def run_interaction(self, train, compose):
data = [{
"image": np.ones((1, 2, 2, 2)).astype(np.float32),
"label": np.ones((1, 2, 2, 2))
} for _ in range(5)]
network = torch.nn.Linear(2, 2)
lr = 1e-3
opt = torch.optim.SGD(network.parameters(), lr)
loss = torch.nn.L1Loss()
train_transforms = Compose([
FindAllValidSlicesd(label="label", sids="sids"),
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
])
dataset = Dataset(data, transform=train_transforms)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=["image", "label", "pred"]),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(guidance="guidance",
discrepancy="discrepancy",
probability="probability"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
]
iteration_transforms = Compose(
iteration_transforms) if compose else iteration_transforms
i = Interaction(transforms=iteration_transforms,
train=train,
max_interactions=5)
self.assertEqual(len(i.transforms.transforms), 6,
"Mismatch in expected transforms")
# set up engine
engine = SupervisedTrainer(
device=torch.device("cpu"),
max_epochs=1,
train_data_loader=data_loader,
network=network,
optimizer=opt,
loss_function=loss,
iteration_update=i,
)
engine.add_event_handler(IterationEvents.INNER_ITERATION_STARTED,
add_one)
engine.add_event_handler(IterationEvents.INNER_ITERATION_COMPLETED,
add_one)
engine.run()
self.assertIsNotNone(engine.state.batch[0].get("guidance"),
"guidance is missing")
self.assertEqual(engine.state.best_metric, 9)
def prepare_data(self):
data_dir = self.hparams.data_dir
# Train imgs/masks
train_imgs = []
train_masks = []
with open(data_dir + 'train_imgs.txt', 'r') as f:
train_imgs = [data_dir + image.rstrip() for image in f.readlines()]
with open(data_dir + 'train_masks.txt', 'r') as f:
train_masks = [data_dir + mask.rstrip() for mask in f.readlines()]
train_dicts = [{'image': image, 'mask': mask} for (image, mask) in zip(train_imgs, train_masks)]
train_dicts, val_dicts = train_test_split(train_dicts, test_size=0.2)
# Basic transforms
data_keys = ["image", "mask"]
data_transforms = Compose(
[
LoadNiftid(keys=data_keys),
AddChanneld(keys=data_keys),
ScaleIntensityRangePercentilesd(
keys='image',
lower=25,
upper=75,
b_min=-0.5,
b_max=0.5
)
]
)
self.train_dataset = monai.data.CacheDataset(
data=train_dicts,
transform=Compose(
[
data_transforms,
RandCropByPosNegLabeld(
keys=data_keys,
label_key="mask",
spatial_size=self.hparams.patch_size,
num_samples=4,
image_key="image",
pos=0.8,
neg=0.2
),
ToTensord(keys=data_keys)
]
),
cache_rate=1.0
)
self.val_dataset = monai.data.CacheDataset(
data=val_dicts,
transform=Compose(
[
data_transforms,
CenterSpatialCropd(keys=data_keys, roi_size=self.hparams.patch_size),
ToTensord(keys=data_keys)
]
),
cache_rate=1.0
)
def transformations(self, H, L):
lower = L - (H / 2)
upper = L + (H / 2)
basic_transforms = Compose([
# Load image
LoadImaged(keys=["image"]),
# Segmentacija
CTSegmentation(keys=["image"]),
AddChanneld(keys=["image"]),
# Crop foreground based on seg image.
CropForegroundd(keys=["image"],
source_key="image",
margin=(30, 30, 0)),
# Obreži sliko v Z smeri, relative_z_roi = ( % od spodaj, % od zgoraj)
RelativeAsymmetricZCropd(keys=["image"],
relative_z_roi=(0.15, 0.25)),
])
train_transforms = Compose([
basic_transforms,
# Normalizacija na CT okno
# https://radiopaedia.org/articles/windowing-ct
RandCTWindowd(keys=["image"],
prob=1.0,
width=(H - 50, H + 50),
level=(L - 25, L + 25)),
# Mogoče zanimiva
RandAxisFlipd(keys=["image"], prob=0.1),
RandAffined(
keys=["image"],
prob=0.25,
rotate_range=(0, 0, np.pi / 16),
shear_range=(0.05, 0.05, 0.0),
translate_range=(10, 10, 0),
scale_range=(0.05, 0.05, 0.0),
spatial_size=(-1, -1, -1),
padding_mode="zeros",
),
ToTensord(keys=["image"]),
]).flatten()
# NOTE: No random transforms in the validation data
valid_transforms = Compose([
basic_transforms,
# Normalizacija na CT okno
# https://radiopaedia.org/articles/windowing-ct
CTWindowd(keys=["image"], width=H, level=L),
ToTensord(keys=["image"]),
]).flatten()
return train_transforms, valid_transforms
def transformations():
train_transforms = Compose([
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ToTensord(keys=['image', 'label'])
])
val_transforms = Compose([
LoadImaged(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ToTensord(keys=['image', 'label'])
])
return train_transforms, val_transforms
def setup(self, stage):
data_dir = 'data/'
# Train imgs/masks
train_imgs = []
with open(data_dir + 'train_imgs.txt', 'r') as f:
train_imgs = [image.rstrip() for image in f.readlines()]
train_masks = []
with open(data_dir + 'train_masks.txt', 'r') as f:
train_masks = [mask.rstrip() for mask in f.readlines()]
train_dicts = [{'image': image, 'mask': mask} for (image, mask) in zip(train_imgs, train_masks)]
train_dicts, val_dicts = train_test_split(train_dicts, test_size=0.2)
# Basic transforms
data_keys = ["image", "mask"]
data_transforms = Compose(
[
LoadNiftid(keys=data_keys),
AddChanneld(keys=data_keys),
NormalizeIntensityd(keys="image"),
RandCropByPosNegLabeld(
keys=data_keys, label_key="mask", size=(256, 256, 16), num_samples=4, image_key="image"
),
]
)
self.train_dataset = monai.data.CacheDataset(
data=train_dicts,
transform=Compose(
[
data_transforms,
self.augmentations,
ToTensord(keys=data_keys)
]
),
cache_rate=1.0
)
self.val_dataset = monai.data.CacheDataset(
data=val_dicts,
transform=Compose(
[
data_transforms,
ToTensord(keys=data_keys)
]
),
cache_rate=1.0
)
def prepare_data(self):
data_df = pd.read_csv(
'/data/shared/prostate/yale_prostate/input_lists/MR_yale.csv')
train_imgs = data_df['IMAGE'][0:295].tolist()
train_masks = data_df['SEGM'][0:295].tolist()
train_dicts = [{
'image': image,
'mask': mask
} for (image, mask) in zip(train_imgs, train_masks)]
train_dicts, val_dicts = train_test_split(train_dicts, test_size=0.2)
# Basic transforms
data_keys = ["image", "mask"]
data_transforms = Compose([
LoadNiftid(keys=data_keys),
AddChanneld(keys=data_keys),
ScaleIntensityRangePercentilesd(keys='image',
lower=25,
upper=75,
b_min=-0.5,
b_max=0.5)
])
self.train_dataset = monai.data.CacheDataset(
data=train_dicts,
transform=Compose([
data_transforms,
RandCropByPosNegLabeld(keys=data_keys,
label_key="mask",
spatial_size=self.hparams.patch_size,
num_samples=4,
image_key="image",
pos=0.8,
neg=0.2),
ToTensord(keys=data_keys)
]),
cache_rate=1.0)
self.val_dataset = monai.data.CacheDataset(
data=val_dicts,
transform=Compose([
data_transforms,
CenterSpatialCropd(keys=data_keys,
roi_size=self.hparams.patch_size),
ToTensord(keys=data_keys)
]),
cache_rate=1.0)
def test_tranform_dict(self, input):
transforms = Compose([
Range("random flip dict")(Flipd(keys="image")),
Range()(ToTensord("image"))
])
# Apply transforms
output = transforms(input)["image"]
# Decorate with NVTX Range
transforms1 = Range()(transforms)
transforms2 = Range("Transforms2")(transforms)
transforms3 = Range(name="Transforms3", methods="__call__")(transforms)
# Apply transforms with Range
output1 = transforms1(input)["image"]
output2 = transforms2(input)["image"]
output3 = transforms3(input)["image"]
# Check the outputs
self.assertIsInstance(output, torch.Tensor)
self.assertIsInstance(output1, torch.Tensor)
self.assertIsInstance(output2, torch.Tensor)
self.assertIsInstance(output3, torch.Tensor)
np.testing.assert_equal(output.numpy(), output1.numpy())
np.testing.assert_equal(output.numpy(), output2.numpy())
np.testing.assert_equal(output.numpy(), output3.numpy())
def val_pre_transforms(self, context: Context):
return [
LoadImaged(keys=("image", "label"), reader="ITKReader"),
NormalizeLabelsInDatasetd(keys="label", label_names=self._labels),
EnsureChannelFirstd(keys=("image", "label")),
Orientationd(keys=["image", "label"], axcodes="RAS"),
# This transform may not work well for MR images
ScaleIntensityRanged(keys=("image"),
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True),
Resized(keys=("image", "label"),
spatial_size=self.spatial_size,
mode=("area", "nearest")),
# Transforms for click simulation
FindAllValidSlicesMissingLabelsd(keys="label", sids="sids"),
AddInitialSeedPointMissingLabelsd(keys="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignalCustomd(
keys="image",
guidance="guidance",
number_intensity_ch=self.number_intensity_ch),
#
ToTensord(keys=("image", "label")),
SelectItemsd(keys=("image", "label", "guidance", "label_names")),
]
def _get_loader(self, folders):
images = []
segs = []
for folder in folders:
images += glob(os.path.join(folder, "*_im.nii.gz"))
segs += glob(os.path.join(folder, "*_seg.nii.gz"))
images = sorted(images, key=os.path.basename)
segs = sorted(segs, key=os.path.basename)
files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys="img"),
ToTensord(keys=["img", "seg"]),
])
ds = CacheDataset(data=files, transform=transforms)
loader = DataLoader(ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
return loader
def test_decollation(self, batch_size=2, num_workers=2):
im = create_test_image_2d(100, 101)[0]
data = [{
"image": make_nifti_image(im) if has_nib else im
} for _ in range(6)]
transforms = Compose([
AddChanneld("image"),
SpatialPadd("image", 150),
RandFlipd("image", prob=1.0, spatial_axis=1),
ToTensord("image"),
])
# If nibabel present, read from disk
if has_nib:
transforms = Compose([LoadImaged("image"), transforms])
dataset = CacheDataset(data, transforms, progress=False)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
for b, batch_data in enumerate(loader):
decollated_1 = decollate_batch(batch_data)
decollated_2 = Decollated()(batch_data)
for decollated in [decollated_1, decollated_2]:
for i, d in enumerate(decollated):
self.check_match(dataset[b * batch_size + i], d)
def test_decollation(self, *transforms):
batch_size = 2
num_workers = 2
t_compose = Compose(
[AddChanneld(KEYS),
Compose(transforms),
ToTensord(KEYS)])
# If nibabel present, read from disk
if has_nib:
t_compose = Compose([LoadImaged("image"), t_compose])
dataset = CacheDataset(self.data, t_compose, progress=False)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
for b, batch_data in enumerate(loader):
decollated_1 = decollate_batch(batch_data)
decollated_2 = Decollated()(batch_data)
for decollated in [decollated_1, decollated_2]:
for i, d in enumerate(decollated):
self.check_match(dataset[b * batch_size + i], d)
def pre_transforms(self):
t = [
LoadImaged(keys="image", reader="nibabelreader"),
AddChanneld(keys="image"),
# Spacing might not be needed as resize transform is used later.
# Spacingd(keys="image", pixdim=self.spacing),
RandAffined(
keys="image",
prob=1,
rotate_range=(np.pi / 4, np.pi / 4, np.pi / 4),
padding_mode="zeros",
as_tensor_output=False,
),
RandFlipd(keys="image", prob=0.5, spatial_axis=0),
RandRotated(keys="image",
range_x=(-5, 5),
range_y=(-5, 5),
range_z=(-5, 5)),
Resized(keys="image", spatial_size=self.spatial_size),
]
# If using TTA for deepedit
if self.deepedit:
t.append(DiscardAddGuidanced(keys="image"))
t.append(ToTensord(keys="image"))
return Compose(t)
def test_collation(self, _, transform, collate_fn, ndim):
data = self.data_3d if ndim == 3 else self.data_2d
if collate_fn:
modified_transform = transform
else:
modified_transform = Compose(
[transform,
ResizeWithPadOrCropd(KEYS, 100),
ToTensord(KEYS)])
# num workers = 0 for mac or gpu transforms
num_workers = 0 if sys.platform != "linux" or torch.cuda.is_available(
) else 2
dataset = CacheDataset(data,
transform=modified_transform,
progress=False)
loader = DataLoader(dataset,
num_workers,
batch_size=self.batch_size,
collate_fn=collate_fn)
for item in loader:
np.testing.assert_array_equal(
item["image_transforms"][0]["do_transforms"],
item["label_transforms"][0]["do_transforms"])
def run_inference_test(root_dir, device=torch.device("cuda:0")):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
Spacingd(keys=["img", "seg"],
pixdim=[1.2, 0.8, 0.7],
mode=["bilinear", "nearest"],
dtype=np.float32),
ScaleIntensityd(keys=["img", "seg"]),
ToTensord(keys=["img", "seg"]),
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inferene need to input 1 image in every iteration
val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
dice_metric = DiceMetric(include_background=True,
to_onehot_y=False,
sigmoid=True,
reduction="mean")
model = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model_filename = os.path.join(root_dir, "best_metric_model.pth")
model.load_state_dict(torch.load(model_filename))
model.eval()
with torch.no_grad():
metric_sum = 0.0
metric_count = 0
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
saver = NiftiSaver(output_dir=os.path.join(root_dir, "output"),
dtype=np.float32)
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
sw_batch_size, roi_size = 4, (96, 96, 96)
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = dice_metric(y_pred=val_outputs, y=val_labels)
not_nans = dice_metric.not_nans.item()
metric_count += not_nans
metric_sum += value.item() * not_nans
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(val_outputs, val_data["img_meta_dict"])
metric = metric_sum / metric_count
return metric
def train_pre_transforms(self, context: Context):
return [
LoadImaged(keys=("image", "label"), dtype=np.uint8),
FilterImaged(keys="image", min_size=5),
AsChannelFirstd(keys="image"),
AddChanneld(keys="label"),
ToTensord(keys="image"),
TorchVisiond(keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04),
ToNumpyd(keys="image"),
RandRotate90d(keys=("image", "label"),
prob=0.5,
spatial_axes=(0, 1)),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
AddInitialSeedPointExd(label="label", guidance="guidance"),
AddGuidanceSignald(image="image",
guidance="guidance",
number_intensity_ch=3),
EnsureTyped(keys=("image", "label")),
]
def __init__(self, tranforms):
self.tranform_list = []
for tranform in tranforms:
if 'LoadImaged' == tranform:
self.tranform_list.append(LoadImaged(keys=["image", "label"]))
elif 'AsChannelFirstd' == tranform:
self.tranform_list.append(AsChannelFirstd(keys="image"))
elif 'ConvertToMultiChannelBasedOnBratsClassesd' == tranform:
self.tranform_list.append(ConvertToMultiChannelBasedOnBratsClassesd(keys="label"))
elif 'Spacingd' == tranform:
self.tranform_list.append(Spacingd(keys=["image", "label"], pixdim=(1.5, 1.5, 2.0), mode=("bilinear", "nearest")))
elif 'Orientationd' == tranform:
self.tranform_list.append(Orientationd(keys=["image", "label"], axcodes="RAS"))
elif 'CenterSpatialCropd' == tranform:
self.tranform_list.append(CenterSpatialCropd(keys=["image", "label"], roi_size=[128, 128, 64]))
elif 'NormalizeIntensityd' == tranform:
self.tranform_list.append(NormalizeIntensityd(keys="image", nonzero=True, channel_wise=True))
elif 'ToTensord' == tranform:
self.tranform_list.append(ToTensord(keys=["image", "label"]))
elif 'Activations' == tranform:
self.tranform_list.append(Activations(sigmoid=True))
elif 'AsDiscrete' == tranform:
self.tranform_list.append(AsDiscrete(threshold_values=True))
else:
raise ValueError(
f"Unsupported tranform: {tranform}. Please add it to support it."
)
super().__init__(self.tranform_list)
def run_inference_test(root_dir, device=torch.device("cuda:0")):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadNiftid(keys=["img", "seg"]),
AsChannelFirstd(keys=["img", "seg"], channel_dim=-1),
ScaleIntensityd(keys=["img", "seg"]),
ToTensord(keys=["img", "seg"]),
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inferene need to input 1 image in every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
model = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model_filename = os.path.join(root_dir, "best_metric_model.pth")
model.load_state_dict(torch.load(model_filename))
model.eval()
with torch.no_grad():
metric_sum = 0.0
metric_count = 0
saver = NiftiSaver(output_dir=os.path.join(root_dir, "output"),
dtype=int)
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
sw_batch_size, roi_size = 4, (96, 96, 96)
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
value = compute_meandice(y_pred=val_outputs,
y=val_labels,
include_background=True,
to_onehot_y=False,
add_sigmoid=True)
metric_count += len(value)
metric_sum += value.sum().item()
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
saver.save_batch(
val_outputs, {
"filename_or_obj": val_data["img.filename_or_obj"],
"affine": val_data["img.affine"]
})
metric = metric_sum / metric_count
return metric
def setup(self, stage):
data_dir = "data/"
# Train imgs/masks
train_imgs = []
with open(data_dir + "train_imgs.txt", "r") as f:
train_imgs = [image.rstrip() for image in f.readlines()]
train_masks = []
with open(data_dir + "train_masks.txt", "r") as f:
train_masks = [mask.rstrip() for mask in f.readlines()]
train_dicts = [{
"image": image,
"mask": mask
} for (image, mask) in zip(train_imgs, train_masks)]
train_dicts, val_dicts = train_test_split(train_dicts, test_size=0.2)
# Basic transforms
data_keys = ["image", "mask"]
data_transforms = Compose([
AddChanneld(keys=data_keys),
NormalizeIntensityd(keys="image"),
RandCropByPosNegLabeld(
keys=data_keys,
label_key="mask",
spatial_size=self.hparams.patch_size,
num_samples=4,
image_key="image",
),
])
self.train_dataset = monai.data.CacheDataset(
data=train_dicts,
transform=Compose([data_transforms,
ToTensord(keys=data_keys)]),
cache_rate=1.0,
)
self.val_dataset = monai.data.CacheDataset(
data=val_dicts,
transform=Compose([data_transforms,
ToTensord(keys=data_keys)]),
cache_rate=1.0,
)
def test_decollation_dict(self, *transforms):
t_compose = Compose([AddChanneld(KEYS), Compose(transforms), ToTensord(KEYS)])
# If nibabel present, read from disk
if has_nib:
t_compose = Compose([LoadImaged("image"), t_compose])
dataset = CacheDataset(self.data_dict, t_compose, progress=False)
self.check_decollate(dataset=dataset)
def run_inference_test(root_dir, device="cuda:0"):
images = sorted(glob(os.path.join(root_dir, "im*.nii.gz")))
segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose(
[
LoadImaged(keys=["img", "seg"]),
EnsureChannelFirstd(keys=["img", "seg"]),
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
Spacingd(keys=["img", "seg"], pixdim=[1.2, 0.8, 0.7], mode=["bilinear", "nearest"], dtype=np.float32),
ScaleIntensityd(keys="img"),
ToTensord(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 = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
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_filename = os.path.join(root_dir, "best_metric_model.pth")
model.load_state_dict(torch.load(model_filename))
with eval_mode(model):
# resampling with align_corners=True or dtype=float64 will generate
# slight different results between PyTorch 1.5 an 1.6
saver = SaveImage(
output_dir=os.path.join(root_dir, "output"),
dtype=np.float32,
output_ext=".nii.gz",
output_postfix="seg",
mode="bilinear",
)
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
sw_batch_size, roi_size = 4, (96, 96, 96)
val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)
# decollate prediction into a list
val_outputs = [val_post_tran(i) for i in decollate_batch(val_outputs)]
val_meta = decollate_batch(val_data[PostFix.meta("img")])
# compute metrics
dice_metric(y_pred=val_outputs, y=val_labels)
for img, meta in zip(val_outputs, val_meta): # save a decollated batch of files
saver(img, meta)
return dice_metric.aggregate().item()
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.astype("uint8")).save(os.path.join(tempdir, f"img{i:d}.png"))
Image.fromarray(seg.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"),
ToTensord(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, to_onehot_y=False, sigmoid=True, reduction="mean")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(
dimensions=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():
metric_sum = 0.0
metric_count = 0
saver = PNGSaver(output_dir="./output")
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)
value = dice_metric(y_pred=val_outputs, y=val_labels)
metric_count += len(value)
metric_sum += value.item() * len(value)
val_outputs = val_outputs.sigmoid() >= 0.5
saver.save_batch(val_outputs)
metric = metric_sum / metric_count
print("evaluation metric:", metric)
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadImaged(keys="image"),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(
len(dataset),
int(MEDNIST_FULL_DATASET_LENGTH * dataset.test_frac))
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue(PostFix.meta("image") in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
with skip_if_downloading_fails():
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=True,
copy_cache=False)
_test_dataset(data)
# testing from
data = MedNISTDataset(root_dir=Path(testing_dir),
transform=transform,
section="test",
download=False)
self.assertEqual(data.get_num_classes(), 6)
_test_dataset(data)
data = MedNISTDataset(root_dir=testing_dir,
section="test",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (64, 64))
# test same dataset length with different random seed
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False,
seed=42)
_test_dataset(data)
self.assertEqual(data[0]["class_name"], "AbdomenCT")
self.assertEqual(data[0]["label"].cpu().item(), 0)
shutil.rmtree(os.path.join(testing_dir, "MedNIST"))
try:
MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(str(e).startswith("Cannot find dataset directory"))
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI607-Guys-1097-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI175-HH-1570-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI385-HH-2078-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI344-Guys-0905-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI409-Guys-0960-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI584-Guys-1129-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI253-HH-1694-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI092-HH-1436-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI574-IOP-1156-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI585-Guys-1130-T1.nii.gz"]),
]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
val_files = [{"img": img, "label": label} for img, label in zip(images, labels)]
# Define transforms for image
val_transforms = Compose(
[
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
ToTensord(keys=["img"]),
]
)
# 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
device = torch.device("cuda:0")
model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
model.load_state_dict(torch.load("best_metric_model.pth"))
model.eval()
with torch.no_grad():
num_correct = 0.0
metric_count = 0
saver = CSVSaver(output_dir="./output")
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(device), val_data["label"].to(device)
val_outputs = model(val_images).argmax(dim=1)
value = torch.eq(val_outputs, val_labels)
metric_count += len(value)
num_correct += value.sum().item()
saver.save_batch(val_outputs, val_data["img_meta_dict"])
metric = num_correct / metric_count
print("evaluation metric:", metric)
saver.finalize()
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 52)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 33, 47, 34))
try: # will start downloading if testing_dir doesn't have the Decathlon files
data = DecathlonDataset(
root_dir=testing_dir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=True,
)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(e))
if isinstance(e, RuntimeError):
# FIXME: skip MD5 check as current downloading method may fail
self.assertTrue(str(e).startswith("MD5 check"))
return # skipping this test due the network connection errors
_test_dataset(data)
data = DecathlonDataset(root_dir=testing_dir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=False)
_test_dataset(data)
data = DecathlonDataset(root_dir=testing_dir,
task="Task04_Hippocampus",
section="validation",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (33, 47, 34))
shutil.rmtree(os.path.join(testing_dir, "Task04_Hippocampus"))
try:
data = DecathlonDataset(
root_dir=testing_dir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=False,
)
except RuntimeError as e:
print(str(e))
self.assertTrue(str(e).startswith("Cannot find dataset directory"))
def get_image_transforms():
itk_reader = monai.data.ITKReader()
# Define transforms for image
image_transforms = Compose([
LoadImaged(keys=['img'], reader=itk_reader),
EnsureChannelFirstd(keys=['img']),
ScaleIntensityd(keys=['img']),
ToTensord(keys=['img']),
])
return image_transforms
def get_transforms(self):
self.logger.info("Getting transforms...")
# Setup transforms of data sets
train_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
NormalizeIntensityd(keys=["image"]),
SpatialPadd(keys=["image", "label"],
spatial_size=self.pad_crop_shape),
RandFlipd(keys=["image", "label"], prob=0.5, spatial_axis=0),
RandSpatialCropd(keys=["image", "label"],
roi_size=self.pad_crop_shape,
random_center=True,
random_size=False),
ToTensord(keys=["image", "label"]),
])
val_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
NormalizeIntensityd(keys=["image"]),
SpatialPadd(keys=["image", "label"],
spatial_size=self.pad_crop_shape),
RandSpatialCropd(
keys=["image", "label"],
roi_size=self.pad_crop_shape,
random_center=True,
random_size=False,
),
ToTensord(keys=["image", "label"]),
])
test_transforms = Compose([
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
NormalizeIntensityd(keys=["image"]),
ToTensord(keys=["image", "label"]),
])
return train_transforms, val_transforms, test_transforms
def test_values(self):
tempdir = tempfile.mkdtemp()
transform = Compose([
LoadNiftid(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 52)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 33, 47, 34))
try:
data = DecathlonDataset(root_dir=tempdir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=True)
except RuntimeError as e:
if str(e).startswith(
"download failed due to network issue or permission denied."
):
shutil.rmtree(tempdir)
return
_test_dataset(data)
data = DecathlonDataset(root_dir=tempdir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=False)
_test_dataset(data)
data = DecathlonDataset(root_dir=tempdir,
task="Task04_Hippocampus",
section="validation",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (33, 47, 34))
shutil.rmtree(os.path.join(tempdir, "Task04_Hippocampus"))
try:
data = DecathlonDataset(root_dir=tempdir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(
str(e).startswith("can not find dataset directory"))
shutil.rmtree(tempdir)
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
train_transform = Compose([
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
val_transform = LoadImaged(keys=["image", "label"])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 52)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 34, 49, 41))
cvdataset = CrossValidation(
dataset_cls=DecathlonDataset,
nfolds=5,
seed=12345,
root_dir=testing_dir,
task="Task04_Hippocampus",
section="validation",
transform=train_transform,
download=True,
)
try: # will start downloading if testing_dir doesn't have the Decathlon files
data = cvdataset.get_dataset(folds=0)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(e))
if isinstance(e, RuntimeError):
# FIXME: skip MD5 check as current downloading method may fail
self.assertTrue(str(e).startswith("md5 check"))
return # skipping this test due the network connection errors
_test_dataset(data)
# test training data for fold [1, 2, 3, 4] of 5 splits
data = cvdataset.get_dataset(folds=[1, 2, 3, 4])
self.assertTupleEqual(data[0]["image"].shape, (1, 35, 52, 33))
self.assertEqual(len(data), 208)
# test train / validation for fold 4 of 5 splits
data = cvdataset.get_dataset(folds=[4],
transform=val_transform,
download=False)
# val_transform doesn't add the channel dim to shape
self.assertTupleEqual(data[0]["image"].shape, (38, 53, 30))
self.assertEqual(len(data), 52)
data = cvdataset.get_dataset(folds=[0, 1, 2, 3])
self.assertTupleEqual(data[0]["image"].shape, (1, 34, 49, 41))
self.assertEqual(len(data), 208)
def test_deep_copy(self):
data = {"img": np.ones((1, 10, 11, 12))}
num_samples = 3
sampler = RandSpatialCropSamplesd(
keys=["img"], roi_size=(3, 3, 3), num_samples=num_samples, random_center=True, random_size=False
)
transform = Compose([ToTensord(keys="img"), sampler])
samples = transform(data)
self.assertEqual(len(samples), num_samples)
for sample in samples:
self.assertEqual(len(sample["img_transforms"]), len(transform))
def train_post_transforms(self, context: Context):
return [
ToTensord(keys=("pred", "label")),
Activationsd(keys="pred", softmax=True),
AsDiscreted(
keys=("pred", "label"),
argmax=(True, False),
to_onehot=True,
n_classes=2,
),
]
