func=lambda x: np.concatenate(tuple([
ndimage.binary_dilation(
(x == _k).astype(x.dtype), iterations=48).astype(x.dtype)
for _k in range(output_classes)
]),
axis=0),
overwrite=True,
),
EnsureTyped(keys=["image", "label"]),
CastToTyped(keys=["image"], dtype=(torch.float32)),
SpatialPadd(keys=["image", "label", "label4crop"],
spatial_size=patch_size,
mode=["reflect", "constant", "constant"]),
RandCropByLabelClassesd(keys=["image", "label"],
label_key="label4crop",
num_classes=output_classes,
ratios=[
1,
] * output_classes,
spatial_size=patch_size,
num_samples=num_patches_per_image),
Lambdad(keys=["label4crop"], func=lambda x: 0),
RandRotated(keys=["image", "label"],
range_x=0.3,
range_y=0.3,
range_z=0.3,
mode=["bilinear", "nearest"],
prob=0.2),
RandZoomd(keys=["image", "label"],
min_zoom=0.8,
max_zoom=1.2,
mode=["trilinear", "nearest"],
align_corners=[True, None],
prob=0.16),
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=0.5),
RandShiftIntensityd(keys=["image"], offsets=0.1, prob=0.5),
RandGaussianNoised(keys=["image"], std=0.01, prob=0.15),
RandFlipd(keys=["image", "label"], spatial_axis=0, prob=0.5),
RandFlipd(keys=["image", "label"], spatial_axis=1, prob=0.5),
RandFlipd(keys=["image", "label"], spatial_axis=2, prob=0.5),
CastToTyped(keys=["image", "label"],
dtype=(torch.float32, torch.uint8)),
ToTensord(keys=["image", "label"]),
])
val_transforms = Compose([
LoadImaged(keys=["image", "label"]),
EnsureChannelFirstd(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(keys=["image", "label"],
pixdim=spacing,
mode=("bilinear", "nearest"),
align_corners=(True, True)),
CastToTyped(keys=["image"], dtype=(torch.float32)),
ScaleIntensityRanged(keys=["image"],
a_min=-87.0,
a_max=199.0,
b_min=0.0,
b_max=1.0,
clip=True),
CastToTyped(keys=["image", "label"], dtype=(np.float32, np.uint8)),
EnsureTyped(keys=["image", "label"]),
ToTensord(keys=["image", "label"])
])
train_ds_a = monai.data.CacheDataset(data=train_files_a,
transform=train_transforms,
cache_rate=1.0,
num_workers=8)
train_ds_w = monai.data.CacheDataset(data=train_files_w,
transform=train_transforms,
cache_rate=1.0,
num_workers=8)
val_ds = monai.data.CacheDataset(data=val_files,
transform=val_transforms,
cache_rate=1.0,
num_workers=2)
# monai.data.Dataset can be used as alternatives when debugging or RAM space is limited.
# train_ds_a = monai.data.Dataset(data=train_files_a, transform=train_transforms)
# train_ds_w = monai.data.Dataset(data=train_files_w, transform=train_transforms)
# val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
train_loader_a = ThreadDataLoader(train_ds_a,
num_workers=0,
batch_size=num_images_per_batch,
shuffle=True)
train_loader_w = ThreadDataLoader(train_ds_w,
num_workers=0,
batch_size=num_images_per_batch,
shuffle=True)
val_loader = ThreadDataLoader(val_ds,
num_workers=0,
batch_size=1,
shuffle=False)
# DataLoader can be used as alternatives when ThreadDataLoader is less efficient.
# train_loader_a = DataLoader(train_ds_a, batch_size=num_images_per_batch, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())
# train_loader_w = DataLoader(train_ds_w, batch_size=num_images_per_batch, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())
# val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=2, pin_memory=torch.cuda.is_available())
dints_space = monai.networks.nets.TopologySearch(
channel_mul=0.5,
num_blocks=12,
num_depths=4,
use_downsample=True,
device=device,
)
model = monai.networks.nets.DiNTS(
dints_space=dints_space,
in_channels=input_channels,
num_classes=output_classes,
use_downsample=True,
)
model = model.to(device)
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
post_pred = Compose(
[EnsureType(),
AsDiscrete(argmax=True, to_onehot=output_classes)])
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=output_classes)])
# loss function
loss_func = monai.losses.DiceCELoss(
include_background=False,
to_onehot_y=True,
softmax=True,
squared_pred=True,
batch=True,
smooth_nr=0.00001,
smooth_dr=0.00001,
)
# optimizer
optimizer = torch.optim.SGD(model.weight_parameters(),
lr=learning_rate * world_size,
momentum=0.9,
weight_decay=0.00004)
arch_optimizer_a = torch.optim.Adam([dints_space.log_alpha_a],
lr=learning_rate_arch * world_size,
betas=(0.5, 0.999),
weight_decay=0.0)
arch_optimizer_c = torch.optim.Adam([dints_space.log_alpha_c],
lr=learning_rate_arch * world_size,
betas=(0.5, 0.999),
weight_decay=0.0)
print()
if torch.cuda.device_count() > 1:
if dist.get_rank() == 0:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DistributedDataParallel(model,
device_ids=[device],
find_unused_parameters=True)
if args.checkpoint != None and os.path.isfile(args.checkpoint):
print("[info] fine-tuning pre-trained checkpoint {0:s}".format(
args.checkpoint))
model.load_state_dict(torch.load(args.checkpoint, map_location=device))
torch.cuda.empty_cache()
else:
print("[info] training from scratch")
# amp
if amp:
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
if dist.get_rank() == 0:
print("[info] amp enabled")
# start a typical PyTorch training
val_interval = num_epochs_per_validation
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
idx_iter = 0
metric_values = list()
if dist.get_rank() == 0:
writer = SummaryWriter(
log_dir=os.path.join(args.output_root, "Events"))
with open(os.path.join(args.output_root, "accuracy_history.csv"),
"a") as f:
f.write("epoch\tmetric\tloss\tlr\ttime\titer\n")
dataloader_a_iterator = iter(train_loader_a)
start_time = time.time()
for epoch in range(num_epochs):
decay = 0.5**np.sum([
(epoch - num_epochs_warmup) /
(num_epochs - num_epochs_warmup) > learning_rate_milestones
])
lr = learning_rate * decay
for param_group in optimizer.param_groups:
param_group["lr"] = lr
if dist.get_rank() == 0:
print("-" * 10)
print(f"epoch {epoch + 1}/{num_epochs}")
print("learning rate is set to {}".format(lr))
model.train()
epoch_loss = 0
loss_torch = torch.zeros(2, dtype=torch.float, device=device)
epoch_loss_arch = 0
loss_torch_arch = torch.zeros(2, dtype=torch.float, device=device)
step = 0
for batch_data in train_loader_w:
step += 1
inputs, labels = batch_data["image"].to(
device), batch_data["label"].to(device)
if world_size == 1:
for _ in model.weight_parameters():
_.requires_grad = True
else:
for _ in model.module.weight_parameters():
_.requires_grad = True
dints_space.log_alpha_a.requires_grad = False
dints_space.log_alpha_c.requires_grad = False
optimizer.zero_grad()
if amp:
with autocast():
outputs = model(inputs)
if output_classes == 2:
loss = loss_func(torch.flip(outputs, dims=[1]),
1 - labels)
else:
loss = loss_func(outputs, labels)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
outputs = model(inputs)
if output_classes == 2:
loss = loss_func(torch.flip(outputs, dims=[1]), 1 - labels)
else:
loss = loss_func(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
loss_torch[0] += loss.item()
loss_torch[1] += 1.0
epoch_len = len(train_loader_w)
idx_iter += 1
if dist.get_rank() == 0:
print("[{0}] ".format(str(datetime.now())[:19]) +
f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
if epoch < num_epochs_warmup:
continue
try:
sample_a = next(dataloader_a_iterator)
except StopIteration:
dataloader_a_iterator = iter(train_loader_a)
sample_a = next(dataloader_a_iterator)
inputs_search, labels_search = sample_a["image"].to(
device), sample_a["label"].to(device)
if world_size == 1:
for _ in model.weight_parameters():
_.requires_grad = False
else:
for _ in model.module.weight_parameters():
_.requires_grad = False
dints_space.log_alpha_a.requires_grad = True
dints_space.log_alpha_c.requires_grad = True
# linear increase topology and RAM loss
entropy_alpha_c = torch.tensor(0.).to(device)
entropy_alpha_a = torch.tensor(0.).to(device)
ram_cost_full = torch.tensor(0.).to(device)
ram_cost_usage = torch.tensor(0.).to(device)
ram_cost_loss = torch.tensor(0.).to(device)
topology_loss = torch.tensor(0.).to(device)
probs_a, arch_code_prob_a = dints_space.get_prob_a(child=True)
entropy_alpha_a = -((probs_a) * torch.log(probs_a + 1e-5)).mean()
entropy_alpha_c = -(F.softmax(dints_space.log_alpha_c, dim=-1) * \
F.log_softmax(dints_space.log_alpha_c, dim=-1)).mean()
topology_loss = dints_space.get_topology_entropy(probs_a)
ram_cost_full = dints_space.get_ram_cost_usage(inputs.shape,
full=True)
ram_cost_usage = dints_space.get_ram_cost_usage(inputs.shape)
ram_cost_loss = torch.abs(factor_ram_cost -
ram_cost_usage / ram_cost_full)
arch_optimizer_a.zero_grad()
arch_optimizer_c.zero_grad()
combination_weights = (epoch - num_epochs_warmup) / (
num_epochs - num_epochs_warmup)
if amp:
with autocast():
outputs_search = model(inputs_search)
if output_classes == 2:
loss = loss_func(torch.flip(outputs_search, dims=[1]),
1 - labels_search)
else:
loss = loss_func(outputs_search, labels_search)
loss += combination_weights * ((entropy_alpha_a + entropy_alpha_c) + ram_cost_loss \
+ 0.001 * topology_loss)
scaler.scale(loss).backward()
scaler.step(arch_optimizer_a)
scaler.step(arch_optimizer_c)
scaler.update()
else:
outputs_search = model(inputs_search)
if output_classes == 2:
loss = loss_func(torch.flip(outputs_search, dims=[1]),
1 - labels_search)
else:
loss = loss_func(outputs_search, labels_search)
loss += 1.0 * (combination_weights * (entropy_alpha_a + entropy_alpha_c) + ram_cost_loss \
+ 0.001 * topology_loss)
loss.backward()
arch_optimizer_a.step()
arch_optimizer_c.step()
epoch_loss_arch += loss.item()
loss_torch_arch[0] += loss.item()
loss_torch_arch[1] += 1.0
if dist.get_rank() == 0:
print(
"[{0}] ".format(str(datetime.now())[:19]) +
f"{step}/{epoch_len}, train_loss_arch: {loss.item():.4f}")
writer.add_scalar("train_loss_arch", loss.item(),
epoch_len * epoch + step)
# synchronizes all processes and reduce results
dist.barrier()
dist.all_reduce(loss_torch, op=torch.distributed.ReduceOp.SUM)
loss_torch = loss_torch.tolist()
loss_torch_arch = loss_torch_arch.tolist()
if dist.get_rank() == 0:
loss_torch_epoch = loss_torch[0] / loss_torch[1]
print(
f"epoch {epoch + 1} average loss: {loss_torch_epoch:.4f}, best mean dice: {best_metric:.4f} at epoch {best_metric_epoch}"
)
if epoch >= num_epochs_warmup:
loss_torch_arch_epoch = loss_torch_arch[0] / loss_torch_arch[1]
print(
f"epoch {epoch + 1} average arch loss: {loss_torch_arch_epoch:.4f}, best mean dice: {best_metric:.4f} at epoch {best_metric_epoch}"
)
if (epoch + 1) % val_interval == 0:
torch.cuda.empty_cache()
model.eval()
with torch.no_grad():
metric = torch.zeros((output_classes - 1) * 2,
dtype=torch.float,
device=device)
metric_sum = 0.0
metric_count = 0
metric_mat = []
val_images = None
val_labels = None
val_outputs = None
_index = 0
for val_data in val_loader:
val_images = val_data["image"].to(device)
val_labels = val_data["label"].to(device)
roi_size = patch_size_valid
sw_batch_size = num_sw_batch_size
if amp:
with torch.cuda.amp.autocast():
pred = sliding_window_inference(
val_images,
roi_size,
sw_batch_size,
lambda x: model(x),
mode="gaussian",
overlap=overlap_ratio,
)
else:
pred = sliding_window_inference(
val_images,
roi_size,
sw_batch_size,
lambda x: model(x),
mode="gaussian",
overlap=overlap_ratio,
)
val_outputs = pred
val_outputs = post_pred(val_outputs[0, ...])
val_outputs = val_outputs[None, ...]
val_labels = post_label(val_labels[0, ...])
val_labels = val_labels[None, ...]
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=False)
print(_index + 1, "/", len(val_loader), value)
metric_count += len(value)
metric_sum += value.sum().item()
metric_vals = value.cpu().numpy()
if len(metric_mat) == 0:
metric_mat = metric_vals
else:
metric_mat = np.concatenate((metric_mat, metric_vals),
axis=0)
for _c in range(output_classes - 1):
val0 = torch.nan_to_num(value[0, _c], nan=0.0)
val1 = 1.0 - torch.isnan(value[0, 0]).float()
metric[2 * _c] += val0 * val1
metric[2 * _c + 1] += val1
_index += 1
# synchronizes all processes and reduce results
dist.barrier()
dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)
metric = metric.tolist()
if dist.get_rank() == 0:
for _c in range(output_classes - 1):
print(
"evaluation metric - class {0:d}:".format(_c + 1),
metric[2 * _c] / metric[2 * _c + 1])
avg_metric = 0
for _c in range(output_classes - 1):
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
avg_metric = avg_metric / float(output_classes - 1)
print("avg_metric", avg_metric)
if avg_metric > best_metric:
best_metric = avg_metric
best_metric_epoch = epoch + 1
best_metric_iterations = idx_iter
node_a_d, arch_code_a_d, arch_code_c_d, arch_code_a_max_d = dints_space.decode(
)
torch.save(
{
"node_a": node_a_d,
"arch_code_a": arch_code_a_d,
"arch_code_a_max": arch_code_a_max_d,
"arch_code_c": arch_code_c_d,
"iter_num": idx_iter,
"epochs": epoch + 1,
"best_dsc": best_metric,
"best_path": best_metric_iterations,
},
os.path.join(args.output_root,
"search_code_" + str(idx_iter) + ".pth"),
)
print("saved new best metric model")
dict_file = {}
dict_file["best_avg_dice_score"] = float(best_metric)
dict_file["best_avg_dice_score_epoch"] = int(
best_metric_epoch)
dict_file["best_avg_dice_score_iteration"] = int(idx_iter)
with open(os.path.join(args.output_root, "progress.yaml"),
"w") as out_file:
documents = yaml.dump(dict_file, stream=out_file)
print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}"
.format(epoch + 1, avg_metric, best_metric,
best_metric_epoch))
current_time = time.time()
elapsed_time = (current_time - start_time) / 60.0
with open(
os.path.join(args.output_root,
"accuracy_history.csv"), "a") as f:
f.write(
"{0:d}\t{1:.5f}\t{2:.5f}\t{3:.5f}\t{4:.1f}\t{5:d}\n"
.format(epoch + 1, avg_metric, loss_torch_epoch,
lr, elapsed_time, idx_iter))
dist.barrier()
torch.cuda.empty_cache()
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
if dist.get_rank() == 0:
writer.close()
dist.destroy_process_group()
return
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
# the path of ixi IXI-T1 dataset
data_path = os.sep.join([".", "workspace", "data", "medical", "ixi", "IXI-T1"])
images = [
"IXI314-IOP-0889-T1.nii.gz",
"IXI249-Guys-1072-T1.nii.gz",
"IXI609-HH-2600-T1.nii.gz",
"IXI173-HH-1590-T1.nii.gz",
"IXI020-Guys-0700-T1.nii.gz",
"IXI342-Guys-0909-T1.nii.gz",
"IXI134-Guys-0780-T1.nii.gz",
"IXI577-HH-2661-T1.nii.gz",
"IXI066-Guys-0731-T1.nii.gz",
"IXI130-HH-1528-T1.nii.gz",
"IXI607-Guys-1097-T1.nii.gz",
"IXI175-HH-1570-T1.nii.gz",
"IXI385-HH-2078-T1.nii.gz",
"IXI344-Guys-0905-T1.nii.gz",
"IXI409-Guys-0960-T1.nii.gz",
"IXI584-Guys-1129-T1.nii.gz",
"IXI253-HH-1694-T1.nii.gz",
"IXI092-HH-1436-T1.nii.gz",
"IXI574-IOP-1156-T1.nii.gz",
"IXI585-Guys-1130-T1.nii.gz",
]
images = [os.sep.join([data_path, f]) for f in images]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
# define transforms
train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90(), EnsureType()])
val_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), EnsureType()])
# define image dataset, data loader
check_ds = ImageDataset(image_files=images, labels=labels, transform=train_transforms)
check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
im, label = monai.utils.misc.first(check_loader)
print(type(im), im.shape, label)
# create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
loss = torch.nn.CrossEntropyLoss()
lr = 1e-5
opt = torch.optim.Adam(net.parameters(), lr)
# Ignite trainer expects batch=(img, label) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
trainer = create_supervised_trainer(net, opt, loss, device, False)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs_array/", "net", n_saved=10, require_empty=False)
trainer.add_event_handler(
event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={"net": net, "opt": opt}
)
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer", output_transform=lambda x: x)
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler(output_transform=lambda x: x)
train_tensorboard_stats_handler.attach(trainer)
# set parameters for validation
validation_every_n_epochs = 1
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net, val_metrics, device, True)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x: None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
# create a validation data loader
val_ds = ImageDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# create a training data loader
train_ds = ImageDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
print(state)
def main():
opt = Options().parse()
# monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# check gpus
if opt.gpu_ids != '-1':
num_gpus = len(opt.gpu_ids.split(','))
else:
num_gpus = 0
print('number of GPU:', num_gpus)
# Data loader creation
# train images
train_images = sorted(glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))
train_segs = sorted(glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))
train_images_for_dice = sorted(glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))
train_segs_for_dice = sorted(glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))
# validation images
val_images = sorted(glob(os.path.join(opt.images_folder, 'val', 'image*.nii')))
val_segs = sorted(glob(os.path.join(opt.labels_folder, 'val', 'label*.nii')))
# test images
test_images = sorted(glob(os.path.join(opt.images_folder, 'test', 'image*.nii')))
test_segs = sorted(glob(os.path.join(opt.labels_folder, 'test', 'label*.nii')))
# augment the data list for training
for i in range(int(opt.increase_factor_data)):
train_images.extend(train_images)
train_segs.extend(train_segs)
print('Number of training patches per epoch:', len(train_images))
print('Number of training images per epoch:', len(train_images_for_dice))
print('Number of validation images per epoch:', len(val_images))
print('Number of test images per epoch:', len(test_images))
# Creation of data directories for data_loader
train_dicts = [{'image': image_name, 'label': label_name}
for image_name, label_name in zip(train_images, train_segs)]
train_dice_dicts = [{'image': image_name, 'label': label_name}
for image_name, label_name in zip(train_images_for_dice, train_segs_for_dice)]
val_dicts = [{'image': image_name, 'label': label_name}
for image_name, label_name in zip(val_images, val_segs)]
test_dicts = [{'image': image_name, 'label': label_name}
for image_name, label_name in zip(test_images, test_segs)]
# Transforms list
if opt.resolution is not None:
train_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135), # CT HU filter
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'], source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # augmentation
ScaleIntensityd(keys=['image']), # intensity
Spacingd(keys=['image', 'label'], pixdim=opt.resolution, mode=('bilinear', 'nearest')), # resolution
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=1),
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=0),
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=2),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 36, np.pi / 36, np.pi * 2), padding_mode="zeros"),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 36, np.pi / 2, np.pi / 36), padding_mode="zeros"),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 2, np.pi / 36, np.pi / 36), padding_mode="zeros"),
Rand3DElasticd(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
sigma_range=(5, 8), magnitude_range=(100, 200), scale_range=(0.15, 0.15, 0.15),
padding_mode="zeros"),
RandGaussianSmoothd(keys=["image"], sigma_x=(0.5, 1.15), sigma_y=(0.5, 1.15), sigma_z=(0.5, 1.15), prob=0.1,),
RandAdjustContrastd(keys=['image'], gamma=(0.5, 2.5), prob=0.1),
RandGaussianNoised(keys=['image'], prob=0.1, mean=np.random.uniform(0, 0.5), std=np.random.uniform(0, 15)),
RandShiftIntensityd(keys=['image'], offsets=np.random.uniform(0,0.3), prob=0.1),
SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'), # pad if the image is smaller than patch
RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),
ToTensord(keys=['image', 'label'])
]
val_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'], source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
Spacingd(keys=['image', 'label'], pixdim=opt.resolution, mode=('bilinear', 'nearest')), # resolution
SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'), # pad if the image is smaller than patch
ToTensord(keys=['image', 'label'])
]
else:
train_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'], source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # augmentation
ScaleIntensityd(keys=['image']), # intensity
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=1),
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=0),
RandFlipd(keys=['image', 'label'], prob=0.15, spatial_axis=2),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 36, np.pi / 36, np.pi * 2), padding_mode="zeros"),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 36, np.pi / 2, np.pi / 36), padding_mode="zeros"),
RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
rotate_range=(np.pi / 2, np.pi / 36, np.pi / 36), padding_mode="zeros"),
Rand3DElasticd(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=0.1,
sigma_range=(5, 8), magnitude_range=(100, 200), scale_range=(0.15, 0.15, 0.15),
padding_mode="zeros"),
RandGaussianSmoothd(keys=["image"], sigma_x=(0.5, 1.15), sigma_y=(0.5, 1.15), sigma_z=(0.5, 1.15), prob=0.1,),
RandAdjustContrastd(keys=['image'], gamma=(0.5, 2.5), prob=0.1),
RandGaussianNoised(keys=['image'], prob=0.1, mean=np.random.uniform(0, 0.5), std=np.random.uniform(0, 1)),
RandShiftIntensityd(keys=['image'], offsets=np.random.uniform(0,0.3), prob=0.1),
SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'), # pad if the image is smaller than patch
RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),
ToTensord(keys=['image', 'label'])
]
val_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135),
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'], source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
SpatialPadd(keys=['image', 'label'], spatial_size=opt.patch_size, method= 'end'), # pad if the image is smaller than patch
ToTensord(keys=['image', 'label'])
]
train_transforms = Compose(train_transforms)
val_transforms = Compose(val_transforms)
# create a training data loader
check_train = monai.data.Dataset(data=train_dicts, transform=train_transforms)
train_loader = DataLoader(check_train, batch_size=opt.batch_size, shuffle=True, collate_fn=list_data_collate, num_workers=opt.workers, pin_memory=False)
# create a training_dice data loader
check_val = monai.data.Dataset(data=train_dice_dicts, transform=val_transforms)
train_dice_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)
# create a validation data loader
check_val = monai.data.Dataset(data=val_dicts, transform=val_transforms)
val_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)
# create a validation data loader
check_val = monai.data.Dataset(data=test_dicts, transform=val_transforms)
test_loader = DataLoader(check_val, batch_size=1, num_workers=opt.workers, collate_fn=list_data_collate, pin_memory=False)
# build the network
if opt.network is 'nnunet':
net = build_net() # nn build_net
elif opt.network is 'unetr':
net = build_UNETR() # UneTR
net.cuda()
if num_gpus > 1:
net = torch.nn.DataParallel(net)
if opt.preload is not None:
net.load_state_dict(torch.load(opt.preload))
dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
loss_function = monai.losses.DiceCELoss(sigmoid=True)
torch.backends.cudnn.benchmark = opt.benchmark
if opt.network is 'nnunet':
optim = torch.optim.SGD(net.parameters(), lr=opt.lr, momentum=0.99, weight_decay=3e-5, nesterov=True,)
net_scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=lambda epoch: (1 - epoch / opt.epochs) ** 0.9)
elif opt.network is 'unetr':
optim = torch.optim.AdamW(net.parameters(), lr=1e-4, weight_decay=1e-5)
# start a typical PyTorch training
val_interval = 1
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
writer = SummaryWriter()
for epoch in range(opt.epochs):
print("-" * 10)
print(f"epoch {epoch + 1}/{opt.epochs}")
net.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["image"].cuda(), batch_data["label"].cuda()
optim.zero_grad()
outputs = net(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optim.step()
epoch_loss += loss.item()
epoch_len = len(check_train) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if opt.network is 'nnunet':
update_learning_rate(net_scheduler, optim)
if (epoch + 1) % val_interval == 0:
net.eval()
with torch.no_grad():
def plot_dice(images_loader):
val_images = None
val_labels = None
val_outputs = None
for data in images_loader:
val_images, val_labels = data["image"].cuda(), data["label"].cuda()
roi_size = opt.patch_size
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, net)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
dice_metric(y_pred=val_outputs, y=val_labels)
# aggregate the final mean dice result
metric = dice_metric.aggregate().item()
# reset the status for next validation round
dice_metric.reset()
return metric, val_images, val_labels, val_outputs
metric, val_images, val_labels, val_outputs = plot_dice(val_loader)
# Save best model
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(net.state_dict(), "best_metric_model.pth")
print("saved new best metric model")
metric_train, train_images, train_labels, train_outputs = plot_dice(train_dice_loader)
metric_test, test_images, test_labels, test_outputs = plot_dice(test_loader)
# Logger bar
print(
"current epoch: {} Training dice: {:.4f} Validation dice: {:.4f} Testing dice: {:.4f} Best Validation dice: {:.4f} at epoch {}".format(
epoch + 1, metric_train, metric, metric_test, best_metric, best_metric_epoch
)
)
writer.add_scalar("Mean_epoch_loss", epoch_loss, epoch + 1)
writer.add_scalar("Testing_dice", metric_test, epoch + 1)
writer.add_scalar("Training_dice", metric_train, epoch + 1)
writer.add_scalar("Validation_dice", metric, epoch + 1)
# plot the last model output as GIF image in TensorBoard with the corresponding image and label
# val_outputs = (val_outputs.sigmoid() >= 0.5).float()
plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag="validation image")
plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag="validation label")
plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag="validation inference")
plot_2d_or_3d_image(test_images, epoch + 1, writer, index=0, tag="test image")
plot_2d_or_3d_image(test_labels, epoch + 1, writer, index=0, tag="test label")
plot_2d_or_3d_image(test_outputs, epoch + 1, writer, index=0, tag="test inference")
print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
writer.close()
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1, channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
]
)
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
post_trans = Compose([EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
saver = SaveImage(output_dir="./output", output_ext=".nii.gz", output_postfix="seg")
# try to use all the available GPUs
devices = [torch.device("cuda" if torch.cuda.is_available() else "cpu")]
#devices = get_devices_spec(None)
model = 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(devices[0])
model.load_state_dict(torch.load("best_metric_model_segmentation3d_dict.pth"))
# if we have multiple GPUs, set data parallel to execute sliding window inference
if len(devices) > 1:
model = torch.nn.DataParallel(model, device_ids=devices)
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(devices[0]), val_data["seg"].to(devices[0])
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
val_labels = decollate_batch(val_labels)
meta_data = decollate_batch(val_data["img_meta_dict"])
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
for val_output, data in zip(val_outputs, meta_data):
saver(val_output, data)
# aggregate the final mean dice result
print("evaluation metric:", dice_metric.aggregate().item())
# reset the status
dice_metric.reset()
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask pairs
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_2d(128, 128, num_seg_classes=1)
Image.fromarray((im * 255).astype("uint8")).save(
os.path.join(tempdir, f"img{i:d}.png"))
Image.fromarray((seg * 255).astype("uint8")).save(
os.path.join(tempdir, f"seg{i:d}.png"))
images = sorted(glob(os.path.join(tempdir, "img*.png")))
segs = sorted(glob(os.path.join(tempdir, "seg*.png")))
# define transforms for image and segmentation
train_imtrans = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
RandSpatialCrop((96, 96), random_size=False),
RandRotate90(prob=0.5, spatial_axes=(0, 1)),
EnsureType(),
])
train_segtrans = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
RandSpatialCrop((96, 96), random_size=False),
RandRotate90(prob=0.5, spatial_axes=(0, 1)),
EnsureType(),
])
val_imtrans = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
EnsureType()
])
val_segtrans = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
EnsureType()
])
# define array dataset, data loader
check_ds = ArrayDataset(images, train_imtrans, segs, train_segtrans)
check_loader = DataLoader(check_ds,
batch_size=10,
num_workers=2,
pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(check_loader)
print(im.shape, seg.shape)
# create a training data loader
train_ds = ArrayDataset(images[:20], train_imtrans, segs[:20],
train_segtrans)
train_loader = DataLoader(train_ds,
batch_size=4,
shuffle=True,
num_workers=8,
pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = ArrayDataset(images[-20:], val_imtrans, segs[-20:], val_segtrans)
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
pin_memory=torch.cuda.is_available())
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.UNet(
spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss_function = monai.losses.DiceLoss(sigmoid=True)
optimizer = torch.optim.Adam(model.parameters(), 1e-3)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
for epoch in range(10):
print("-" * 10)
print(f"epoch {epoch + 1}/{10}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data[0].to(device), batch_data[1].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
val_images = None
val_labels = None
val_outputs = None
for val_data in val_loader:
val_images, val_labels = val_data[0].to(
device), val_data[1].to(device)
roi_size = (96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, model)
val_outputs = [
post_trans(i) for i in decollate_batch(val_outputs)
]
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
# aggregate the final mean dice result
metric = dice_metric.aggregate().item()
# reset the status for next validation round
dice_metric.reset()
metric_values.append(metric)
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(),
"best_metric_model_segmentation2d_array.pth")
print("saved new best metric model")
print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}"
.format(epoch + 1, metric, best_metric, best_metric_epoch))
writer.add_scalar("val_mean_dice", metric, epoch + 1)
# plot the last model output as GIF image in TensorBoard with the corresponding image and label
plot_2d_or_3d_image(val_images,
epoch + 1,
writer,
index=0,
tag="image")
plot_2d_or_3d_image(val_labels,
epoch + 1,
writer,
index=0,
tag="label")
plot_2d_or_3d_image(val_outputs,
epoch + 1,
writer,
index=0,
tag="output")
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask pairs
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_3d(128,
128,
128,
num_seg_classes=1,
channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"img{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "img*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
train_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
RandCropByPosNegLabeld(
keys=["img", "seg"],
label_key="seg",
spatial_size=[96, 96, 96],
pos=1,
neg=1,
num_samples=4,
),
RandRotate90d(keys=["img", "seg"], prob=0.5, spatial_axes=[0, 2]),
EnsureTyped(keys=["img", "seg"]),
])
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
EnsureTyped(keys=["img", "seg"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
check_loader = DataLoader(
check_ds,
batch_size=2,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["seg"].shape)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = DataLoader(
train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(
val_ds,
batch_size=5,
num_workers=8,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = monai.networks.nets.UNet(
spatial_dims=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss = monai.losses.DiceLoss(sigmoid=True)
lr = 1e-3
opt = torch.optim.Adam(net.parameters(), lr)
# Ignite trainer expects batch=(img, seg) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch["img"], batch["seg"]), device,
non_blocking)
trainer = create_supervised_trainer(net,
opt,
loss,
device,
False,
prepare_batch=prepare_batch)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs_dict/",
"net",
n_saved=10,
require_empty=False)
trainer.add_event_handler(
event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
"net": net,
"opt": opt
},
)
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer",
output_transform=lambda x: x)
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x: x)
train_tensorboard_stats_handler.attach(trainer)
validation_every_n_iters = 5
# set parameters for validation
metric_name = "Mean_Dice"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: MeanDice()}
post_pred = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
post_label = Compose([EnsureType(), AsDiscrete(threshold=0.5)])
# Ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(
net,
val_metrics,
device,
True,
output_transform=lambda x, y, y_pred:
([post_pred(i) for i in decollate_batch(y_pred)],
[post_label(i) for i in decollate_batch(y)]),
prepare_batch=prepare_batch,
)
@trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters))
def run_validation(engine):
evaluator.run(val_loader)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(
patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=early_stopper)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every validation epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.iteration,
) # fetch global iteration number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add handler to draw the first image and the corresponding label and model output in the last batch
# here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations.
val_tensorboard_image_handler = TensorBoardImageHandler(
batch_transform=lambda batch: (batch["img"], batch["seg"]),
output_transform=lambda output: output[0],
global_iter_transform=lambda x: trainer.state.epoch,
)
evaluator.add_event_handler(
event_name=Events.ITERATION_COMPLETED(every=2),
handler=val_tensorboard_image_handler,
)
train_epochs = 5
state = trainer.run(train_loader, train_epochs)
print(state)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
# the path of ixi IXI-T1 dataset
data_path = os.sep.join(
[".", "workspace", "data", "medical", "ixi", "IXI-T1"])
images = [
"IXI314-IOP-0889-T1.nii.gz",
"IXI249-Guys-1072-T1.nii.gz",
"IXI609-HH-2600-T1.nii.gz",
"IXI173-HH-1590-T1.nii.gz",
"IXI020-Guys-0700-T1.nii.gz",
"IXI342-Guys-0909-T1.nii.gz",
"IXI134-Guys-0780-T1.nii.gz",
"IXI577-HH-2661-T1.nii.gz",
"IXI066-Guys-0731-T1.nii.gz",
"IXI130-HH-1528-T1.nii.gz",
"IXI607-Guys-1097-T1.nii.gz",
"IXI175-HH-1570-T1.nii.gz",
"IXI385-HH-2078-T1.nii.gz",
"IXI344-Guys-0905-T1.nii.gz",
"IXI409-Guys-0960-T1.nii.gz",
"IXI584-Guys-1129-T1.nii.gz",
"IXI253-HH-1694-T1.nii.gz",
"IXI092-HH-1436-T1.nii.gz",
"IXI574-IOP-1156-T1.nii.gz",
"IXI585-Guys-1130-T1.nii.gz",
]
images = [os.sep.join([data_path, f]) for f in images]
# 2 binary labels for gender classification: man and woman
labels = np.array(
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0],
dtype=np.int64)
# Define transforms
train_transforms = Compose([
ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
RandRotate90(),
EnsureType()
])
val_transforms = Compose(
[ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
EnsureType()])
# Define image dataset, data loader
check_ds = ImageDataset(image_files=images,
labels=labels,
transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=2,
pin_memory=torch.cuda.is_available())
im, label = monai.utils.misc.first(check_loader)
print(type(im), im.shape, label)
# create a training data loader
train_ds = ImageDataset(image_files=images[:10],
labels=labels[:10],
transform=train_transforms)
train_loader = DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=2,
pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = ImageDataset(image_files=images[-10:],
labels=labels[-10:],
transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=2,
num_workers=2,
pin_memory=torch.cuda.is_available())
# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.DenseNet121(spatial_dims=3,
in_channels=1,
out_channels=2).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
for epoch in range(5):
print("-" * 10)
print(f"epoch {epoch + 1}/{5}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data[0].to(device), batch_data[1].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
num_correct = 0.0
metric_count = 0
for val_data in val_loader:
val_images, val_labels = val_data[0].to(
device), val_data[1].to(device)
val_outputs = model(val_images)
value = torch.eq(val_outputs.argmax(dim=1), val_labels)
metric_count += len(value)
num_correct += value.sum().item()
metric = num_correct / metric_count
metric_values.append(metric)
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(),
"best_metric_model_classification3d_array.pth")
print("saved new best metric model")
print(
"current epoch: {} current accuracy: {:.4f} best accuracy: {:.4f} at epoch {}"
.format(epoch + 1, metric, best_metric, best_metric_epoch))
writer.add_scalar("val_accuracy", metric, epoch + 1)
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
def segment(image, label, result, weights, resolution, patch_size, network,
gpu_ids):
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
if label is not None:
uniform_img_dimensions_internal(image, label, True)
files = [{"image": image, "label": label}]
else:
files = [{"image": image}]
# original size, size after crop_background, cropped roi coordinates, cropped resampled roi size
original_shape, crop_shape, coord1, coord2, resampled_size, original_resolution = statistics_crop(
image, resolution)
# -------------------------------
if label is not None:
if resolution is not None:
val_transforms = Compose([
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135), # Threshold CT
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'],
source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
Spacingd(keys=['image', 'label'],
pixdim=resolution,
mode=('bilinear', 'nearest')), # resolution
SpatialPadd(keys=['image', 'label'],
spatial_size=patch_size,
method='end'),
ToTensord(keys=['image', 'label'])
])
else:
val_transforms = Compose([
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135), # Threshold CT
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image', 'label'],
source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
SpatialPadd(
keys=['image', 'label'],
spatial_size=patch_size,
method='end'), # pad if the image is smaller than patch
ToTensord(keys=['image', 'label'])
])
else:
if resolution is not None:
val_transforms = Compose([
LoadImaged(keys=['image']),
AddChanneld(keys=['image']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135), # Threshold CT
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image'],
source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
Spacingd(keys=['image'], pixdim=resolution,
mode=('bilinear')), # resolution
SpatialPadd(
keys=['image'], spatial_size=patch_size,
method='end'), # pad if the image is smaller than patch
ToTensord(keys=['image'])
])
else:
val_transforms = Compose([
LoadImaged(keys=['image']),
AddChanneld(keys=['image']),
# ThresholdIntensityd(keys=['image'], threshold=-135, above=True, cval=-135), # Threshold CT
# ThresholdIntensityd(keys=['image'], threshold=215, above=False, cval=215),
CropForegroundd(keys=['image'],
source_key='image'), # crop CropForeground
NormalizeIntensityd(keys=['image']), # intensity
ScaleIntensityd(keys=['image']),
SpatialPadd(
keys=['image'], spatial_size=patch_size,
method='end'), # pad if the image is smaller than patch
ToTensord(keys=['image'])
])
val_ds = monai.data.Dataset(data=files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=0,
collate_fn=list_data_collate,
pin_memory=False)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose([
EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold_values=True)
])
if gpu_ids != '-1':
# try to use all the available GPUs
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_ids
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# build the network
if network == 'nnunet':
net = build_net() # nn build_net
elif network == 'unetr':
net = build_UNETR() # UneTR
net = net.to(device)
if gpu_ids == '-1':
net.load_state_dict(new_state_dict_cpu(weights))
else:
net.load_state_dict(new_state_dict(weights))
# define sliding window size and batch size for windows inference
roi_size = patch_size
sw_batch_size = 4
net.eval()
with torch.no_grad():
if label is None:
for val_data in val_loader:
val_images = val_data["image"].to(device)
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, net)
val_outputs = [
post_trans(i) for i in decollate_batch(val_outputs)
]
else:
for val_data in val_loader:
val_images, val_labels = val_data["image"].to(
device), val_data["label"].to(device)
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, net)
val_outputs = [
post_trans(i) for i in decollate_batch(val_outputs)
]
dice_metric(y_pred=val_outputs, y=val_labels)
metric = dice_metric.aggregate().item()
print("Evaluation Metric (Dice):", metric)
result_array = val_outputs[0].squeeze().data.cpu().numpy()
# Remove the pad if the image was smaller than the patch in some directions
result_array = result_array[0:resampled_size[0], 0:resampled_size[1],
0:resampled_size[2]]
# resample back to the original resolution
if resolution is not None:
result_array_np = np.transpose(result_array, (2, 1, 0))
result_array_temp = sitk.GetImageFromArray(result_array_np)
result_array_temp.SetSpacing(resolution)
# save temporary label
writer = sitk.ImageFileWriter()
writer.SetFileName('temp_seg.nii')
writer.Execute(result_array_temp)
files = [{"image": 'temp_seg.nii'}]
files_transforms = Compose([
LoadImaged(keys=['image']),
AddChanneld(keys=['image']),
Spacingd(keys=['image'],
pixdim=original_resolution,
mode=('nearest')),
Resized(keys=['image'],
spatial_size=crop_shape,
mode=('nearest')),
])
files_ds = Dataset(data=files, transform=files_transforms)
files_loader = DataLoader(files_ds, batch_size=1, num_workers=0)
for files_data in files_loader:
files_images = files_data["image"]
res = files_images.squeeze().data.numpy()
result_array = np.rint(res)
os.remove('./temp_seg.nii')
# recover the cropped background before saving the image
empty_array = np.zeros(original_shape)
empty_array[coord1[0]:coord2[0], coord1[1]:coord2[1],
coord1[2]:coord2[2]] = result_array
result_seg = from_numpy_to_itk(empty_array, image)
# save label
writer = sitk.ImageFileWriter()
writer.SetFileName(result)
writer.Execute(result_seg)
print("Saved Result at:", str(result))
def main(tempdir):
config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz"))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz"))
images = sorted(glob(os.path.join(tempdir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz")))
# define transforms for image and segmentation
imtrans = Compose([ScaleIntensity(), AddChannel(), EnsureType()])
segtrans = Compose([AddChannel(), EnsureType()])
val_ds = ImageDataset(images,
segs,
transform=imtrans,
seg_transform=segtrans,
image_only=False)
# sliding window inference for one image at every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=1,
pin_memory=torch.cuda.is_available())
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
saver = SaveImage(output_dir="./output",
output_ext=".nii.gz",
output_postfix="seg")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = 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)
model.load_state_dict(
torch.load("best_metric_model_segmentation3d_array.pth"))
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data[0].to(device), val_data[1].to(
device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
val_labels = decollate_batch(val_labels)
meta_data = decollate_batch(val_data[2])
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
for val_output, data in zip(val_outputs, meta_data):
saver(val_output, data)
# aggregate the final mean dice result
print("evaluation metric:", dice_metric.aggregate().item())
# reset the status
dice_metric.reset()
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
# the path of ixi IXI-T1 dataset
data_path = os.sep.join([".", "workspace", "data", "medical", "ixi", "IXI-T1"])
images = [
"IXI314-IOP-0889-T1.nii.gz",
"IXI249-Guys-1072-T1.nii.gz",
"IXI609-HH-2600-T1.nii.gz",
"IXI173-HH-1590-T1.nii.gz",
"IXI020-Guys-0700-T1.nii.gz",
"IXI342-Guys-0909-T1.nii.gz",
"IXI134-Guys-0780-T1.nii.gz",
"IXI577-HH-2661-T1.nii.gz",
"IXI066-Guys-0731-T1.nii.gz",
"IXI130-HH-1528-T1.nii.gz",
"IXI607-Guys-1097-T1.nii.gz",
"IXI175-HH-1570-T1.nii.gz",
"IXI385-HH-2078-T1.nii.gz",
"IXI344-Guys-0905-T1.nii.gz",
"IXI409-Guys-0960-T1.nii.gz",
"IXI584-Guys-1129-T1.nii.gz",
"IXI253-HH-1694-T1.nii.gz",
"IXI092-HH-1436-T1.nii.gz",
"IXI574-IOP-1156-T1.nii.gz",
"IXI585-Guys-1130-T1.nii.gz",
]
images = [os.sep.join([data_path, f]) for f in images]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
train_files = [{"img": img, "label": label} for img, label in zip(images[:10], labels[:10])]
val_files = [{"img": img, "label": label} for img, label in zip(images[-10:], labels[-10:])]
# Define transforms for image
train_transforms = Compose(
[
LoadImaged(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]),
EnsureTyped(keys=["img"]),
]
)
val_transforms = Compose(
[
LoadImaged(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
EnsureTyped(keys=["img"]),
]
)
post_pred = Compose([EnsureType(), Activations(softmax=True)])
post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])
# Define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["label"])
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
auc_metric = ROCAUCMetric()
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
writer = SummaryWriter()
for epoch in range(5):
print("-" * 10)
print(f"epoch {epoch + 1}/{5}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["img"].to(device), batch_data["label"].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
epoch_loss /= step
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
y_pred = torch.tensor([], dtype=torch.float32, device=device)
y = torch.tensor([], dtype=torch.long, device=device)
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(device), val_data["label"].to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
acc_metric = acc_value.sum().item() / len(acc_value)
y_onehot = [post_label(i) for i in decollate_batch(y)]
y_pred_act = [post_pred(i) for i in decollate_batch(y_pred)]
auc_metric(y_pred_act, y_onehot)
auc_result = auc_metric.aggregate()
auc_metric.reset()
del y_pred_act, y_onehot
if acc_metric > best_metric:
best_metric = acc_metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), "best_metric_model_classification3d_dict.pth")
print("saved new best metric model")
print(
"current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}".format(
epoch + 1, acc_metric, auc_result, best_metric, best_metric_epoch
)
)
writer.add_scalar("val_accuracy", acc_metric, epoch + 1)
print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
writer.close()
