def test_nvtx_transfroms_array(self, input):
# with prob == 0.0
transforms = Compose([
RandMark("Mark: Transforms Start!"),
RandRangePush("Range: RandFlip"),
RandFlip(prob=0.0),
RandRangePop(),
RangePush("Range: ToTensor"),
ToTensor(),
RangePop(),
Mark("Mark: Transforms End!"),
])
output = transforms(input)
self.assertIsInstance(output, torch.Tensor)
np.testing.assert_array_equal(input, output)
# with prob == 1.0
transforms = Compose([
RandMark("Mark: Transforms Start!"),
RandRangePush("Range: RandFlip"),
RandFlip(prob=1.0),
RandRangePop(),
RangePush("Range: ToTensor"),
ToTensor(),
RangePop(),
Mark("Mark: Transforms End!"),
])
output = transforms(input)
self.assertIsInstance(output, torch.Tensor)
np.testing.assert_array_equal(input, Flip()(output.numpy()))
def test_correct_results(self, _, spatial_axis):
flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)
expected = list()
for channel in self.imt[0]:
expected.append(np.flip(channel, spatial_axis))
expected = np.stack(expected)
self.assertTrue(np.allclose(expected, flip(self.imt[0])))
def test_correct_results(self, _, spatial_axis):
for p in TEST_NDARRAYS:
im = p(self.imt[0])
flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)
expected = [
np.flip(channel, spatial_axis) for channel in self.imt[0]
]
expected = np.stack(expected)
result = flip(im)
assert_allclose(result, p(expected))
def test_correct_results(self, _, spatial_axis):
for p in TEST_NDARRAYS_ALL:
im = p(self.imt[0])
flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)
set_track_meta(False)
result = flip(im)
self.assertNotIsInstance(result, MetaTensor)
self.assertIsInstance(result, torch.Tensor)
set_track_meta(True)
expected = [
np.flip(channel, spatial_axis) for channel in self.imt[0]
]
expected = np.stack(expected)
result = flip(im)
assert_allclose(result, p(expected), type_test="tensor")
test_local_inversion(flip, result, im)
def test_invert(self):
set_determinism(seed=0)
im_fname = make_nifti_image(create_test_image_3d(101, 100, 107, noise_max=100)[1]) # label image, discrete
data = [im_fname for _ in range(12)]
transform = Compose(
[
LoadImage(image_only=True),
EnsureChannelFirst(),
Orientation("RPS"),
Spacing(pixdim=(1.2, 1.01, 0.9), mode="bilinear", dtype=np.float32),
RandFlip(prob=0.5, spatial_axis=[1, 2]),
RandAxisFlip(prob=0.5),
RandRotate90(prob=0, spatial_axes=(1, 2)),
RandZoom(prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True),
RandRotate(prob=0.5, range_x=np.pi, mode="bilinear", align_corners=True, dtype=np.float64),
RandAffine(prob=0.5, rotate_range=np.pi, mode="nearest"),
ResizeWithPadOrCrop(100),
CastToType(dtype=torch.uint8),
]
)
# num workers = 0 for mac or gpu transforms
num_workers = 0 if sys.platform != "linux" or torch.cuda.is_available() else 2
dataset = Dataset(data, transform=transform)
self.assertIsInstance(transform.inverse(dataset[0]), MetaTensor)
loader = DataLoader(dataset, num_workers=num_workers, batch_size=1)
inverter = Invert(transform=transform, nearest_interp=True, device="cpu")
for d in loader:
d = decollate_batch(d)
for item in d:
orig = deepcopy(item)
i = inverter(item)
self.assertTupleEqual(orig.shape[1:], (100, 100, 100))
# check the nearest interpolation mode
torch.testing.assert_allclose(i.to(torch.uint8).to(torch.float), i.to(torch.float))
self.assertTupleEqual(i.shape[1:], (100, 101, 107))
# check labels match
reverted = i.detach().cpu().numpy().astype(np.int32)
original = LoadImage(image_only=True)(data[-1])
n_good = np.sum(np.isclose(reverted, original.numpy(), atol=1e-3))
reverted_name = i.meta["filename_or_obj"]
original_name = original.meta["filename_or_obj"]
self.assertEqual(reverted_name, original_name)
print("invert diff", reverted.size - n_good)
self.assertTrue((reverted.size - n_good) < 300000, f"diff. {reverted.size - n_good}")
set_determinism(seed=None)
def test_tranform_randomized(self, input):
# Compose deterministic and randomized transforms
transforms = Compose([
Range("flip")(Flip()),
Rotate90(),
Range()(RandAdjustContrast(prob=0.0)),
Range("random flip")(RandFlip(prob=1.0)),
ToTensor(),
])
# Apply transforms
output = transforms(input)
# 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)
output2 = transforms2(input)
output3 = transforms3(input)
# Check if the outputs are equal
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())
# Check if the first randomized is RandAdjustContrast
for tran in transforms.transforms:
if isinstance(tran, Randomizable):
self.assertIsInstance(tran, RandAdjustContrast)
break
def run_training_test(root_dir,
train_x,
train_y,
val_x,
val_y,
device="cuda:0",
num_workers=10):
monai.config.print_config()
# define transforms for image and classification
train_transforms = Compose([
LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),
RandFlip(spatial_axis=0, prob=0.5),
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensor(),
])
train_transforms.set_random_state(1234)
val_transforms = Compose(
[LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
ToTensor()])
# create train, val data loaders
train_ds = MedNISTDataset(train_x, train_y, train_transforms)
train_loader = DataLoader(train_ds,
batch_size=300,
shuffle=True,
num_workers=num_workers)
val_ds = MedNISTDataset(val_x, val_y, val_transforms)
val_loader = DataLoader(val_ds, batch_size=300, num_workers=num_workers)
model = densenet121(spatial_dims=2,
in_channels=1,
out_channels=len(np.unique(train_y))).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
epoch_num = 4
val_interval = 1
# start training validation
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
model_filename = os.path.join(root_dir, "best_metric_model.pth")
for epoch in range(epoch_num):
print("-" * 10)
print(f"Epoch {epoch + 1}/{epoch_num}")
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_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss:{epoch_loss:0.4f}")
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[0].to(
device), val_data[1].to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
auc_metric = compute_roc_auc(y_pred,
y,
to_onehot_y=True,
softmax=True)
metric_values.append(auc_metric)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
acc_metric = acc_value.sum().item() / len(acc_value)
if auc_metric > best_metric:
best_metric = auc_metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), model_filename)
print("saved new best metric model")
print(
f"current epoch {epoch +1} current AUC: {auc_metric:0.4f} "
f"current accuracy: {acc_metric:0.4f} best AUC: {best_metric:0.4f} at epoch {best_metric_epoch}"
)
print(
f"train completed, best_metric: {best_metric:0.4f} at epoch: {best_metric_epoch}"
)
return epoch_loss_values, best_metric, best_metric_epoch
from monai.transforms.spatial.dictionary import RandAffined, RandRotate90d
from monai.utils import optional_import, set_determinism
from monai.utils.enums import InverseKeys
from tests.utils import make_nifti_image
_, has_nib = optional_import("nibabel")
KEYS = ["image"]
TESTS_DICT: List[Tuple] = []
TESTS_DICT.append((SpatialPadd(KEYS, 150), RandFlipd(KEYS, prob=1.0, spatial_axis=1)))
TESTS_DICT.append((RandRotate90d(KEYS, prob=0.0, max_k=1),))
TESTS_DICT.append((RandAffined(KEYS, prob=0.0, translate_range=10),))
TESTS_LIST: List[Tuple] = []
TESTS_LIST.append((SpatialPad(150), RandFlip(prob=1.0, spatial_axis=1)))
TESTS_LIST.append((RandRotate90(prob=0.0, max_k=1),))
TESTS_LIST.append((RandAffine(prob=0.0, translate_range=10),))
TEST_BASIC = [
[("channel", "channel"), ["channel", "channel"]],
[torch.Tensor([1, 2, 3]), [torch.tensor(1.0), torch.tensor(2.0), torch.tensor(3.0)]],
[
[[torch.Tensor((1.0, 2.0, 3.0)), torch.Tensor((2.0, 3.0, 1.0))]],
[
[[torch.tensor(1.0), torch.tensor(2.0)]],
[[torch.tensor(2.0), torch.tensor(3.0)]],
[[torch.tensor(3.0), torch.tensor(1.0)]],
],
],
def test_invalid_inputs(self, _, spatial_axis, raises):
with self.assertRaises(raises):
flip = RandFlip(prob=1.0, spatial_axis=spatial_axis)
flip(self.imt[0])
train_indices = indices[val_split:]
train_x = [image_files_list[i] for i in train_indices]
train_y = [image_class[i] for i in train_indices]
val_x = [image_files_list[i] for i in val_indices]
val_y = [image_class[i] for i in val_indices]
test_x = [image_files_list[i] for i in test_indices]
test_y = [image_class[i] for i in test_indices]
# MONAI transforms, Dataset and Dataloader for preprocessing
train_transforms = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),
RandFlip(spatial_axis=0, prob=0.5),
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensor(),
])
val_transforms = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
ToTensor()
])
act = Activations(softmax=True)
to_onehot = AsDiscrete(to_onehot=True, n_classes=num_class)
class MedNISTDataset(torch.utils.data.Dataset):
TEST_CASE_DICT_0 = [{"image": np.random.randn(3, 3)}]
TEST_CASE_DICT_1 = [{"image": np.random.randn(3, 10, 10)}]
TEST_CASE_TORCH_0 = [torch.randn(3, 3)]
TEST_CASE_TORCH_1 = [torch.randn(3, 10, 10)]
TEST_CASE_WRAPPER = [np.random.randn(3, 10, 10)]
TEST_CASE_RECURSIVE_0 = [
torch.randn(3, 3),
Compose([
ToNumpy(),
Flip(),
RandAdjustContrast(prob=0.0),
RandFlip(prob=1.0),
ToTensor()
]),
]
TEST_CASE_RECURSIVE_1 = [
torch.randn(3, 3),
Compose([
ToNumpy(),
Flip(),
Compose([RandAdjustContrast(prob=0.0),
RandFlip(prob=1.0)]),
ToTensor()
]),
]
TEST_CASE_RECURSIVE_2 = [
torch.randn(3, 3),
class Loader():
"""Loader for different image datasets with built in split function and download if needed.
Functions:
load_IXIT1: Loads the IXIT1 3D brain MRI dataset.
load_MedNIST: Loads the MedNIST 2D image dataset.
"""
ixi_train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90()])
ixi_test_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96))])
mednist_train_transforms = Compose([LoadImage(image_only=True), AddChannel(), ScaleIntensity(),
RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),
RandFlip(spatial_axis=0, prob=0.5),
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5)])
mednist_test_transforms = Compose([LoadImage(image_only=True), AddChannel(), ScaleIntensity()])
@staticmethod
def load_IXIT1(download: bool = False, train_transforms: object = ixi_train_transforms,
test_transforms: object = ixi_test_transforms, test_size: float = 0.2,
val_size: float = 0.0, sample_size: float = 0.01, shuffle: bool = True):
"""Loads the IXIT1 3D Brain MRI dataset.
Consists of ~566 images of 3D Brain MRI scans and labels (0) for male and (1) for female.
Args:
download (bool): If true, then data is downloaded before loading it as dataset.
train_transforms (Compose): Specify the transformations to be applied to the training dataset.
test_transforms (Compose): Specify the transformations to be applied to the test dataset.
sample_size (float): Percentage of available images to be used.
test_size (float): Precantage of sample to be used as test data.
val_size (float): Percentage of sample to be used as validation data.
shuffle (bool): Whether or not the data should be shuffled after loading.
"""
# Download data if needed
if download:
data_url = 'http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar'
compressed_file = os.sep.join(['Data', 'IXI-T1.tar'])
data_dir = os.sep.join(['Data', 'IXI-T1'])
# Data download
monai.apps.download_and_extract(data_url, compressed_file, './Data/IXI-T1')
# Labels document download
labels_url = 'http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI.xls'
monai.apps.download_url(labels_url, './Data/IXI.xls')
# Get all the images and corresponding Labels
images = [impath for impath in os.listdir('./Data/IXI-T1')]
df = pd.read_excel('./Data/IXI.xls')
data = []
labels = []
for i in images:
ixi_id = int(i[3:6])
row = df.loc[df['IXI_ID'] == ixi_id]
if not row.empty:
data.append(os.sep.join(['Data', 'IXI-T1', i]))
labels.append(int(row.iat[0, 1] - 1)) # Sex labels are 1/2 but need to be 0/1
data, labels = data[:int(len(data) * sample_size)], labels[:int(len(data) * sample_size)]
# Make train test validation split
train_data, train_labels, test_data, test_labels, val_data, val_labels = _split(data, labels,
test_size, val_size)
# Construct and return Datasets
train_ds = IXIT1Dataset(train_data, train_labels, train_transforms, shuffle)
test_ds = IXIT1Dataset(test_data, test_labels, test_transforms, shuffle)
if val_size == 0:
return train_ds, test_ds
else:
val_ds = IXIT1Dataset(val_data, val_labels, test_transforms, shuffle)
return train_ds, test_ds, val_ds
@staticmethod
def load_MedNIST(download: bool = False, train_transforms: object = mednist_train_transforms,
test_transforms: object = mednist_test_transforms, test_size: float = 0.2,
val_size: float = 0.0, sample_size: float = 0.01, shuffle: bool = True):
"""Loads the MedNIST 2D image dataset.
Consists of ~60.000 2D images from 6 classes: AbdomenCT, BreastMRI, ChestCT, CXR, Hand, HeadCT.
Args:
download (bool): If true, then data is downloaded before loading it as dataset.
train_transforms (Compose): Specify the transformations to be applied to the training dataset.
test_transforms (Compose): Specify the transformations to be applied to the test dataset.
sample_size (float): Percentage of available images to be used.
test_size (float): Precantage of sample to be used as test data.
val_size (float): Percentage of sample to be used as validation data.
shuffle (bool): Whether or not the data should be shuffled after loading.
"""
root_dir = './Data'
resource = "https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1"
md5 = "0bc7306e7427e00ad1c5526a6677552d"
compressed_file = os.path.join(root_dir, "MedNIST.tar.gz")
data_dir = os.path.join(root_dir, "MedNIST")
if download:
monai.apps.download_and_extract(resource, compressed_file, root_dir, md5)
# Reading image filenames from dataset folders and assigning labels
class_names = sorted(x for x in os.listdir(data_dir)
if os.path.isdir(os.path.join(data_dir, x)))
num_class = len(class_names)
image_files = [
[
os.path.join(data_dir, class_names[i], x)
for x in os.listdir(os.path.join(data_dir, class_names[i]))
]
for i in range(num_class)
]
image_files = [images[:int(len(images) * sample_size)] for images in image_files]
# Constructing data and labels
num_each = [len(image_files[i]) for i in range(num_class)]
data = []
labels = []
for i in range(num_class):
data.extend(image_files[i])
labels.extend([int(i)] * num_each[i])
if shuffle:
np.random.seed(42)
indicies = np.arange(len(data))
np.random.shuffle(indicies)
data = [data[i] for i in indicies]
labels = [labels[i] for i in indicies]
# Make train test validation split
train_data, train_labels, test_data, test_labels, val_data, val_labels = _split(data, labels,
test_size, val_size)
# Construct and return datasets
train_ds = MedNISTDataset(train_data, train_labels, train_transforms, shuffle)
test_ds = MedNISTDataset(test_data, test_labels, test_transforms, shuffle)
if val_size == 0:
return train_ds, test_ds
else:
val_ds = MedNISTDataset(val_data, val_labels, test_transforms, shuffle)
return train_ds, test_ds, val_ds
is_val_split=is_val_split)
# data preprocessing/augmentation
trans_train = MozartTheComposer([
#ScaleIntensity(),
# AddChannel(),
# RandSpatialCrop(roi_size=256, random_size=False),
#CenterSpatialCrop(roi_size=2154), # 2154
# RandScaleIntensity(factors=0.25, prob=aug_prob),
RandRotate(range_x=15,
prob=aug_prob,
keep_size=True,
padding_mode="reflection"),
RandRotate90(prob=aug_prob, spatial_axes=(1, 2)),
RandFlip(spatial_axis=(1, 2), prob=aug_prob),
ToTensor()
])
trans_val = MozartTheComposer([
# LoadImage(PILReader(), image_only=True),
#ScaleIntensity(),
# AddChannel(),
# RandSpatialCrop(roi_size=256, random_size=False),
#CenterSpatialCrop(roi_size=2154),
ToTensor()
])
# create dataset class
train_dataset = OurDataset(data=train_split,
data_reader=PILReader(),
def transform_and_copy(data, cahce_dir):
copy_dir = os.path.join(cahce_dir, 'copied_images')
if not os.path.exists(copy_dir):
os.mkdir(copy_dir)
copy_list_path = os.path.join(copy_dir, 'copied_images.npy')
if not os.path.exists(copy_list_path):
print("transforming and copying images...")
imageLoader = LoadImage()
to_copy_list = [x for x in data if int(x['_label']) == 1]
mul = 1 #int(len(data)/len(to_copy_list) - 1)
rand_x_flip = RandFlip(spatial_axis=0, prob=0.50)
rand_y_flip = RandFlip(spatial_axis=1, prob=0.50)
rand_z_flip = RandFlip(spatial_axis=2, prob=0.50)
rand_affine = RandAffine(prob=1.0,
rotate_range=(0, 0, np.pi / 10),
shear_range=(0.12, 0.12, 0.0),
translate_range=(0, 0, 0),
scale_range=(0.12, 0.12, 0.0),
padding_mode="zeros")
rand_gaussian_noise = RandGaussianNoise(prob=0.5, mean=0.0, std=0.05)
transform = Compose([
AddChannel(),
rand_x_flip,
rand_y_flip,
rand_z_flip,
rand_affine,
SqueezeDim(),
])
copy_list = []
n = len(to_copy_list)
for i in range(len(to_copy_list)):
print(f'Copying image {i+1}/{n}', end="\r")
to_copy = to_copy_list[i]
image_file = to_copy['image']
_image_file = replace_suffix(image_file, '.nii.gz', '')
label = to_copy['label']
_label = to_copy['_label']
image_data, _ = imageLoader(image_file)
seg_file = to_copy['seg']
seg_data, _ = nrrd.read(seg_file)
for i in range(mul):
rand_seed = np.random.randint(1e8)
transform.set_random_state(seed=rand_seed)
new_image_data = rand_gaussian_noise(
np.array(transform(image_data)))
transform.set_random_state(seed=rand_seed)
new_seg_data = np.array(transform(seg_data))
#multi_slice_viewer(image_data, image_file)
#multi_slice_viewer(seg_data, seg_file)
#seg_image = MaskIntensity(seg_data)(image_data)
#multi_slice_viewer(seg_image, seg_file)
image_basename = os.path.basename(_image_file)
seg_basename = image_basename + f'_seg_{i}.nrrd'
image_basename = image_basename + f'_{i}.nii.gz'
new_image_file = os.path.join(copy_dir, image_basename)
write_nifti(new_image_data, new_image_file, resample=False)
new_seg_file = os.path.join(copy_dir, seg_basename)
nrrd.write(new_seg_file, new_seg_data)
copy_list.append({
'image': new_image_file,
'seg': new_seg_file,
'label': label,
'_label': _label
})
np.save(copy_list_path, copy_list)
print("done transforming and copying!")
copy_list = np.load(copy_list_path, allow_pickle=True)
return copy_list
for i, elem in enumerate(image_files_list):
if elem in list_all_images:
image_files_list_updated.append(elem)
image_class_list.append(image_class[i])
"""
transforms
"""
train_transforms = Compose(
[
LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
RandRotate(range_x=15, prob=0.1, keep_size=True), # low probability for rotation
RandFlip(spatial_axis=0, prob=0.5),# left right flip
RandFlip(spatial_axis=1, prob=0.5), # horizontal flip
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensor(),
Lambda(lambda x: torch.cat([x, x, x], 0)),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]
)
val_transforms = Compose(
[
LoadPNG(image_only=True),
# Resize((480,640)),
AddChannel(),
ScaleIntensity(),
ToTensor(),
