def test_invert(self):
set_determinism(seed=0)
im_fname, seg_fname = [
make_nifti_image(i)
for i in create_test_image_3d(101, 100, 107, noise_max=100)
]
transform = Compose([
LoadImaged(KEYS),
AddChanneld(KEYS),
Orientationd(KEYS, "RPS"),
Spacingd(KEYS,
pixdim=(1.2, 1.01, 0.9),
mode=["bilinear", "nearest"],
dtype=np.float32),
ScaleIntensityd("image", minv=1, maxv=10),
RandFlipd(KEYS, prob=0.5, spatial_axis=[1, 2]),
RandAxisFlipd(KEYS, prob=0.5),
RandRotate90d(KEYS, spatial_axes=(1, 2)),
RandZoomd(KEYS,
prob=0.5,
min_zoom=0.5,
max_zoom=1.1,
keep_size=True),
RandRotated(KEYS,
prob=0.5,
range_x=np.pi,
mode="bilinear",
align_corners=True),
RandAffined(KEYS, prob=0.5, rotate_range=np.pi, mode="nearest"),
ResizeWithPadOrCropd(KEYS, 100),
ToTensord(KEYS),
CastToTyped(KEYS, dtype=torch.uint8),
])
data = [{"image": im_fname, "label": seg_fname} for _ in range(12)]
# num workers = 0 for mac or gpu transforms
num_workers = 0 if sys.platform == "darwin" or torch.cuda.is_available(
) else 2
dataset = CacheDataset(data, transform=transform, progress=False)
loader = DataLoader(dataset, num_workers=num_workers, batch_size=5)
# set up engine
def _train_func(engine, batch):
self.assertTupleEqual(batch["image"].shape[1:], (1, 100, 100, 100))
engine.state.output = batch
engine.fire_event(IterationEvents.MODEL_COMPLETED)
return engine.state.output
engine = Engine(_train_func)
engine.register_events(*IterationEvents)
# set up testing handler
TransformInverter(
transform=transform,
loader=loader,
output_keys=["image", "label"],
batch_keys="label",
nearest_interp=True,
num_workers=0
if sys.platform == "darwin" or torch.cuda.is_available() else 2,
).attach(engine)
engine.run(loader, max_epochs=1)
set_determinism(seed=None)
self.assertTupleEqual(engine.state.output["image"].shape,
(2, 1, 100, 100, 100))
self.assertTupleEqual(engine.state.output["label"].shape,
(2, 1, 100, 100, 100))
for i in engine.state.output["image_inverted"] + engine.state.output[
"label_inverted"]:
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 = engine.state.output["label_inverted"][-1].detach().cpu(
).numpy()[0].astype(np.int32)
original = LoadImaged(KEYS)(data[-1])["label"]
n_good = np.sum(np.isclose(reverted, original, atol=1e-3))
reverted_name = engine.state.output["label_meta_dict"][
"filename_or_obj"][-1]
original_name = data[-1]["label"]
self.assertEqual(reverted_name, original_name)
print("invert diff", reverted.size - n_good)
self.assertTrue((reverted.size - n_good) in (25300, 1812),
"diff. in two possible values")
def test_fail_random_but_not_invertible(self):
transforms = Compose(
[AddChanneld("im"),
Rand2DElasticd("im", None, None)])
with self.assertRaises(RuntimeError):
TestTimeAugmentation(transforms, None, None, None)
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadImaged(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, 36, 47, 44))
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)
# test validation without transforms
data = DecathlonDataset(root_dir=testing_dir,
task="Task04_Hippocampus",
section="validation",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (36, 47, 44))
self.assertEqual(len(data), 52)
data = DecathlonDataset(root_dir=testing_dir,
task="Task04_Hippocampus",
section="training",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (34, 56, 31))
self.assertEqual(len(data), 208)
# test dataset properties
data = DecathlonDataset(root_dir=testing_dir,
task="Task04_Hippocampus",
section="validation",
download=False)
properties = data.get_properties(keys="labels")
self.assertDictEqual(properties["labels"], {
"0": "background",
"1": "Anterior",
"2": "Posterior"
})
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 main():
print_config()
# Define paths for running the script
data_dir = os.path.normpath('/to/be/defined')
json_path = os.path.normpath('/to/be/defined')
logdir = os.path.normpath('/to/be/defined')
# If use_pretrained is set to 0, ViT weights will not be loaded and random initialization is used
use_pretrained = 1
pretrained_path = os.path.normpath('/to/be/defined')
# Training Hyper-parameters
lr = 1e-4
max_iterations = 30000
eval_num = 100
if os.path.exists(logdir) == False:
os.mkdir(logdir)
# Training & Validation Transform chain
train_transforms = Compose([
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=(1.5, 1.5, 2.0),
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(
keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True,
),
CropForegroundd(keys=["image", "label"], source_key="image"),
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,
),
RandFlipd(
keys=["image", "label"],
spatial_axis=[0],
prob=0.10,
),
RandFlipd(
keys=["image", "label"],
spatial_axis=[1],
prob=0.10,
),
RandFlipd(
keys=["image", "label"],
spatial_axis=[2],
prob=0.10,
),
RandRotate90d(
keys=["image", "label"],
prob=0.10,
max_k=3,
),
RandShiftIntensityd(
keys=["image"],
offsets=0.10,
prob=0.50,
),
ToTensord(keys=["image", "label"]),
])
val_transforms = Compose([
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Orientationd(keys=["image", "label"], axcodes="RAS"),
Spacingd(
keys=["image", "label"],
pixdim=(1.5, 1.5, 2.0),
mode=("bilinear", "nearest"),
),
ScaleIntensityRanged(keys=["image"],
a_min=-175,
a_max=250,
b_min=0.0,
b_max=1.0,
clip=True),
CropForegroundd(keys=["image", "label"], source_key="image"),
ToTensord(keys=["image", "label"]),
])
datalist = load_decathlon_datalist(base_dir=data_dir,
data_list_file_path=json_path,
is_segmentation=True,
data_list_key="training")
val_files = load_decathlon_datalist(base_dir=data_dir,
data_list_file_path=json_path,
is_segmentation=True,
data_list_key="validation")
train_ds = CacheDataset(
data=datalist,
transform=train_transforms,
cache_num=24,
cache_rate=1.0,
num_workers=4,
)
train_loader = DataLoader(train_ds,
batch_size=1,
shuffle=True,
num_workers=4,
pin_memory=True)
val_ds = CacheDataset(data=val_files,
transform=val_transforms,
cache_num=6,
cache_rate=1.0,
num_workers=4)
val_loader = DataLoader(val_ds,
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True)
case_num = 0
img = val_ds[case_num]["image"]
label = val_ds[case_num]["label"]
img_shape = img.shape
label_shape = label.shape
print(f"image shape: {img_shape}, label shape: {label_shape}")
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNETR(
in_channels=1,
out_channels=14,
img_size=(96, 96, 96),
feature_size=16,
hidden_size=768,
mlp_dim=3072,
num_heads=12,
pos_embed="conv",
norm_name="instance",
res_block=True,
dropout_rate=0.0,
)
# Load ViT backbone weights into UNETR
if use_pretrained == 1:
print('Loading Weights from the Path {}'.format(pretrained_path))
vit_dict = torch.load(pretrained_path)
vit_weights = vit_dict['state_dict']
# Remove items of vit_weights if they are not in the ViT backbone (this is used in UNETR).
# For example, some variables names like conv3d_transpose.weight, conv3d_transpose.bias,
# conv3d_transpose_1.weight and conv3d_transpose_1.bias are used to match dimensions
# while pretraining with ViTAutoEnc and are not a part of ViT backbone.
model_dict = model.vit.state_dict()
vit_weights = {k: v for k, v in vit_weights.items() if k in model_dict}
model_dict.update(vit_weights)
model.vit.load_state_dict(model_dict)
del model_dict, vit_weights, vit_dict
print('Pretrained Weights Succesfully Loaded !')
elif use_pretrained == 0:
print(
'No weights were loaded, all weights being used are randomly initialized!'
)
model.to(device)
loss_function = DiceCELoss(to_onehot_y=True, softmax=True)
torch.backends.cudnn.benchmark = True
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-5)
post_label = AsDiscrete(to_onehot=14)
post_pred = AsDiscrete(argmax=True, to_onehot=14)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
global_step = 0
dice_val_best = 0.0
global_step_best = 0
epoch_loss_values = []
metric_values = []
def validation(epoch_iterator_val):
model.eval()
dice_vals = list()
with torch.no_grad():
for step, batch in enumerate(epoch_iterator_val):
val_inputs, val_labels = (batch["image"].cuda(),
batch["label"].cuda())
val_outputs = sliding_window_inference(val_inputs,
(96, 96, 96), 4, model)
val_labels_list = decollate_batch(val_labels)
val_labels_convert = [
post_label(val_label_tensor)
for val_label_tensor in val_labels_list
]
val_outputs_list = decollate_batch(val_outputs)
val_output_convert = [
post_pred(val_pred_tensor)
for val_pred_tensor in val_outputs_list
]
dice_metric(y_pred=val_output_convert, y=val_labels_convert)
dice = dice_metric.aggregate().item()
dice_vals.append(dice)
epoch_iterator_val.set_description(
"Validate (%d / %d Steps) (dice=%2.5f)" %
(global_step, 10.0, dice))
dice_metric.reset()
mean_dice_val = np.mean(dice_vals)
return mean_dice_val
def train(global_step, train_loader, dice_val_best, global_step_best):
model.train()
epoch_loss = 0
step = 0
epoch_iterator = tqdm(train_loader,
desc="Training (X / X Steps) (loss=X.X)",
dynamic_ncols=True)
for step, batch in enumerate(epoch_iterator):
step += 1
x, y = (batch["image"].cuda(), batch["label"].cuda())
logit_map = model(x)
loss = loss_function(logit_map, y)
loss.backward()
epoch_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
epoch_iterator.set_description(
"Training (%d / %d Steps) (loss=%2.5f)" %
(global_step, max_iterations, loss))
if (global_step % eval_num == 0
and global_step != 0) or global_step == max_iterations:
epoch_iterator_val = tqdm(
val_loader,
desc="Validate (X / X Steps) (dice=X.X)",
dynamic_ncols=True)
dice_val = validation(epoch_iterator_val)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
metric_values.append(dice_val)
if dice_val > dice_val_best:
dice_val_best = dice_val
global_step_best = global_step
torch.save(model.state_dict(),
os.path.join(logdir, "best_metric_model.pth"))
print(
"Model Was Saved ! Current Best Avg. Dice: {} Current Avg. Dice: {}"
.format(dice_val_best, dice_val))
else:
print(
"Model Was Not Saved ! Current Best Avg. Dice: {} Current Avg. Dice: {}"
.format(dice_val_best, dice_val))
plt.figure(1, (12, 6))
plt.subplot(1, 2, 1)
plt.title("Iteration Average Loss")
x = [eval_num * (i + 1) for i in range(len(epoch_loss_values))]
y = epoch_loss_values
plt.xlabel("Iteration")
plt.plot(x, y)
plt.grid()
plt.subplot(1, 2, 2)
plt.title("Val Mean Dice")
x = [eval_num * (i + 1) for i in range(len(metric_values))]
y = metric_values
plt.xlabel("Iteration")
plt.plot(x, y)
plt.grid()
plt.savefig(
os.path.join(logdir, 'btcv_finetune_quick_update.png'))
plt.clf()
plt.close(1)
global_step += 1
return global_step, dice_val_best, global_step_best
while global_step < max_iterations:
global_step, dice_val_best, global_step_best = train(
global_step, train_loader, dice_val_best, global_step_best)
model.load_state_dict(
torch.load(os.path.join(logdir, "best_metric_model.pth")))
print(f"train completed, best_metric: {dice_val_best:.4f} "
f"at iteration: {global_step_best}")
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
download_len = 1
val_frac = 1.0
collection = "QIN-PROSTATE-Repeatability"
transform = Compose([
LoadImaged(keys=["image", "seg"],
reader="PydicomReader",
label_dict=TCIA_LABEL_DICT[collection]),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), int(download_len * val_frac))
self.assertTrue("image" in dataset[0])
self.assertTrue("seg" in dataset[0])
self.assertTrue(isinstance(dataset[0]["image"], MetaTensor))
self.assertTupleEqual(dataset[0]["image"].shape, (1, 256, 256, 24))
self.assertTupleEqual(dataset[0]["seg"].shape, (256, 256, 24, 4))
with skip_if_downloading_fails():
data = TciaDataset(
root_dir=testing_dir,
collection=collection,
transform=transform,
section="validation",
download=True,
download_len=download_len,
copy_cache=False,
val_frac=val_frac,
)
_test_dataset(data)
data = TciaDataset(
root_dir=testing_dir,
collection=collection,
transform=transform,
section="validation",
download=False,
val_frac=val_frac,
)
_test_dataset(data)
self.assertTrue(data[0]["image"].meta["filename_or_obj"].endswith(
"QIN-PROSTATE-Repeatability/PCAMPMRI-00015/1901/image"))
self.assertTrue(data[0]["seg"].meta["filename_or_obj"].endswith(
"QIN-PROSTATE-Repeatability/PCAMPMRI-00015/1901/seg"))
# test validation without transforms
data = TciaDataset(root_dir=testing_dir,
collection=collection,
section="validation",
download=False,
val_frac=val_frac)
self.assertTupleEqual(data[0]["image"].shape, (256, 256, 24))
self.assertEqual(len(data), int(download_len * val_frac))
data = TciaDataset(root_dir=testing_dir,
collection=collection,
section="validation",
download=False,
val_frac=val_frac)
self.assertTupleEqual(data[0]["image"].shape, (256, 256, 24))
self.assertEqual(len(data), download_len)
shutil.rmtree(os.path.join(testing_dir, collection))
try:
TciaDataset(
root_dir=testing_dir,
collection=collection,
transform=transform,
section="validation",
download=False,
val_frac=val_frac,
)
except RuntimeError as e:
self.assertTrue(str(e).startswith("Cannot find dataset directory"))
opt = Options().parse()
train_images = sorted(
glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))
train_segs = sorted(
glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))
data_dicts = [{
'image': image_name,
'label': label_name,
'mask': mask_name
} for image_name, label_name, mask_name in zip(train_images, train_segs)]
monai_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=1, spatial_axis=2),
# RandAffined(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1,
# rotate_range=(np.pi / 36, np.pi / 4, np.pi / 36)),
# Rand3DElasticd(keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1,
# sigma_range=(5, 8), magnitude_range=(100, 200), scale_range=(0.20, 0.20, 0.20)),
# RandAdjustContrastd(keys=['image'], gamma=(0.5, 3), prob=1),
# RandGaussianNoised(keys=['image'], prob=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=1),
# BorderPadd(keys=['image', 'label'],spatial_border=(16,16,0)),
# RandSpatialCropd(keys=['image', 'label'], roi_size=opt.patch_size, random_size=False),
# Orientationd(keys=["image", "label"], axcodes="PLI"),
ToTensord(keys=['image', 'label'])
def test_shape(self, input_param, input_data, expected_shape):
result = AddChanneld(**input_param)(input_data)
self.assertEqual(result['img'].shape, expected_shape)
self.assertEqual(result['seg'].shape, expected_shape)
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([
LoadImaged(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" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.DenseNet121(spatial_dims=3,
in_channels=1,
out_channels=2).to(device)
model.load_state_dict(
torch.load("best_metric_model_classification3d_dict.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 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",
"IXI314-IOP-0889-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI249-Guys-1072-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI609-HH-2600-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI173-HH-1590-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI020-Guys-0700-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI342-Guys-0909-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI134-Guys-0780-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI577-HH-2661-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI066-Guys-0731-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI130-HH-1528-T1.nii.gz"
]),
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, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0],
dtype=np.int64)
train_files = [{
"img": img,
"label": label
} for img, label in zip(images[:10], labels[:10])]
val_files = [{
"img": img,
"label": label
} for img, label in zip(images[-10:], labels[-10:])]
# define transforms for image
train_transforms = Compose([
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]),
ToTensord(keys=["img"]),
])
val_transforms = Compose([
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
ToTensord(keys=["img"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["label"])
# create DenseNet121, CrossEntropyLoss and Adam optimizer
net = monai.networks.nets.densenet.densenet121(spatial_dims=3,
in_channels=1,
out_channels=2)
loss = torch.nn.CrossEntropyLoss()
lr = 1e-5
opt = torch.optim.Adam(net.parameters(), lr)
device = torch.device("cuda:0")
# Ignite trainer expects batch=(img, label) and returns output=loss at every iteration,
# user can add output_transform to return other values, like: y_pred, y, etc.
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch["img"], batch["label"]), device,
non_blocking)
trainer = create_supervised_trainer(net,
opt,
loss,
device,
False,
prepare_batch=prepare_batch)
# adding checkpoint handler to save models (network params and optimizer stats) during training
checkpoint_handler = ModelCheckpoint("./runs/",
"net",
n_saved=10,
require_empty=False)
trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=checkpoint_handler,
to_save={
"net": net,
"opt": opt
})
# StatsHandler prints loss at every iteration and print metrics at every epoch,
# we don't set metrics for trainer here, so just print loss, user can also customize print functions
# and can use output_transform to convert engine.state.output if it's not loss value
train_stats_handler = StatsHandler(name="trainer")
train_stats_handler.attach(trainer)
# TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler
train_tensorboard_stats_handler = TensorBoardStatsHandler()
train_tensorboard_stats_handler.attach(trainer)
# set parameters for validation
validation_every_n_epochs = 1
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {
metric_name: Accuracy(),
"AUC": ROCAUC(to_onehot_y=True, softmax=True)
}
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net,
val_metrics,
device,
True,
prepare_batch=prepare_batch)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_stats_handler.attach(evaluator)
# add handler to record metrics to TensorBoard at every epoch
val_tensorboard_stats_handler = TensorBoardStatsHandler(
output_transform=lambda x:
None, # no need to plot loss value, so disable per iteration output
global_epoch_transform=lambda x: trainer.state.epoch,
) # fetch global epoch number from trainer
val_tensorboard_stats_handler.attach(evaluator)
# add early stopping handler to evaluator
early_stopper = EarlyStopping(
patience=4,
score_function=stopping_fn_from_metric(metric_name),
trainer=trainer)
evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED,
handler=early_stopper)
# create a validation data loader
val_ds = 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())
@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
def run_validation(engine):
evaluator.run(val_loader)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
pin_memory=torch.cuda.is_available())
train_epochs = 30
state = trainer.run(train_loader, train_epochs)
print(state)
Rand2DElasticd,
RandAffined,
)
aug_prob = 0.5
keys = ("img", "seg")
# use these when interpolating binary segmentations to ensure values are 0 or 1 only
zoom_mode = monai.utils.enums.InterpolateMode.NEAREST
elast_mode = monai.utils.enums.GridSampleMode.BILINEAR, monai.utils.enums.GridSampleMode.NEAREST
trans = Compose(
[
ScaleIntensityd(keys=("img",)), # rescale image data to range [0,1]
AddChanneld(keys=keys), # add 1-size channel dimension
RandRotate90d(keys=keys, prob=aug_prob),
RandFlipd(keys=keys, prob=aug_prob),
RandZoomd(keys=keys, prob=aug_prob, mode=zoom_mode),
Rand2DElasticd(keys=keys, prob=aug_prob, spacing=10, magnitude_range=(-2, 2), mode=elast_mode),
RandAffined(keys=keys, prob=aug_prob, rotate_range=1, translate_range=16, mode=elast_mode),
ToTensord(keys=keys), # convert to tensor
]
)
data = [
{"img": train_images[i], "seg": train_segs[i]} for i in range(len(train_images))
]
ds = CacheDataset(data, trans)
def main(config):
now = datetime.now().strftime("%Y%m%d-%H:%M:%S")
# path
csv_path = config['path']['csv_path']
trained_model_path = config['path'][
'trained_model_path'] # if None, trained from scratch
training_model_folder = os.path.join(
config['path']['training_model_folder'], now) # '/path/to/folder'
if not os.path.exists(training_model_folder):
os.makedirs(training_model_folder)
logdir = os.path.join(training_model_folder, 'logs')
if not os.path.exists(logdir):
os.makedirs(logdir)
# PET CT scan params
image_shape = tuple(config['preprocessing']['image_shape']) # (x, y, z)
in_channels = config['preprocessing']['in_channels']
voxel_spacing = tuple(
config['preprocessing']
['voxel_spacing']) # (4.8, 4.8, 4.8) # in millimeter, (x, y, z)
data_augment = config['preprocessing'][
'data_augment'] # True # for training dataset only
resize = config['preprocessing']['resize'] # True # not use yet
origin = config['preprocessing']['origin'] # how to set the new origin
normalize = config['preprocessing'][
'normalize'] # True # whether or not to normalize the inputs
number_class = config['preprocessing']['number_class'] # 2
# CNN params
architecture = config['model']['architecture'] # 'unet' or 'vnet'
cnn_params = config['model'][architecture]['cnn_params']
# transform list to tuple
for key, value in cnn_params.items():
if isinstance(value, list):
cnn_params[key] = tuple(value)
# Training params
epochs = config['training']['epochs']
batch_size = config['training']['batch_size']
shuffle = config['training']['shuffle']
opt_params = config['training']["optimizer"]["opt_params"]
# Get Data
DM = DataManager(csv_path=csv_path)
train_images_paths, val_images_paths, test_images_paths = DM.get_train_val_test(
wrap_with_dict=True)
# Input preprocessing
# use data augmentation for training
train_transforms = Compose([ # read img + meta info
LoadNifti(keys=["pet_img", "ct_img", "mask_img"]),
Roi2Mask(keys=['pet_img', 'mask_img'],
method='otsu',
tval=0.0,
idx_channel=0),
ResampleReshapeAlign(target_shape=image_shape,
target_voxel_spacing=voxel_spacing,
keys=['pet_img', "ct_img", 'mask_img'],
origin='head',
origin_key='pet_img'),
Sitk2Numpy(keys=['pet_img', 'ct_img', 'mask_img']),
# user can also add other random transforms
RandAffined(keys=("pet_img", "ct_img", "mask_img"),
spatial_size=None,
prob=0.4,
rotate_range=(0, np.pi / 30, np.pi / 15),
shear_range=None,
translate_range=(10, 10, 10),
scale_range=(0.1, 0.1, 0.1),
mode=("bilinear", "bilinear", "nearest"),
padding_mode="border"),
# normalize input
ScaleIntensityRanged(
keys=["pet_img"],
a_min=0.0,
a_max=25.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
ScaleIntensityRanged(
keys=["ct_img"],
a_min=-1000.0,
a_max=1000.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
# Prepare for neural network
ConcatModality(keys=['pet_img', 'ct_img']),
AddChanneld(keys=["mask_img"]), # Add channel to the first axis
ToTensord(keys=["image", "mask_img"]),
])
# without data augmentation for validation
val_transforms = Compose([ # read img + meta info
LoadNifti(keys=["pet_img", "ct_img", "mask_img"]),
Roi2Mask(keys=['pet_img', 'mask_img'],
method='otsu',
tval=0.0,
idx_channel=0),
ResampleReshapeAlign(target_shape=image_shape,
target_voxel_spacing=voxel_spacing,
keys=['pet_img', "ct_img", 'mask_img'],
origin='head',
origin_key='pet_img'),
Sitk2Numpy(keys=['pet_img', 'ct_img', 'mask_img']),
# normalize input
ScaleIntensityRanged(
keys=["pet_img"],
a_min=0.0,
a_max=25.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
ScaleIntensityRanged(
keys=["ct_img"],
a_min=-1000.0,
a_max=1000.0,
b_min=0.0,
b_max=1.0,
clip=True,
),
# Prepare for neural network
ConcatModality(keys=['pet_img', 'ct_img']),
AddChanneld(keys=["mask_img"]), # Add channel to the first axis
ToTensord(keys=["image", "mask_img"]),
])
# create a training data loader
train_ds = monai.data.CacheDataset(data=train_images_paths,
transform=train_transforms,
cache_rate=0.5)
# use batch_size=2 to load images to generate 2 x 4 images for network training
train_loader = monai.data.DataLoader(train_ds,
batch_size=batch_size,
shuffle=shuffle,
num_workers=2)
# create a validation data loader
val_ds = monai.data.CacheDataset(data=val_images_paths,
transform=val_transforms,
cache_rate=1.0)
val_loader = monai.data.DataLoader(val_ds,
batch_size=batch_size,
num_workers=2)
# Model
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = UNet(
dimensions=3, # 3D
in_channels=in_channels,
out_channels=1,
kernel_size=5,
channels=(8, 16, 32, 64, 128),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss = monai.losses.DiceLoss(sigmoid=True, squared_pred=True)
opt = torch.optim.Adam(net.parameters(), 1e-3)
# training
val_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
])
val_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir="./runs/",
output_transform=lambda x: None),
# TensorBoardImageHandler(
# log_dir="./runs/",
# batch_transform=lambda x: (x["image"], x["label"]),
# output_transform=lambda x: x["pred"],
# ),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": opt
},
save_key_metric=True),
]
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=val_loader,
network=net,
inferer=SimpleInferer(),
post_transform=val_post_transforms,
key_val_metric={
"val_mean_dice":
MeanDice(include_background=True,
output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"val_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"])),
"val_precision":
Precision(output_transform=lambda x: (x["pred"], x["label"])),
"val_recall":
Recall(output_transform=lambda x: (x["pred"], x["label"]))
},
val_handlers=val_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation
# amp=True if monai.config.get_torch_version_tuple() >= (1, 6) else False,
)
train_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
AsDiscreted(keys="pred", threshold_values=True),
])
train_handlers = [
# LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(log_dir="./runs/",
tag_name="train_loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir="./runs/",
save_dict={
"net": net,
"opt": opt
},
save_interval=2,
epoch_level=True),
]
trainer = SupervisedTrainer(
device=device,
max_epochs=5,
train_data_loader=train_loader,
network=net,
optimizer=opt,
loss_function=loss,
prepare_batch=lambda x: (x['image'], x['mask_img']),
inferer=SimpleInferer(),
post_transform=train_post_transforms,
key_train_metric={
"train_mean_dice":
MeanDice(include_background=True,
output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"train_acc":
Accuracy(output_transform=lambda x: (x["pred"], x["label"])),
"train_precision":
Precision(output_transform=lambda x: (x["pred"], x["label"])),
"train_recall":
Recall(output_transform=lambda x: (x["pred"], x["label"]))
},
train_handlers=train_handlers,
# if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training
amp=True if monai.config.get_torch_version_tuple() >=
(1, 6) else False,
)
trainer.run()
def evaluta_model(test_files, model_name):
test_transforms = Compose(
[
LoadNiftid(keys=modalDataKey),
AddChanneld(keys=modalDataKey),
NormalizeIntensityd(keys=modalDataKey),
# ScaleIntensityd(keys=modalDataKey),
# Resized(keys=modalDataKey, spatial_size=(48, 48), mode='bilinear'),
ResizeWithPadOrCropd(keys=modalDataKey, spatial_size=(64, 64)),
ConcatItemsd(keys=modalDataKey, name="inputs"),
ToTensord(keys=["inputs"]),
]
)
# create a validation data loader
device = torch.device("cpu")
print(len(test_files))
test_ds = monai.data.Dataset(data=test_files, transform=test_transforms)
test_loader = DataLoader(test_ds, batch_size=len(test_files), num_workers=2, pin_memory=torch.device)
# model = monai.networks.nets.se_resnet101(spatial_dims=2, in_ch=3, num_classes=6).to(device)
model = DenseNetASPP(spatial_dims=2, in_channels=2, out_channels=5).to(device)
# Evaluate the model on test dataset #
# print(os.path.basename(model_name).split('.')[0])
checkpoint = torch.load(model_name)
model.load_state_dict(checkpoint['model'])
# optimizer.load_state_dict(checkpoint['optimizer'])
# epochs = checkpoint['epoch']
# model.load_state_dict(torch.load(log_dir))
model.eval()
with torch.no_grad():
saver = CSVSaver(output_dir="../result/GLeason/2d_output/",
filename=os.path.basename(model_name).split('.')[0] + '.csv')
for test_data in test_loader:
test_images, test_labels = test_data["inputs"].to(device), test_data["label"].to(device)
pred = model(test_images) # Gleason Classification
# y_soft_label = (test_labels / 0.25).long()
# y_soft_pred = (pred / 0.25).round().squeeze_().long()
# print(test_data)
probabilities = torch.sigmoid(pred)
# pred2 = model(test_images).argmax(dim=1)
# print(test_data)
# saver.save_batch(probabilities.argmax(dim=1), test_data["t2Img_meta_dict"])
# zero = torch.zeros_like(probabilities)
# one = torch.ones_like(probabilities)
# y_pred_ordinal = torch.where(probabilities > 0.5, one, zero)
# y_pred_acc = (y_pred_ordinal.sum(1)).to(torch.long)
saver.save_batch(probabilities.argmax(dim=1), test_data["dwiImg_meta_dict"])
# print(test_labels)
# print(probabilities[:, 1])
# for x in np.nditer(probabilities[:, 1]):
# print(x)
# prob_list.append(x)
# falseList = []
# trueList = []
# for pre, label in zip(pred2.tolist(), test_labels.tolist() ):
# if pre == 0 and label == 0:
# falseList.append(0)
# elif pre == 1 and label == 1:
# trueList.append(1)
# specificity = (falseList.count(0) / test_labels.tolist().count(0))
# sensitivity = (trueList.count(1) / test_labels.tolist().count(1))
# print('specificity:' + '%.4f' % specificity + ',',
# 'sensitivity:' + '%.4f' % sensitivity + ',',
# 'accuracy:' + '%.4f' % ((specificity + sensitivity) / 2))
# print(type(test_labels), type(pred))
# fpr, tpr, thresholds = roc_curve(test_labels, probabilities[:, 1])
# roc_auc = auc(fpr, tpr)
# print('AUC = ' + str(roc_auc))
# AUC_list.append(roc_auc)
# # print(accuracy_score(test_labels, pred2))
# accuracy_list.append(accuracy_score(test_labels, pred2))
# plt.plot(fpr, tpr, linewidth=2, label="ROC")
# plt.xlabel("false presitive rate")
# plt.ylabel("true presitive rate")
# # plt.ylim(0, 1.05)
# # plt.xlim(0, 1.05)
# plt.legend(loc=4) # 图例的位置
# plt.show()
saver.finalize()
# cm = confusion_matrix(test_labels, y_pred_acc)
cm = confusion_matrix(test_labels, probabilities.argmax(dim=1))
# cm = confusion_matrix(y_soft_label, y_soft_pred)
# kappa_value = cohen_kappa_score(test_labels, y_pred_acc, weights='quadratic')
kappa_value = cohen_kappa_score(test_labels, probabilities.argmax(dim=1), weights='quadratic')
print('quadratic weighted kappa=' + str(kappa_value))
kappa_list.append(kappa_value)
plot_confusion_matrix(cm, 'confusion_matrix.png', title='confusion matrix')
from sklearn.metrics import classification_report
print(classification_report(test_labels, probabilities.argmax(dim=1), digits=4))
accuracy_list.append(
classification_report(test_labels, probabilities.argmax(dim=1), digits=4, output_dict=True)["accuracy"])
def training(train_files, val_files, log_dir):
# Define transforms for image
print(log_dir)
train_transforms = Compose(
[
LoadNiftid(keys=modalDataKey),
AddChanneld(keys=modalDataKey),
NormalizeIntensityd(keys=modalDataKey),
# ScaleIntensityd(keys=modalDataKey),
ResizeWithPadOrCropd(keys=modalDataKey, spatial_size=(64, 64)),
# Resized(keys=modalDataKey, spatial_size=(48, 48), mode='bilinear'),
ConcatItemsd(keys=modalDataKey, name="inputs"),
RandRotate90d(keys=["inputs"], prob=0.8, spatial_axes=[0, 1]),
RandAffined(keys=["inputs"], prob=0.8, scale_range=[0.1, 0.5]),
RandZoomd(keys=["inputs"], prob=0.8, max_zoom=1.5, min_zoom=0.5),
# RandFlipd(keys=["inputs"], prob=0.5, spatial_axis=1),
ToTensord(keys=["inputs"]),
]
)
val_transforms = Compose(
[
LoadNiftid(keys=modalDataKey),
AddChanneld(keys=modalDataKey),
NormalizeIntensityd(keys=modalDataKey),
# ScaleIntensityd(keys=modalDataKey),
ResizeWithPadOrCropd(keys=modalDataKey, spatial_size=(64, 64)),
# Resized(keys=modalDataKey, spatial_size=(48, 48), mode='bilinear'),
ConcatItemsd(keys=modalDataKey, name="inputs"),
ToTensord(keys=["inputs"]),
]
)
# data_size = len(full_files)
# split = data_size // 2
# indices = list(range(data_size))
# train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:])
# valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
# full_loader = DataLoader(full_files, batch_size=64, sampler=sampler(full_files), pin_memory=True)
# train_loader = DataLoader(full_files, batch_size=128, sampler=train_sampler, collate_fn=collate_fn)
# val_loader = DataLoader(full_files, batch_size=split, sampler=valid_sampler, collate_fn=collate_fn)
# DL = DataLoader(train_files, batch_size=64, shuffle=True, num_workers=0, drop_last=True, collate_fn=collate_fn)
# randomBatch_sizeList = [8, 16, 32, 64, 128]
# randomLRList = [1e-4, 1e-5, 5e-5, 5e-4, 1e-3]
# batch_size = random.choice(randomBatch_sizeList)
# lr = random.choice(randomLRList)
lr = 0.01
batch_size = 256
# print(batch_size)
# print(lr)
# Define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds, batch_size=batch_size, num_workers=2, pin_memory=torch.device)
check_data = monai.utils.misc.first(check_loader)
# print(check_data)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=torch.device)
# train_data = monai.utils.misc.first(train_loader)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=batch_size, num_workers=2, pin_memory=torch.device)
# Create Net, CrossEntropyLoss and Adam optimizer
# model = monai.networks.nets.se_resnet101(spatial_dims=2, in_ch=3, num_classes=6).to(device)
# model = densenet121(spatial_dims=2, in_channels=3, out_channels=5).to(device)
# im_size = (2,) + tuple(train_ds[0]["inputs"].shape)
model = DenseNetASPP(spatial_dims=2, in_channels=2, out_channels=5).to(device)
classes = np.array([0, 1, 2, 3, 4])
# print(check_data["label"].numpy())
class_weights = class_weight.compute_class_weight('balanced', classes, check_data["label"].numpy())
class_weights_tensor = torch.Tensor(class_weights).to(device)
# print(class_weights_tensor)
# loss_function = nn.BCEWithLogitsLoss()
loss_function = torch.nn.CrossEntropyLoss(weight=class_weights_tensor)
# loss_function = torch.nn.MSELoss()
# m = torch.nn.LogSoftmax(dim=1)
optimizer = torch.optim.Adam(model.parameters(), lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 50, gamma=0.5, last_epoch=-1)
# 如果有保存的模型，则加载模型，并在其基础上继续训练
if os.path.exists(log_dir):
checkpoint = torch.load(log_dir)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
print('加载 epoch {} 成功！'.format(start_epoch))
else:
start_epoch = 0
print('无保存模型，将从头开始训练！')
# start a typical PyTorch training
epoch_num = 300
val_interval = 2
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
# checkpoint_interval = 100
for epoch in range(start_epoch + 1, epoch_num):
print("-" * 10)
print(f"epoch {epoch + 1}/{epoch_num}")
# print(scheduler.get_last_lr())
model.train()
epoch_loss = 0
step = 0
# for i, (inputs, labels, imgName) in enumerate(train_loader):
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["inputs"].to(device), batch_data["label"].to(device)
# batch_arr = []
# for j in range(len(inputs)):
# batch_arr.append(inputs[i])
# batch_img = Variable(torch.from_numpy(np.array(batch_arr)).to(device))
# labels = Variable(torch.from_numpy(np.array(labels)).to(device))
# batch_img = batch_img.type(torch.FloatTensor).to(device)
outputs = model(inputs)
# y_ordinal_encoding = transformOrdinalEncoding(labels, labels.shape[0], 5)
# loss = loss_function(outputs, torch.from_numpy(y_ordinal_encoding).to(device))
loss = loss_function(outputs, labels.long())
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
print(f"{step}/{len(train_loader)}, train_loss: {loss.item():.4f}")
epoch_len = len(train_loader) // train_loader.batch_size
writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
epoch_loss /= step
scheduler.step()
print(epoch, 'lr={:.6f}'.format(scheduler.get_last_lr()[0]))
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
# if (epoch + 1) % checkpoint_interval == 0: # 每隔checkpoint_interval保存一次
# checkpoint = {'model': model.state_dict(),
# 'optimizer': optimizer.state_dict(),
# 'epoch': epoch
# }
# path_checkpoint = './model/checkpoint_{}_epoch.pth'.format(epoch)
# torch.save(checkpoint, path_checkpoint)
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 i, (inputs, labels, imgName) in enumerate(val_loader):
for val_data in val_loader:
val_images, val_labels = val_data["inputs"].to(device), val_data["label"].to(device)
# val_batch_arr = []
# for j in range(len(inputs)):
# val_batch_arr.append(inputs[i])
# val_img = Variable(torch.from_numpy(np.array(val_batch_arr)).to(device))
# labels = Variable(torch.from_numpy(np.array(labels)).to(device))
# val_img = val_img.type(torch.FloatTensor).to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
# y_ordinal_encoding = transformOrdinalEncoding(y, y.shape[0], 5)
# y_pred = torch.sigmoid(y_pred)
# y = (y / 0.25).long()
# print(y)
# auc_metric = compute_roc_auc(y_pred, y, to_onehot_y=True, softmax=True)
# zero = torch.zeros_like(y_pred)
# one = torch.ones_like(y_pred)
# y_pred_label = torch.where(y_pred > 0.5, one, zero)
# print((y_pred_label.sum(1)).to(torch.long))
# y_pred_acc = (y_pred_label.sum(1)).to(torch.long)
# print(y_pred.argmax(dim=1))
# kappa_value = kappa(cm)
kappa_value = cohen_kappa_score(y.to("cpu"), y_pred.argmax(dim=1).to("cpu"), weights='quadratic')
# kappa_value = cohen_kappa_score(y.to("cpu"), y_pred_acc.to("cpu"), weights='quadratic')
metric_values.append(kappa_value)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
# print(acc_value)
acc_metric = acc_value.sum().item() / len(acc_value)
if kappa_value > best_metric:
best_metric = kappa_value
best_metric_epoch = epoch + 1
checkpoint = {'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}
torch.save(checkpoint, log_dir)
print("saved new best metric model")
print(
"current epoch: {} current Kappa: {:.4f} current accuracy: {:.4f} best Kappa: {:.4f} at epoch {}".format(
epoch + 1, kappa_value, acc_metric, best_metric, best_metric_epoch
)
)
writer.add_scalar("val_accuracy", acc_metric, epoch + 1)
print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
writer.close()
plt.figure('train', (12, 6))
plt.subplot(1, 2, 1)
plt.title("Epoch Average Loss")
x = [i + 1 for i in range(len(epoch_loss_values))]
y = epoch_loss_values
plt.xlabel('epoch')
plt.plot(x, y)
plt.subplot(1, 2, 2)
plt.title("Validation: Area under the ROC curve")
x = [val_interval * (i + 1) for i in range(len(metric_values))]
y = metric_values
plt.xlabel('epoch')
plt.plot(x, y)
plt.show()
evaluta_model(val_files, log_dir)
def image_mixing(data, seed=None):
#random.seed(seed)
file_list = [x for x in data if int(x['_label']) == 1]
random.shuffle(file_list)
crop_foreground = CropForegroundd(keys=["image"],
source_key="image",
margin=(0, 0, 0),
select_fn=lambda x: x != 0)
WW, WL = 1500, -600
ct_window = CTWindowd(keys=["image"], width=WW, level=WL)
resize2 = Resized(keys=["image"],
spatial_size=(int(512 * 0.75), int(512 * 0.75), -1),
mode="area")
resize1 = Resized(keys=["image"],
spatial_size=(-1, -1, 40),
mode="nearest")
gauss = GaussianSmooth(sigma=(1., 1., 0))
gauss2 = GaussianSmooth(sigma=(2.0, 2.0, 0))
affine = Affined(keys=["image"],
scale_params=(1.0, 2.0, 1.0),
padding_mode='zeros')
common_transform = Compose([
LoadImaged(keys=["image"]),
ct_window,
CTSegmentation(keys=["image"]),
AddChanneld(keys=["image"]),
affine,
crop_foreground,
resize1,
resize2,
SqueezeDimd(keys=["image"]),
])
dirs = setup_directories()
data_dir = dirs['data']
mixed_images_dir = os.path.join(data_dir, 'mixed_images')
if not os.path.exists(mixed_images_dir):
os.mkdir(mixed_images_dir)
for img1, img2 in itertools.combinations(file_list, 2):
img1 = {'image': img1["image"], 'seg': img1['seg']}
img2 = {'image': img2["image"], 'seg': img2['seg']}
img1_data = common_transform(img1)["image"]
img2_data = common_transform(img2)["image"]
img1_mask, img2_mask = (img1_data > 0), (img2_data > 0)
img_presek = np.logical_and(img1_mask, img2_mask)
img = np.maximum(img_presek * img1_data, img_presek * img2_data)
multi_slice_viewer(img, "img1")
loop = True
while loop:
save = input("Save image [y/n/e]: ")
if save.lower() == 'y':
loop = False
k = str(time.time()).encode('utf-8')
h = blake2b(key=k, digest_size=16)
name = h.hexdigest() + '.nii.gz'
out_path = os.path.join(mixed_images_dir, name)
write_nifti(img, out_path, resample=False)
elif save.lower() == 'n':
loop = False
break
elif save.lower() == 'e':
print("exeting")
exit()
else:
print("wrong input!")
def test_invert(self):
set_determinism(seed=0)
im_fname, seg_fname = [
make_nifti_image(i)
for i in create_test_image_3d(101, 100, 107, noise_max=100)
]
transform = Compose([
LoadImaged(KEYS),
AddChanneld(KEYS),
Orientationd(KEYS, "RPS"),
Spacingd(KEYS,
pixdim=(1.2, 1.01, 0.9),
mode=["bilinear", "nearest"],
dtype=np.float32),
ScaleIntensityd("image", minv=1, maxv=10),
RandFlipd(KEYS, prob=0.5, spatial_axis=[1, 2]),
RandAxisFlipd(KEYS, prob=0.5),
RandRotate90d(KEYS, spatial_axes=(1, 2)),
RandZoomd(KEYS,
prob=0.5,
min_zoom=0.5,
max_zoom=1.1,
keep_size=True),
RandRotated(KEYS,
prob=0.5,
range_x=np.pi,
mode="bilinear",
align_corners=True),
RandAffined(KEYS, prob=0.5, rotate_range=np.pi, mode="nearest"),
ResizeWithPadOrCropd(KEYS, 100),
ToTensord(
"image"
), # test to support both Tensor and Numpy array when inverting
CastToTyped(KEYS, dtype=[torch.uint8, np.uint8]),
])
data = [{"image": im_fname, "label": seg_fname} for _ in range(12)]
# num workers = 0 for mac or gpu transforms
num_workers = 0 if sys.platform == "darwin" or torch.cuda.is_available(
) else 2
dataset = CacheDataset(data, transform=transform, progress=False)
loader = DataLoader(dataset, num_workers=num_workers, batch_size=5)
# set up engine
def _train_func(engine, batch):
self.assertTupleEqual(batch["image"].shape[1:], (1, 100, 100, 100))
engine.state.output = batch
engine.fire_event(IterationEvents.MODEL_COMPLETED)
return engine.state.output
engine = Engine(_train_func)
engine.register_events(*IterationEvents)
# set up testing handler
TransformInverter(
transform=transform,
loader=loader,
output_keys=["image", "label"],
batch_keys="label",
nearest_interp=True,
postfix="inverted1",
to_tensor=[True, False],
device="cpu",
num_workers=0
if sys.platform == "darwin" or torch.cuda.is_available() else 2,
).attach(engine)
# test different nearest interpolation values
TransformInverter(
transform=transform,
loader=loader,
output_keys=["image", "label"],
batch_keys="image",
nearest_interp=[True, False],
post_func=[lambda x: x + 10, lambda x: x],
postfix="inverted2",
collate_fn=pad_list_data_collate,
num_workers=0
if sys.platform == "darwin" or torch.cuda.is_available() else 2,
).attach(engine)
engine.run(loader, max_epochs=1)
set_determinism(seed=None)
self.assertTupleEqual(engine.state.output["image"].shape,
(2, 1, 100, 100, 100))
self.assertTupleEqual(engine.state.output["label"].shape,
(2, 1, 100, 100, 100))
# check the nearest inerpolation mode
for i in engine.state.output["image_inverted1"]:
torch.testing.assert_allclose(
i.to(torch.uint8).to(torch.float), i.to(torch.float))
self.assertTupleEqual(i.shape, (1, 100, 101, 107))
for i in engine.state.output["label_inverted1"]:
np.testing.assert_allclose(
i.astype(np.uint8).astype(np.float32), i.astype(np.float32))
self.assertTupleEqual(i.shape, (1, 100, 101, 107))
# check labels match
reverted = engine.state.output["label_inverted1"][-1].astype(np.int32)
original = LoadImaged(KEYS)(data[-1])["label"]
n_good = np.sum(np.isclose(reverted, original, atol=1e-3))
reverted_name = engine.state.output["label_meta_dict"][
"filename_or_obj"][-1]
original_name = data[-1]["label"]
self.assertEqual(reverted_name, original_name)
print("invert diff", reverted.size - n_good)
# 25300: 2 workers (cpu, non-macos)
# 1812: 0 workers (gpu or macos)
# 1824: torch 1.5.1
self.assertTrue((reverted.size - n_good) in (25300, 1812, 1824),
"diff. in 3 possible values")
# check the case that different items use different interpolation mode to invert transforms
d = engine.state.output["image_inverted2"]
# if the interpolation mode is nearest, accumulated diff should be smaller than 1
self.assertLess(
torch.sum(d.to(torch.float) -
d.to(torch.uint8).to(torch.float)).item(), 1.0)
self.assertTupleEqual(d.shape, (2, 1, 100, 101, 107))
d = engine.state.output["label_inverted2"]
# if the interpolation mode is not nearest, accumulated diff should be greater than 10000
self.assertGreater(
torch.sum(d.to(torch.float) -
d.to(torch.uint8).to(torch.float)).item(), 10000.0)
self.assertTupleEqual(d.shape, (2, 1, 100, 101, 107))
def load_image(filename):
data = {"image": filename}
t = Compose([LoadImaged(keys="image"), AddChanneld(keys="image")])
return t(data)
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
transform = Compose(
[
LoadImaged(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(PostFix.meta("image") in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 36, 47, 44))
with skip_if_downloading_fails():
data = DecathlonDataset(
root_dir=testing_dir,
task="Task04_Hippocampus",
transform=transform,
section="validation",
download=True,
copy_cache=False,
)
_test_dataset(data)
data = DecathlonDataset(
root_dir=testing_dir, task="Task04_Hippocampus", transform=transform, section="validation", download=False
)
_test_dataset(data)
self.assertTrue(data[0][PostFix.meta("image")]["filename_or_obj"].endswith("hippocampus_163.nii.gz"))
self.assertTrue(data[0][PostFix.meta("label")]["filename_or_obj"].endswith("hippocampus_163.nii.gz"))
# test validation without transforms
data = DecathlonDataset(root_dir=testing_dir, task="Task04_Hippocampus", section="validation", download=False)
self.assertTupleEqual(data[0]["image"].shape, (36, 47, 44))
self.assertEqual(len(data), 52)
data = DecathlonDataset(root_dir=testing_dir, task="Task04_Hippocampus", section="training", download=False)
self.assertTupleEqual(data[0]["image"].shape, (34, 56, 31))
self.assertEqual(len(data), 208)
# test dataset properties
data = DecathlonDataset(
root_dir=Path(testing_dir), task="Task04_Hippocampus", section="validation", download=False
)
properties = data.get_properties(keys="labels")
self.assertDictEqual(properties["labels"], {"0": "background", "1": "Anterior", "2": "Posterior"})
shutil.rmtree(os.path.join(testing_dir, "Task04_Hippocampus"))
try:
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 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, reduction="mean")
post_trans = Compose(
[Activations(sigmoid=True),
AsDiscrete(threshold_values=True)])
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)
val_outputs = post_trans(val_outputs)
value, _ = dice_metric(y_pred=val_outputs, y=val_labels)
metric_count += len(value)
metric_sum += value.item() * len(value)
saver.save_batch(val_outputs)
metric = metric_sum / metric_count
print("evaluation metric:", metric)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# create a temporary directory and 40 random image, mask pairs
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(40):
im, seg = create_test_image_2d(128, 128, num_seg_classes=1)
Image.fromarray((im * 255).astype("uint8")).save(
os.path.join(tempdir, f"img{i:d}.png"))
Image.fromarray((seg * 255).astype("uint8")).save(
os.path.join(tempdir, f"seg{i:d}.png"))
images = sorted(glob(os.path.join(tempdir, "img*.png")))
segs = sorted(glob(os.path.join(tempdir, "seg*.png")))
train_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[:20], segs[:20])]
val_files = [{
"img": img,
"seg": seg
} for img, seg in zip(images[-20:], segs[-20:])]
# define transforms for image and segmentation
train_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AddChanneld(keys=["img", "seg"]),
ScaleIntensityd(keys=["img", "seg"]),
RandCropByPosNegLabeld(keys=["img", "seg"],
label_key="seg",
spatial_size=[96, 96],
pos=1,
neg=1,
num_samples=4),
RandRotate90d(keys=["img", "seg"], prob=0.5, spatial_axes=[0, 1]),
EnsureTyped(keys=["img", "seg"]),
])
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AddChanneld(keys=["img", "seg"]),
ScaleIntensityd(keys=["img", "seg"]),
EnsureTyped(keys=["img", "seg"]),
])
# define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
collate_fn=list_data_collate)
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["seg"].shape)
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
train_loader = DataLoader(
train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available(),
)
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
# create UNet, DiceLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.UNet(
spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
loss_function = monai.losses.DiceLoss(sigmoid=True)
optimizer = torch.optim.Adam(model.parameters(), 1e-3)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
for epoch in range(10):
print("-" * 10)
print(f"epoch {epoch + 1}/{10}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["img"].to(
device), batch_data["seg"].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
val_images = None
val_labels = None
val_outputs = None
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
roi_size = (96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, model)
val_outputs = [
post_trans(i) for i in decollate_batch(val_outputs)
]
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
# aggregate the final mean dice result
metric = dice_metric.aggregate().item()
# reset the status for next validation round
dice_metric.reset()
metric_values.append(metric)
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(),
"best_metric_model_segmentation2d_dict.pth")
print("saved new best metric model")
print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}"
.format(epoch + 1, metric, best_metric, best_metric_epoch))
writer.add_scalar("val_mean_dice", metric, epoch + 1)
# plot the last model output as GIF image in TensorBoard with the corresponding image and label
plot_2d_or_3d_image(val_images,
epoch + 1,
writer,
index=0,
tag="image")
plot_2d_or_3d_image(val_labels,
epoch + 1,
writer,
index=0,
tag="label")
plot_2d_or_3d_image(val_outputs,
epoch + 1,
writer,
index=0,
tag="output")
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
def main():
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 DenseNet121
net = monai.networks.nets.densenet.densenet121(spatial_dims=3,
in_channels=1,
out_channels=2)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch["img"], batch["label"]), device,
non_blocking)
metric_name = "Accuracy"
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}
# Ignite evaluator expects batch=(img, label) and returns output=(y_pred, y) at every iteration,
# user can add output_transform to return other values
evaluator = create_supervised_evaluator(net,
val_metrics,
device,
True,
prepare_batch=prepare_batch)
# add stats event handler to print validation stats via evaluator
val_stats_handler = StatsHandler(
name="evaluator",
output_transform=lambda x:
None, # no need to print loss value, so disable per iteration output
)
val_stats_handler.attach(evaluator)
# for the array data format, assume the 3rd item of batch data is the meta_data
prediction_saver = ClassificationSaver(
output_dir="tempdir",
name="evaluator",
batch_transform=lambda batch: batch["img_meta_dict"],
output_transform=lambda output: output[0].argmax(1),
)
prediction_saver.attach(evaluator)
# the model was trained by "densenet_training_dict" example
CheckpointLoader(load_path="./runs_dict/net_checkpoint_20.pth",
load_dict={
"net": net
}).attach(evaluator)
# 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())
state = evaluator.run(val_loader)
print(state)
def main():
opt = Options().parse()
# monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
if opt.gpu_ids != '-1':
num_gpus = len(opt.gpu_ids.split(','))
else:
num_gpus = 0
print('number of GPU:', num_gpus)
# Data loader creation
# train images
train_images = sorted(
glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))
train_segs = sorted(
glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))
train_images_for_dice = sorted(
glob(os.path.join(opt.images_folder, 'train', 'image*.nii')))
train_segs_for_dice = sorted(
glob(os.path.join(opt.labels_folder, 'train', 'label*.nii')))
# validation images
val_images = sorted(
glob(os.path.join(opt.images_folder, 'val', 'image*.nii')))
val_segs = sorted(
glob(os.path.join(opt.labels_folder, 'val', 'label*.nii')))
# test images
test_images = sorted(
glob(os.path.join(opt.images_folder, 'test', 'image*.nii')))
test_segs = sorted(
glob(os.path.join(opt.labels_folder, 'test', 'label*.nii')))
# augment the data list for training
for i in range(int(opt.increase_factor_data)):
train_images.extend(train_images)
train_segs.extend(train_segs)
print('Number of training patches per epoch:', len(train_images))
print('Number of training images per epoch:', len(train_images_for_dice))
print('Number of validation images per epoch:', len(val_images))
print('Number of test images per epoch:', len(test_images))
# Creation of data directories for data_loader
train_dicts = [{
'image': image_name,
'label': label_name
} for image_name, label_name in zip(train_images, train_segs)]
train_dice_dicts = [{
'image': image_name,
'label': label_name
}
for image_name, label_name in zip(
train_images_for_dice, train_segs_for_dice)]
val_dicts = [{
'image': image_name,
'label': label_name
} for image_name, label_name in zip(val_images, val_segs)]
test_dicts = [{
'image': image_name,
'label': label_name
} for image_name, label_name in zip(test_images, test_segs)]
# Transforms list
if opt.resolution is not None:
train_transforms = [
LoadNiftid(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ScaleIntensityRanged(
keys=["image"],
a_min=-120,
a_max=170,
b_min=0.0,
b_max=1.0,
clip=True,
),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
CropForegroundd(keys=["image", "label"], source_key="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 = [
LoadNiftid(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ScaleIntensityRanged(
keys=["image"],
a_min=-120,
a_max=170,
b_min=0.0,
b_max=1.0,
clip=True,
),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
CropForegroundd(keys=["image", "label"], source_key="image"),
Spacingd(keys=['image', 'label'],
pixdim=opt.resolution,
mode=('bilinear', 'nearest')),
ToTensord(keys=['image', 'label'])
]
else:
train_transforms = [
LoadNiftid(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ScaleIntensityRanged(
keys=["image"],
a_min=-120,
a_max=170,
b_min=0.0,
b_max=1.0,
clip=True,
),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
CropForegroundd(keys=["image", "label"], source_key="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 = [
LoadNiftid(keys=['image', 'label']),
AddChanneld(keys=['image', 'label']),
ScaleIntensityRanged(
keys=["image"],
a_min=-120,
a_max=170,
b_min=0.0,
b_max=1.0,
clip=True,
),
NormalizeIntensityd(keys=['image']),
ScaleIntensityd(keys=['image']),
CropForegroundd(keys=["image", "label"], source_key="image"),
ToTensord(keys=['image', 'label'])
]
train_transforms = Compose(train_transforms)
val_transforms = Compose(val_transforms)
# create a training data loader
check_train = monai.data.Dataset(data=train_dicts,
transform=train_transforms)
train_loader = DataLoader(check_train,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
# create a training_dice data loader
check_val = monai.data.Dataset(data=train_dice_dicts,
transform=val_transforms)
train_dice_loader = DataLoader(check_val,
batch_size=1,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
# create a validation data loader
check_val = monai.data.Dataset(data=val_dicts, transform=val_transforms)
val_loader = DataLoader(check_val,
batch_size=1,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
# create a validation data loader
check_val = monai.data.Dataset(data=test_dicts, transform=val_transforms)
test_loader = DataLoader(check_val,
batch_size=1,
num_workers=opt.workers,
pin_memory=torch.cuda.is_available())
# try to use all the available GPUs
devices = get_devices_spec(None)
# build the network
net = build_net()
net.cuda()
if num_gpus > 1:
net = torch.nn.DataParallel(net)
if opt.preload is not None:
net.load_state_dict(torch.load(opt.preload))
dice_metric = DiceMetric(include_background=True,
to_onehot_y=False,
sigmoid=True,
reduction="mean")
# loss_function = monai.losses.DiceLoss(sigmoid=True)
loss_function = monai.losses.TverskyLoss(sigmoid=True, alpha=0.3, beta=0.7)
optim = torch.optim.Adam(net.parameters(), lr=opt.lr)
net_scheduler = get_scheduler(optim, opt)
# start a typical PyTorch training
val_interval = 1
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
writer = SummaryWriter()
for epoch in range(opt.epochs):
print("-" * 10)
print(f"epoch {epoch + 1}/{opt.epochs}")
net.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["image"].cuda(
), batch_data["label"].cuda()
optim.zero_grad()
outputs = net(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optim.step()
epoch_loss += loss.item()
epoch_len = len(check_train) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
update_learning_rate(net_scheduler, optim)
if (epoch + 1) % val_interval == 0:
net.eval()
with torch.no_grad():
def plot_dice(images_loader):
metric_sum = 0.0
metric_count = 0
val_images = None
val_labels = None
val_outputs = None
for data in images_loader:
val_images, val_labels = data["image"].cuda(
), data["label"].cuda()
roi_size = opt.patch_size
sw_batch_size = 4
val_outputs = sliding_window_inference(
val_images, roi_size, sw_batch_size, net)
value = dice_metric(y_pred=val_outputs, y=val_labels)
metric_count += len(value)
metric_sum += value.item() * len(value)
metric = metric_sum / metric_count
metric_values.append(metric)
return metric, val_images, val_labels, val_outputs
metric, val_images, val_labels, val_outputs = plot_dice(
val_loader)
# Save best model
if metric > best_metric:
best_metric = metric
best_metric_epoch = epoch + 1
torch.save(net.state_dict(), "best_metric_model.pth")
print("saved new best metric model")
metric_train, train_images, train_labels, train_outputs = plot_dice(
train_dice_loader)
metric_test, test_images, test_labels, test_outputs = plot_dice(
test_loader)
# Logger bar
print(
"current epoch: {} Training dice: {:.4f} Validation dice: {:.4f} Testing dice: {:.4f} Best Validation dice: {:.4f} at epoch {}"
.format(epoch + 1, metric_train, metric, metric_test,
best_metric, best_metric_epoch))
writer.add_scalar("Mean_epoch_loss", epoch_loss, epoch + 1)
writer.add_scalar("Testing_dice", metric_test, epoch + 1)
writer.add_scalar("Training_dice", metric_train, epoch + 1)
writer.add_scalar("Validation_dice", metric, epoch + 1)
# plot the last model output as GIF image in TensorBoard with the corresponding image and label
val_outputs = (val_outputs.sigmoid() >= 0.5).float()
plot_2d_or_3d_image(val_images,
epoch + 1,
writer,
index=0,
tag="validation image")
plot_2d_or_3d_image(val_labels,
epoch + 1,
writer,
index=0,
tag="validation label")
plot_2d_or_3d_image(val_outputs,
epoch + 1,
writer,
index=0,
tag="validation inference")
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
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",
"IXI314-IOP-0889-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI249-Guys-1072-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI609-HH-2600-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI173-HH-1590-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI020-Guys-0700-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI342-Guys-0909-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI134-Guys-0780-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI577-HH-2661-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI066-Guys-0731-T1.nii.gz"
]),
os.sep.join([
"workspace", "data", "medical", "ixi", "IXI-T1",
"IXI130-HH-1528-T1.nii.gz"
]),
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, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0],
dtype=np.int64)
train_files = [{
"img": img,
"label": label
} for img, label in zip(images[:10], labels[:10])]
val_files = [{
"img": img,
"label": label
} for img, label in zip(images[-10:], labels[-10:])]
# Define transforms for image
train_transforms = Compose([
LoadImaged(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]),
ToTensord(keys=["img"]),
])
val_transforms = Compose([
LoadImaged(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
ToTensord(keys=["img"]),
])
# Define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["label"])
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds,
batch_size=2,
shuffle=True,
num_workers=4,
pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds,
batch_size=2,
num_workers=4,
pin_memory=torch.cuda.is_available())
# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.densenet.densenet121(spatial_dims=3,
in_channels=1,
out_channels=2).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
writer = SummaryWriter()
for epoch in range(5):
print("-" * 10)
print(f"epoch {epoch + 1}/{5}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["img"].to(
device), batch_data["label"].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(),
epoch_len * epoch + step)
epoch_loss /= step
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
y_pred = torch.tensor([], dtype=torch.float32, device=device)
y = torch.tensor([], dtype=torch.long, device=device)
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["label"].to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
acc_metric = acc_value.sum().item() / len(acc_value)
auc_metric = compute_roc_auc(y_pred,
y,
to_onehot_y=True,
softmax=True)
if acc_metric > best_metric:
best_metric = acc_metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(),
"best_metric_model_classification3d_dict.pth")
print("saved new best metric model")
print(
"current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}"
.format(epoch + 1, acc_metric, auc_metric, best_metric,
best_metric_epoch))
writer.add_scalar("val_accuracy", acc_metric, epoch + 1)
print(
f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}"
)
writer.close()
def main(hparams):
print('===== INITIAL PARAMETERS =====')
print('Model name: ', hparams.name)
print('Batch size: ', hparams.batch_size)
print('Patch size: ', hparams.patch_size)
print('Epochs: ', hparams.epochs)
print('Learning rate: ', hparams.learning_rate)
print('Loss function: ', hparams.loss)
print()
### Data collection
data_dir = 'data/'
print('Available directories: ', os.listdir(data_dir))
# Get paths for images and masks, organize into dictionaries
images = sorted(glob.glob(data_dir + '**/*CTImg*', recursive=True))
masks = sorted(glob.glob(data_dir + '**/*Mask*', recursive=True))
data_dicts = [{'image': image_file, 'mask': mask_file} for image_file, mask_file in zip(images, masks)]
# Dataset selection
train_dicts = select_animals(images, masks, [12, 13, 14, 18, 20])
val_dicts = select_animals(images, masks, [25])
test_dicts = select_animals(images, masks, [27])
data_keys = ['image', 'mask']
# Data transformation
data_transforms = Compose([
LoadNiftid(keys=data_keys),
AddChanneld(keys=data_keys),
ScaleIntensityd(keys=data_keys),
CropForegroundd(keys=data_keys, source_key='image'),
RandSpatialCropd(
keys=data_keys,
roi_size=(hparams.patch_size, hparams.patch_size, 1),
random_size=False
),
])
train_transforms = Compose([
data_transforms,
ToTensord(keys=data_keys)
])
val_transforms = Compose([
data_transforms,
ToTensord(keys=data_keys)
])
test_transforms = Compose([
data_transforms,
ToTensord(keys=data_keys)
])
# Data loaders
data_loaders = {
'train': create_loader(train_dicts, batch_size=hparams.batch_size, transforms=train_transforms, shuffle=True),
'val': create_loader(val_dicts, transforms=val_transforms),
'test': create_loader(test_dicts, transforms=test_transforms)
}
for key in data_loaders:
print(key, len(data_loaders[key]))
### Model training
if hparams.loss == 'Dice':
criterion = monai.losses.DiceLoss(to_onehot_y=True, do_softmax=True)
elif hparams.loss == 'CrossEntropy':
criterion = nn.CrossEntropyLoss()
model = UNet(
dimensions=2,
in_channels=1,
out_channels=2,
channels=(64, 128, 258, 512, 1024),
strides=(2, 2, 2, 2),
norm=monai.networks.layers.Norm.BATCH,
criterion=criterion,
hparams=hparams,
)
early_stopping = EarlyStopping('val_loss')
checkpoint_callback = ModelCheckpoint
logger = TensorBoardLogger('models/' + hparams.name + '/tb_logs', name=hparams.name)
trainer = Trainer(
check_val_every_n_epoch=5,
default_save_path='models/' + hparams.name + '/checkpoints',
# early_stop_callback=early_stopping,
gpus=1,
max_epochs=hparams.epochs,
# min_epochs=10,
logger=logger
)
trainer.fit(
model,
train_dataloader=data_loaders['train'],
val_dataloaders=data_loaders['val']
)
def main(tempdir):
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print(f"generating synthetic data to {tempdir} (this may take a while)")
for i in range(5):
im, seg = create_test_image_2d(128, 128, num_seg_classes=1)
Image.fromarray((im * 255).astype("uint8")).save(
os.path.join(tempdir, f"img{i:d}.png"))
Image.fromarray((seg * 255).astype("uint8")).save(
os.path.join(tempdir, f"seg{i:d}.png"))
images = sorted(glob(os.path.join(tempdir, "img*.png")))
segs = sorted(glob(os.path.join(tempdir, "seg*.png")))
val_files = [{"img": img, "seg": seg} for img, seg in zip(images, segs)]
# define transforms for image and segmentation
val_transforms = Compose([
LoadImaged(keys=["img", "seg"]),
AddChanneld(keys=["img", "seg"]),
ScaleIntensityd(keys=["img", "seg"]),
EnsureTyped(keys=["img", "seg"]),
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
# sliding window inference need to input 1 image in every iteration
val_loader = DataLoader(val_ds,
batch_size=1,
num_workers=4,
collate_fn=list_data_collate)
dice_metric = DiceMetric(include_background=True,
reduction="mean",
get_not_nans=False)
post_trans = Compose(
[EnsureType(),
Activations(sigmoid=True),
AsDiscrete(threshold=0.5)])
saver = SaveImage(output_dir="./output",
output_ext=".png",
output_postfix="seg")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(
spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
).to(device)
model.load_state_dict(
torch.load("best_metric_model_segmentation2d_dict.pth"))
model.eval()
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(
device), val_data["seg"].to(device)
# define sliding window size and batch size for windows inference
roi_size = (96, 96)
sw_batch_size = 4
val_outputs = sliding_window_inference(val_images, roi_size,
sw_batch_size, model)
val_outputs = [post_trans(i) for i in decollate_batch(val_outputs)]
val_labels = decollate_batch(val_labels)
# compute metric for current iteration
dice_metric(y_pred=val_outputs, y=val_labels)
for val_output in val_outputs:
saver(val_output)
# aggregate the final mean dice result
print("evaluation metric:", dice_metric.aggregate().item())
# reset the status
dice_metric.reset()
def test_fail_non_random(self):
transforms = Compose([AddChanneld("im"), SpatialPadd("im", 1)])
with self.assertRaises(RuntimeError):
TestTimeAugmentation(transforms, None, None, None)
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
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)
# test to convert interpolation mode for 1 data of model output batch
convert_inverse_interp_mode(seg_dict,
mode="nearest",
align_corners=None)
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)
# Inverse of batch
batch_inverter = BatchInverseTransform(transforms,
loader,
collate_fn=no_collation)
with allow_missing_keys_mode(transforms):
inv_batch = batch_inverter(segs_dict)
self.assertEqual(inv_batch[0]["label"].shape[1:],
test_data[0]["label"].shape)
def test_test_time_augmentation(self):
input_size = (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),
RandAffined(
keys,
prob=1.0,
spatial_size=(30, 30),
rotate_range=(np.pi / 3, np.pi / 3),
translate_range=(3, 3),
scale_range=((0.8, 1), (0.8, 1)),
padding_mode="zeros",
mode=("bilinear", "nearest"),
as_tensor_output=False,
),
CropForegroundd(keys, source_key="image"),
DivisiblePadd(keys, 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(2, 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)
post_trans = Compose([
Activations(sigmoid=True),
AsDiscrete(threshold_values=True),
])
def inferrer_fn(x):
return post_trans(model(x))
tt_aug = TestTimeAugmentation(transforms,
batch_size=5,
num_workers=0,
inferrer_fn=inferrer_fn,
device=device)
mode, mean, std, vvc = tt_aug(test_data)
self.assertEqual(mode.shape, (1, ) + input_size)
self.assertEqual(mean.shape, (1, ) + input_size)
self.assertTrue(all(np.unique(mode) == (0, 1)))
self.assertEqual((mean.min(), mean.max()), (0.0, 1.0))
self.assertEqual(std.shape, (1, ) + input_size)
self.assertIsInstance(vvc, float)
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/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
])
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 DenseNet121
net = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
)
device = torch.device("cuda:0")
def prepare_batch(batch, device=None, non_blocking=False):
Here we use several transforms to augment the dataset:
1. `LoadImaged` loads the spleen CT images and labels from NIfTI format files.
1. `AddChanneld` as the original data doesn't have channel dim, add 1 dim to construct "channel first" shape.
1. `Spacingd` adjusts the spacing by `pixdim=(1.5, 1.5, 2.)` based on the affine matrix.
1. `Orientationd` unifies the data orientation based on the affine matrix.
1. `ScaleIntensityRanged` extracts intensity range [-57, 164] and scales to [0, 1].
1. `CropForegroundd` removes all zero borders to focus on the valid body area of the images and labels.
1. `RandCropByPosNegLabeld` randomly crop patch samples from big image based on pos / neg ratio.
The image centers of negative samples must be in valid body area.
1. `RandAffined` efficiently performs `rotate`, `scale`, `shear`, `translate`, etc. together based on PyTorch affine transform.
1. `ToTensord` converts the numpy array to PyTorch Tensor for further steps.
"""
train_transforms = Compose([
LoadImaged(keys=["image", "label"]),
AddChanneld(keys=["image", "label"]),
Spacingd(keys=["image", "label"],
pixdim=(1.5, 1.5, 2.0),
mode=("bilinear", "nearest")),
Orientationd(keys=["image", "label"], axcodes="RAS"),
ScaleIntensityRanged(
keys=["image"],
a_min=-1000,
a_max=300,
b_min=0.0,
b_max=1.0,
clip=True,
),
#CropForegroundd(keys=["image", "label"], source_key="image"),
RandCropByPosNegLabeld(
keys=["image", "label"],
test_files = data_dicts[0:52]
"""## Set deterministic training for reproducibility"""
set_determinism(seed=0)
'''
Label 1: Bladder
Label 2: Liver
Label 3: Lungs
Label 4: Heart
Label 5: Pancreas
'''
test_transforms = Compose([
LoadImaged(keys="image"),
AddChanneld(keys="image"),
Spacingd(keys="image", pixdim=(1.5, 1.5, 2.0), mode="bilinear"),
Orientationd(keys="image", axcodes="RAS"),
ScaleIntensityRanged(
keys="image",
a_min=-1000,
a_max=300,
b_min=0.0,
b_max=1.0,
clip=True,
),
#CropForegroundd(keys=["image", "label"], source_key="image"),
ToTensord(keys="image"),
])
test_ds = CacheDataset(data=test_files,