def test_cuda_value(self):
img = torch.stack([torch.ones(2, 2, 2, 2), torch.ones(2, 2, 2, 2) + 2])
expected_value = torch.ones(2, 2, 2, 2) * torch.tensor([2.5, 1.5]).reshape(1, 2, 1, 1)
if torch.cuda.is_available():
img = img.to(torch.device("cuda:0"))
expected_value = expected_value.to(torch.device("cuda:0"))
result = MeanEnsembled(keys="output", weights=torch.tensor([[[1, 3]], [[3, 1]]]))({"output": img})
assert_allclose(result["output"], expected_value)
def test_value(self, input_param, data, expected_value):
result = MeanEnsembled(**input_param)(data)
torch.testing.assert_allclose(result["output"], expected_value)
def main():
opt = Options().parse()
# monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
set_determinism(seed=0)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
device = torch.device(opt.gpu_id)
# ------- Data loader creation ----------
# images
images = sorted(glob(os.path.join(opt.images_folder, 'image*.nii')))
segs = sorted(glob(os.path.join(opt.labels_folder, 'label*.nii')))
train_files = []
val_files = []
for i in range(opt.models_ensemble):
train_files.append([{
"image": img,
"label": seg
} for img, seg in zip(
images[:(opt.split_val * i)] +
images[(opt.split_val *
(i + 1)):(len(images) -
opt.split_val)], segs[:(opt.split_val * i)] +
segs[(opt.split_val * (i + 1)):(len(images) - opt.split_val)])])
val_files.append([{
"image": img,
"label": seg
} for img, seg in zip(
images[(opt.split_val * i):(opt.split_val *
(i + 1))], segs[(opt.split_val *
i):(opt.split_val *
(i + 1))])])
test_files = [{
"image": img,
"label": seg
} for img, seg in zip(images[(len(images) -
opt.split_test):len(images)], segs[(
len(images) -
opt.split_test):len(images)])]
# ----------- Transforms list --------------
if opt.resolution is not None:
train_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
Spacingd(keys=['image', 'label'],
pixdim=opt.resolution,
mode=('bilinear', 'nearest')),
RandFlipd(keys=['image', 'label'], prob=0.1, spatial_axis=1),
RandFlipd(keys=['image', 'label'], prob=0.1, spatial_axis=0),
RandFlipd(keys=['image', 'label'], prob=0.1, 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"),
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),
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']),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
Spacingd(keys=['image', 'label'],
pixdim=opt.resolution,
mode=('bilinear', 'nearest')),
ToTensord(keys=['image', 'label'])
]
else:
train_transforms = [
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
RandFlipd(keys=['image', 'label'], prob=0.1, spatial_axis=1),
RandFlipd(keys=['image', 'label'], prob=0.1, spatial_axis=0),
RandFlipd(keys=['image', 'label'], prob=0.1, 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"),
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),
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']),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
ToTensord(keys=['image', 'label'])
]
train_transforms = Compose(train_transforms)
val_transforms = Compose(val_transforms)
# ---------- Creation of DataLoaders -------------
train_dss = [
CacheDataset(data=train_files[i], transform=train_transforms)
for i in range(opt.models_ensemble)
]
train_loaders = [
DataLoader(train_dss[i],
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
for i in range(opt.models_ensemble)
]
val_dss = [
CacheDataset(data=val_files[i], transform=val_transforms)
for i in range(opt.models_ensemble)
]
val_loaders = [
DataLoader(val_dss[i],
batch_size=1,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
for i in range(opt.models_ensemble)
]
test_ds = CacheDataset(data=test_files, transform=val_transforms)
test_loader = DataLoader(test_ds,
batch_size=1,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
def train(index):
# ---------- Build the nn-Unet network ------------
if opt.resolution is None:
sizes, spacings = opt.patch_size, opt.spacing
else:
sizes, spacings = opt.patch_size, opt.resolution
strides, kernels = [], []
while True:
spacing_ratio = [sp / min(spacings) for sp in spacings]
stride = [
2 if ratio <= 2 and size >= 8 else 1
for (ratio, size) in zip(spacing_ratio, sizes)
]
kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
if all(s == 1 for s in stride):
break
sizes = [i / j for i, j in zip(sizes, stride)]
spacings = [i * j for i, j in zip(spacings, stride)]
kernels.append(kernel)
strides.append(stride)
strides.insert(0, len(spacings) * [1])
kernels.append(len(spacings) * [3])
net = monai.networks.nets.DynUNet(
spatial_dims=3,
in_channels=opt.in_channels,
out_channels=opt.out_channels,
kernel_size=kernels,
strides=strides,
upsample_kernel_size=strides[1:],
res_block=True,
# act=act_type,
# norm=Norm.BATCH,
).to(device)
from torch.autograd import Variable
from torchsummaryX import summary
data = Variable(
torch.randn(int(opt.batch_size), int(opt.in_channels),
int(opt.patch_size[0]), int(opt.patch_size[1]),
int(opt.patch_size[2]))).cuda()
out = net(data)
summary(net, data)
print("out size: {}".format(out.size()))
# if opt.preload is not None:
# net.load_state_dict(torch.load(opt.preload))
# ---------- ------------------------ ------------
optim = torch.optim.Adam(net.parameters(), lr=opt.lr)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optim, lr_lambda=lambda epoch: (1 - epoch / opt.epochs)**0.9)
loss_function = monai.losses.DiceCELoss(sigmoid=True)
val_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
# KeepLargestConnectedComponentd(keys="pred", applied_labels=[1])
])
val_handlers = [
StatsHandler(output_transform=lambda x: None),
CheckpointSaver(save_dir="./runs/",
save_dict={"net": net},
save_key_metric=True),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loaders[index],
network=net,
inferer=SlidingWindowInferer(roi_size=opt.patch_size,
sw_batch_size=opt.batch_size,
overlap=0.5),
post_transform=val_post_transforms,
key_val_metric={
"val_mean_dice":
MeanDice(
include_background=True,
output_transform=lambda x: (x["pred"], x["label"]),
)
},
val_handlers=val_handlers)
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
# KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
])
train_handlers = [
ValidationHandler(validator=evaluator,
interval=5,
epoch_level=True),
LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": optim
},
save_final=True,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=opt.epochs,
train_data_loader=train_loaders[index],
network=net,
optimizer=optim,
loss_function=loss_function,
inferer=SimpleInferer(),
post_transform=train_post_transforms,
amp=False,
train_handlers=train_handlers,
)
trainer.run()
return net
models = [train(i) for i in range(opt.models_ensemble)]
# -------- Test the models ---------
def ensemble_evaluate(post_transforms, models):
evaluator = EnsembleEvaluator(
device=device,
val_data_loader=test_loader,
pred_keys=opt.pred_keys,
networks=models,
inferer=SlidingWindowInferer(roi_size=opt.patch_size,
sw_batch_size=opt.batch_size,
overlap=0.5),
post_transform=post_transforms,
key_val_metric={
"test_mean_dice":
MeanDice(
include_background=True,
output_transform=lambda x: (x["pred"], x["label"]),
)
},
)
evaluator.run()
mean_post_transforms = Compose([
MeanEnsembled(
keys=opt.pred_keys,
output_key="pred",
# in this particular example, we use validation metrics as weights
weights=opt.weights_models,
),
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
# KeepLargestConnectedComponentd(keys="pred", applied_labels=[1])
])
print('Results from MeanEnsembled:')
ensemble_evaluate(mean_post_transforms, models)
vote_post_transforms = Compose([
Activationsd(keys=opt.pred_keys, sigmoid=True),
# transform data into discrete before voting
AsDiscreted(keys=opt.pred_keys, threshold_values=True),
# KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
VoteEnsembled(keys=opt.pred_keys, output_key="pred"),
])
print('Results from VoteEnsembled:')
ensemble_evaluate(vote_post_transforms, models)