def setUp(self):
if not has_nib:
self.skipTest("nibabel required for test_inverse")
set_determinism(seed=0)
b_size = 11
im_fname, seg_fname = (make_nifti_image(i)
for i in create_test_image_3d(101, 100, 107))
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.data_3d = [
load_ims({
"image": im_fname,
"label": seg_fname
}) for _ in range(b_size)
]
b_size = 8
im_fname, seg_fname = (
make_nifti_image(i)
for i in create_test_image_2d(62, 37, rad_max=10))
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.data_2d = [
load_ims({
"image": im_fname,
"label": seg_fname
}) for _ in range(b_size)
]
self.batch_size = 7
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 train_pre_transforms(self, context: Context):
# Dataset preparation
t: List[Any] = [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
SpatialCropForegroundd(keys=("image", "label"),
source_key="label",
spatial_size=self.roi_size),
Resized(keys=("image", "label"),
spatial_size=self.model_size,
mode=("area", "nearest")),
NormalizeIntensityd(keys="image", subtrahend=208.0,
divisor=388.0), # type: ignore
]
if self.dimension == 3:
t.append(FindAllValidSlicesd(label="label", sids="sids"))
t.extend([
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label"), device=context.device),
SelectItemsd(keys=("image", "label", "guidance")),
])
return t
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_load_spacingd_rotate(self, filename):
data = {"image": filename}
data_dict = LoadNiftid(keys="image")(data)
data_dict = AddChanneld(keys="image")(data_dict)
affine = data_dict["image.affine"]
data_dict["image.original_affine"] = data_dict["image.affine"] = (
np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]
]) @ affine)
t = time.time()
res_dict = Spacingd(keys="image",
pixdim=(1, 2, 3),
diagonal=True,
mode="zeros")(data_dict)
t1 = time.time()
print(f"time monai: {t1 - t}")
anat = nibabel.Nifti1Image(data_dict["image"][0],
data_dict["image.affine"])
ref = resample_to_output(anat, (1, 2, 3), order=1)
t2 = time.time()
print(f"time scipy: {t2 - t1}")
self.assertTrue(t2 >= t1)
np.testing.assert_allclose(data_dict["image.affine"],
res_dict["image.original_affine"])
np.testing.assert_allclose(res_dict["image.affine"], ref.affine)
if "anatomical" not in filename:
np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape)
np.testing.assert_allclose(ref.get_fdata(),
res_dict["image"][0],
atol=0.05)
else:
# different from the ref implementation (shape computed by round
# instead of ceil)
np.testing.assert_allclose(ref.get_fdata()[..., :-1],
res_dict["image"][0],
atol=0.05)
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 test_warn_random_but_has_no_invertible(self):
transforms = Compose(
[AddChanneld("image"), RandFlipd("image", prob=1.0), RandScaleIntensityd("image", 0.1, prob=1.0)]
)
with self.assertWarns(UserWarning):
tta = TestTimeAugmentation(transforms, 5, 0, orig_key="image")
tta(self.get_data(1, (20, 20), data_type=np.float32))
def test_3d_rgb(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
keys = "image"
xforms = Compose(
[
LoadImaged(keys=keys),
AddChanneld(keys=keys),
ScaleIntensityd(keys=keys),
# change to RGB color image
RepeatChanneld(keys=keys, repeats=3),
]
)
image_path = os.path.join(testing_dir, "anatomical.nii")
ims = xforms({keys: image_path})
fig = pyplot.figure() # external figure
fig, _ = matshow3d(
volume=ims[keys],
fig=fig,
figsize=(2, 2),
frames_per_row=5,
every_n=2,
frame_dim=-1,
channel_dim=0,
show=False,
)
with tempfile.TemporaryDirectory() as tempdir:
tempimg = f"{tempdir}/matshow3d_rgb_test.png"
fig.savefig(tempimg)
comp = compare_images(f"{testing_dir}/matshow3d_rgb_test.png", tempimg, 5e-2)
self.assertIsNone(comp, f"value of comp={comp}") # None indicates test passed
def get_data(keys):
"""Get the example data to be used.
Use MarsAtlas as it only contains 1 image for quick download and
that image is parcellated.
"""
cache_dir = os.environ.get("MONAI_DATA_DIRECTORY") or tempfile.mkdtemp()
fname = "MarsAtlas-MNI-Colin27.zip"
url = "https://www.dropbox.com/s/ndz8qtqblkciole/" + fname + "?dl=1"
out_path = os.path.join(cache_dir, "MarsAtlas-MNI-Colin27")
zip_path = os.path.join(cache_dir, fname)
download_and_extract(url, zip_path, out_path)
image, label = sorted(glob(os.path.join(out_path, "*.nii")))
data = {CommonKeys.IMAGE: image, CommonKeys.LABEL: label}
transforms = Compose([
LoadImaged(keys),
AddChanneld(keys),
ScaleIntensityd(CommonKeys.IMAGE),
Rotate90d(keys, spatial_axes=[0, 2])
])
data = transforms(data)
max_size = max(data[keys[0]].shape)
padder = SpatialPadd(keys, (max_size, max_size, max_size))
return padder(data)
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 test_samples(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
keys = "image"
xforms = Compose(
[
LoadImaged(keys=keys),
AddChanneld(keys=keys),
ScaleIntensityd(keys=keys),
RandSpatialCropSamplesd(keys=keys, roi_size=(8, 8, 5), random_size=True, num_samples=10),
]
)
image_path = os.path.join(testing_dir, "anatomical.nii")
xforms.set_random_state(0)
ims = xforms({keys: image_path})
fig, mat = matshow3d(
[im[keys] for im in ims], title=f"testing {keys}", figsize=(2, 2), frames_per_row=5, every_n=2, show=False
)
self.assertTrue(mat.dtype == np.float32)
with tempfile.TemporaryDirectory() as tempdir:
tempimg = f"{tempdir}/matshow3d_patch_test.png"
fig.savefig(tempimg)
comp = compare_images(f"{testing_dir}/matshow3d_patch_test.png", tempimg, 5e-2, in_decorator=True)
if comp:
print("not none comp: ", comp) # matplotlib 3.2.2
np.testing.assert_allclose(comp["rms"], 30.786983, atol=1e-3, rtol=1e-3)
else:
self.assertIsNone(comp, f"value of comp={comp}") # None indicates test passed
def pre_transforms(self, data=None) -> Sequence[Callable]:
t = [
LoadImaged(keys="image"),
AsChannelFirstd(keys="image"),
Spacingd(keys="image",
pixdim=[1.0] * self.dimension,
mode="bilinear"),
AddGuidanceFromPointsd(ref_image="image",
guidance="guidance",
dimensions=self.dimension),
]
if self.dimension == 2:
t.append(Fetch2DSliced(keys="image", guidance="guidance"))
t.extend([
AddChanneld(keys="image"),
SpatialCropGuidanced(keys="image",
guidance="guidance",
spatial_size=self.spatial_size),
Resized(keys="image", spatial_size=self.model_size, mode="area"),
ResizeGuidanced(guidance="guidance", ref_image="image"),
NormalizeIntensityd(keys="image", subtrahend=208,
divisor=388), # type: ignore
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys="image",
device=data.get("device") if data else None),
])
return t
def setUp(self):
if not has_nib:
self.skipTest("nibabel required for test_inverse")
set_determinism(seed=0)
self.all_data = {}
affine = make_rand_affine()
affine[0] *= 2
for size in [10, 11]:
# pad 5 onto both ends so that cropping can be lossless
im_1d = np.pad(np.arange(size), 5)[None]
name = "1D even" if size % 2 == 0 else "1D odd"
self.all_data[name] = {
"image": np.array(im_1d, copy=True),
"label": np.array(im_1d, copy=True),
"other": np.array(im_1d, copy=True),
}
im_2d_fname, seg_2d_fname = [make_nifti_image(i) for i in create_test_image_2d(101, 100)]
im_3d_fname, seg_3d_fname = [make_nifti_image(i, affine) for i in create_test_image_3d(100, 101, 107)]
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.all_data["2D"] = load_ims({"image": im_2d_fname, "label": seg_2d_fname})
self.all_data["3D"] = load_ims({"image": im_3d_fname, "label": seg_3d_fname})
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_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 train_pre_transforms(self, context: Context):
return [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
Spacingd(
keys=("image", "label"),
pixdim=(1.0, 1.0, 1.0),
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(keys="image",
a_min=-57,
a_max=164,
b_min=0.0,
b_max=1.0,
clip=True),
CropForegroundd(keys=("image", "label"), source_key="image"),
EnsureTyped(keys=("image", "label"), device=context.device),
RandCropByPosNegLabeld(
keys=("image", "label"),
label_key="label",
spatial_size=(96, 96, 96),
pos=1,
neg=1,
num_samples=4,
image_key="image",
image_threshold=0,
),
RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5),
SelectItemsd(keys=("image", "label")),
]
def test_load_spacingd_rotate(self, filename):
data = {"image": filename}
data_dict = LoadNiftid(keys="image")(data)
data_dict = AddChanneld(keys="image")(data_dict)
affine = data_dict["image.affine"]
data_dict["image.original_affine"] = data_dict["image.affine"] = (
np.array([[0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0], [0, 0, 0, 1]
]) @ affine)
res_dict = Spacingd(keys="image",
pixdim=(1, 2, 3),
diagonal=True,
mode="constant")(data_dict)
np.testing.assert_allclose(data_dict["image.affine"],
res_dict["image.original_affine"])
anat = nibabel.Nifti1Image(data_dict["image"][0],
data_dict["image.affine"])
ref = resample_to_output(anat, (1, 2, 3))
np.testing.assert_allclose(res_dict["image.affine"], ref.affine)
if "anatomical" not in filename:
np.testing.assert_allclose(res_dict["image"].shape[1:], ref.shape)
np.testing.assert_allclose(ref.get_fdata(), res_dict["image"][0])
else:
# different from the ref implementation (shape computed by round
# instead of ceil)
np.testing.assert_allclose(ref.get_fdata()[..., :-1],
res_dict["image"][0])
def get_seg_transforms(end_seg_axcodes):
seg_transforms = Compose([
LoadNiftid(keys=["image"], as_closest_canonical=False),
AddChanneld(keys=["image"]),
Orientationd(keys=["image"], axcodes=end_seg_axcodes),
])
return seg_transforms
def test_epistemic_scoring(self):
input_size = (20, 20, 20)
device = "cuda" if torch.cuda.is_available() else "cpu"
keys = ["image", "label"]
num_training_ims = 10
train_data = self.get_data(num_training_ims, input_size)
test_data = self.get_data(1, input_size)
transforms = Compose([
AddChanneld(keys),
CropForegroundd(keys, source_key="image"),
DivisiblePadd(keys, 4),
])
infer_transforms = Compose([
AddChannel(),
CropForeground(),
DivisiblePad(4),
])
train_ds = CacheDataset(train_data, transforms)
# output might be different size, so pad so that they match
train_loader = DataLoader(train_ds,
batch_size=2,
collate_fn=pad_list_data_collate)
model = UNet(3, 1, 1, channels=(6, 6), strides=(2, 2)).to(device)
loss_function = DiceLoss(sigmoid=True)
optimizer = torch.optim.Adam(model.parameters(), 1e-3)
num_epochs = 10
for _ in trange(num_epochs):
epoch_loss = 0
for batch_data in train_loader:
inputs, labels = batch_data["image"].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_loss /= len(train_loader)
entropy_score = EpistemicScoring(model=model,
transforms=infer_transforms,
roi_size=[20, 20, 20],
num_samples=10)
# Call Individual Infer from Epistemic Scoring
ip_stack = [test_data["image"], test_data["image"], test_data["image"]]
ip_stack = np.array(ip_stack)
score_3d = entropy_score.entropy_3d_volume(ip_stack)
score_3d_sum = np.sum(score_3d)
# Call Entropy Metric from Epistemic Scoring
self.assertEqual(score_3d.shape, input_size)
self.assertIsInstance(score_3d_sum, np.float32)
self.assertGreater(score_3d_sum, 3.0)
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 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 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_load_spacingd_rotate_non_diag(self):
data = {'image': FILES[0]}
data_dict = LoadNiftid(keys='image')(data)
data_dict = AddChanneld(keys='image')(data_dict)
res_dict = Spacingd(keys='image', pixdim=(1, 2, 3), diagonal=False, mode='nearest')(data_dict)
np.testing.assert_allclose(data_dict['image.affine'], res_dict['image.original_affine'])
np.testing.assert_allclose(
res_dict['image.affine'],
np.array([[-1., 0., 0., 32.], [0., 2., 0., -40.], [0., 0., 3., -16.], [0., 0., 0., 1.]]))
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_inverse_inferred_seg(self):
test_data = []
for _ in range(20):
image, label = create_test_image_2d(100, 101)
test_data.append({
"image": image,
"label": label.astype(np.float32)
})
batch_size = 10
# num workers = 0 for mac
num_workers = 2 if sys.platform != "darwin" else 0
transforms = Compose([
AddChanneld(KEYS),
SpatialPadd(KEYS, (150, 153)),
CenterSpatialCropd(KEYS, (110, 99))
])
num_invertible_transforms = sum(1 for i in transforms.transforms
if isinstance(i, InvertibleTransform))
dataset = CacheDataset(test_data, transform=transforms, progress=False)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = UNet(
dimensions=2,
in_channels=1,
out_channels=1,
channels=(2, 4),
strides=(2, ),
).to(device)
data = first(loader)
labels = data["label"].to(device)
segs = model(labels).detach().cpu()
label_transform_key = "label" + InverseKeys.KEY_SUFFIX.value
segs_dict = {
"label": segs,
label_transform_key: data[label_transform_key]
}
segs_dict_decollated = decollate_batch(segs_dict)
# inverse of individual segmentation
seg_dict = first(segs_dict_decollated)
with allow_missing_keys_mode(transforms):
inv_seg = transforms.inverse(seg_dict)["label"]
self.assertEqual(len(data["label_transforms"]),
num_invertible_transforms)
self.assertEqual(len(seg_dict["label_transforms"]),
num_invertible_transforms)
self.assertEqual(inv_seg.shape[1:], test_data[0]["label"].shape)
def test_inverse_inferred_seg(self, extra_transform):
test_data = []
for _ in range(20):
image, label = create_test_image_2d(100, 101)
test_data.append({
"image": image,
"label": label.astype(np.float32)
})
batch_size = 10
# num workers = 0 for mac
num_workers = 2 if sys.platform == "linux" else 0
transforms = Compose([
AddChanneld(KEYS),
SpatialPadd(KEYS, (150, 153)), extra_transform
])
dataset = CacheDataset(test_data, transform=transforms, progress=False)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = UNet(spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(2, 4),
strides=(1, )).to(device)
data = first(loader)
self.assertEqual(data["image"].shape[0], batch_size * NUM_SAMPLES)
labels = data["label"].to(device)
self.assertIsInstance(labels, MetaTensor)
segs = model(labels).detach().cpu()
segs_decollated = decollate_batch(segs)
self.assertIsInstance(segs_decollated[0], MetaTensor)
# inverse of individual segmentation
seg_metatensor = first(segs_decollated)
# test to convert interpolation mode for 1 data of model output batch
convert_applied_interp_mode(seg_metatensor.applied_operations,
mode="nearest",
align_corners=None)
# manually invert the last crop samples
xform = seg_metatensor.applied_operations.pop(-1)
shape_before_extra_xform = xform["orig_size"]
resizer = ResizeWithPadOrCrop(spatial_size=shape_before_extra_xform)
with resizer.trace_transform(False):
seg_metatensor = resizer(seg_metatensor)
with allow_missing_keys_mode(transforms):
inv_seg = transforms.inverse({"label": seg_metatensor})["label"]
self.assertEqual(inv_seg.shape[1:], test_data[0]["label"].shape)
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 setUp(self):
if not has_nib:
self.skipTest("nibabel required for test_inverse")
set_determinism(seed=0)
im_fname, seg_fname = [make_nifti_image(i) for i in create_test_image_3d(101, 100, 107)]
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.batch_size = 10
self.data = [load_ims({"image": im_fname, "label": seg_fname}) for _ in range(self.batch_size)]
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