def pad_list_data_collate(
batch: Sequence,
method: Union[Method, str] = Method.SYMMETRIC,
mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,
**np_kwargs,
):
"""
Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`.
Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest
tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of
different sizes.
This can be used on both list and dictionary data. In the case of the dictionary data, this transform will be added
to the list of invertible transforms.
The inverse can be called using the static method: `monai.transforms.croppad.batch.PadListDataCollate.inverse`.
Args:
batch: batch of data to pad-collate
method: padding method (see :py:class:`monai.transforms.SpatialPad`)
mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
"""
from monai.transforms.croppad.batch import PadListDataCollate # needs to be here to avoid circular import
return PadListDataCollate(method=method, mode=mode, **np_kwargs)(batch)
def __getitem__(self, index: int) -> Dict[Hashable, np.ndarray]:
data = dict(self.data[index])
# If pad collation was used, then we need to undo this first
if self.pad_collation_used:
data = PadListDataCollate.inverse(data)
return self.invertible_transform.inverse(data)
def pad_list_data_collate(
batch: Sequence,
method: Union[Method, str] = Method.SYMMETRIC,
mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT,
):
"""
Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`.
Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest
tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of
different sizes.
This can be used on both list and dictionary data. In the case of the dictionary data, this transform will be added
to the list of invertible transforms.
The inverse can be called using the static method: `monai.transforms.croppad.batch.PadListDataCollate.inverse`.
Args:
batch: batch of data to pad-collate
method: padding method (see :py:class:`monai.transforms.SpatialPad`)
mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
"""
from monai.transforms.croppad.batch import PadListDataCollate # needs to be here to avoid circular import
return PadListDataCollate(method, mode)(batch)
def __call__(
self,
data: Dict[str, Any],
num_examples: int = 10
) -> Union[Tuple[NdarrayOrTensor, NdarrayOrTensor, NdarrayOrTensor, float],
NdarrayOrTensor]:
"""
Args:
data: dictionary data to be processed.
num_examples: number of realisations to be processed and results combined.
Returns:
- if `return_full_data==False`: mode, mean, std, vvc. The mode, mean and standard deviation are
calculated across `num_examples` outputs at each voxel. The volume variation coefficient (VVC)
is `std/mean` across the whole output, including `num_examples`. See original paper for clarification.
- if `return_full_data==False`: data is returned as-is after applying the `inferrer_fn` and then
concatenating across the first dimension containing `num_examples`. This allows the user to perform
their own analysis if desired.
"""
d = dict(data)
# check num examples is multiple of batch size
if num_examples % self.batch_size != 0:
raise ValueError("num_examples should be multiple of batch size.")
# generate batch of data of size == batch_size, dataset and dataloader
data_in = [deepcopy(d) for _ in range(num_examples)]
ds = Dataset(data_in, self.transform)
dl = DataLoader(ds,
num_workers=self.num_workers,
batch_size=self.batch_size,
collate_fn=pad_list_data_collate)
outs: List = []
for b in tqdm(dl) if has_tqdm and self.progress else dl:
# do model forward pass
b[self._pred_key] = self.inferrer_fn(b[self.image_key].to(
self.device))
outs.extend([
self.inverter(PadListDataCollate.inverse(i))[self._pred_key]
for i in decollate_batch(b)
])
output: NdarrayOrTensor = stack(outs, 0)
if self.return_full_data:
return output
# calculate metrics
_mode = mode(output, dim=0)
mean = output.mean(0)
std = output.std(0)
vvc = (output.std() / output.mean()).item()
return _mode, mean, std, vvc
def __getitem__(self, index: int):
data = dict(self.data[index])
# If pad collation was used, then we need to undo this first
if self.pad_collation_used:
data = PadListDataCollate.inverse(data)
if not isinstance(self.invertible_transform, InvertibleTransform):
warnings.warn("transform is not invertible, can't invert transform for the input data.")
return data
return self.invertible_transform.inverse(data)
def train(self,
train_info,
valid_info,
hyperparameters,
run_data_check=False):
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
if not run_data_check:
start_dt = datetime.datetime.now()
start_dt_string = start_dt.strftime('%d/%m/%Y %H:%M:%S')
print(f'Training started: {start_dt_string}')
# 1. Create folders to save the model
timedate_info = str(
datetime.datetime.now()).split(' ')[0] + '_' + str(
datetime.datetime.now().strftime("%H:%M:%S")).replace(
':', '-')
path_to_model = os.path.join(
self.out_dir, 'trained_models',
self.unique_name + '_' + timedate_info)
os.mkdir(path_to_model)
# 2. Load hyperparameters
learning_rate = hyperparameters['learning_rate']
weight_decay = hyperparameters['weight_decay']
total_epoch = hyperparameters['total_epoch']
multiplicator = hyperparameters['multiplicator']
batch_size = hyperparameters['batch_size']
validation_epoch = hyperparameters['validation_epoch']
validation_interval = hyperparameters['validation_interval']
H = hyperparameters['H']
L = hyperparameters['L']
# 3. Consider class imbalance
negative, positive = 0, 0
for _, label in train_info:
if int(label) == 0:
negative += 1
elif int(label) == 1:
positive += 1
pos_weight = torch.Tensor([(negative / positive)]).to(self.device)
# 4. Create train and validation loaders, batch_size = 10 for validation loader (10 central slices)
train_data = get_data_from_info(self.image_data_dir, self.seg_data_dir,
train_info)
valid_data = get_data_from_info(self.image_data_dir, self.seg_data_dir,
valid_info)
large_image_splitter(train_data, self.cache_dir)
set_determinism(seed=100)
train_trans, valid_trans = self.transformations(H, L)
train_dataset = PersistentDataset(
data=train_data[:],
transform=train_trans,
cache_dir=self.persistent_dataset_dir)
valid_dataset = PersistentDataset(
data=valid_data[:],
transform=valid_trans,
cache_dir=self.persistent_dataset_dir)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=self.pin_memory,
num_workers=self.num_workers,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
valid_loader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=self.pin_memory,
num_workers=self.num_workers,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
# Perform data checks
if run_data_check:
check_data = monai.utils.misc.first(train_loader)
print(check_data["image"].shape, check_data["label"])
for i in range(batch_size):
multi_slice_viewer(
check_data["image"][i, 0, :, :, :],
check_data["image_meta_dict"]["filename_or_obj"][i])
exit()
"""c = 1
for d in train_loader:
img = d["image"]
seg = d["seg"][0]
seg, _ = nrrd.read(seg)
img_name = d["image_meta_dict"]["filename_or_obj"][0]
print(c, "Name:", img_name, "Size:", img.nelement()*img.element_size()/1024/1024, "MB", "shape:", img.shape)
multi_slice_viewer(img[0, 0, :, :, :], d["image_meta_dict"]["filename_or_obj"][0])
#multi_slice_viewer(seg, d["image_meta_dict"]["filename_or_obj"][0])
c += 1
exit()"""
# 5. Prepare model
model = ModelCT().to(self.device)
# 6. Define loss function, optimizer and scheduler
loss_function = torch.nn.BCEWithLogitsLoss(
pos_weight) # pos_weight for class imbalance
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,
multiplicator,
last_epoch=-1)
# 7. Create post validation transforms and handlers
path_to_tensorboard = os.path.join(self.out_dir, 'tensorboard')
writer = SummaryWriter(log_dir=path_to_tensorboard)
valid_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
])
valid_handlers = [
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(summary_writer=writer,
output_transform=lambda x: None),
CheckpointSaver(save_dir=path_to_model,
save_dict={"model": model},
save_key_metric=True),
MetricsSaver(save_dir=path_to_model,
metrics=['Valid_AUC', 'Valid_ACC']),
]
# 8. Create validatior
discrete = AsDiscrete(threshold_values=True)
evaluator = SupervisedEvaluator(
device=self.device,
val_data_loader=valid_loader,
network=model,
post_transform=valid_post_transforms,
key_val_metric={
"Valid_AUC":
ROCAUC(output_transform=lambda x: (x["pred"], x["label"]))
},
additional_metrics={
"Valid_Accuracy":
Accuracy(output_transform=lambda x:
(discrete(x["pred"]), x["label"]))
},
val_handlers=valid_handlers,
amp=self.amp,
)
# 9. Create trainer
# Loss function does the last sigmoid, so we dont need it here.
train_post_transforms = Compose([
# Empty
])
logger = MetricLogger(evaluator=evaluator)
train_handlers = [
logger,
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True),
ValidationHandlerCT(validator=evaluator,
start=validation_epoch,
interval=validation_interval,
epoch_level=True),
StatsHandler(tag_name="loss",
output_transform=lambda x: x["loss"]),
TensorBoardStatsHandler(summary_writer=writer,
tag_name="Train_Loss",
output_transform=lambda x: x["loss"]),
CheckpointSaver(save_dir=path_to_model,
save_dict={
"model": model,
"opt": optimizer
},
save_interval=1,
n_saved=1),
]
trainer = SupervisedTrainer(
device=self.device,
max_epochs=total_epoch,
train_data_loader=train_loader,
network=model,
optimizer=optimizer,
loss_function=loss_function,
post_transform=train_post_transforms,
train_handlers=train_handlers,
amp=self.amp,
)
# 10. Run trainer
trainer.run()
# 11. Save results
np.save(path_to_model + '/AUCS.npy',
np.array(logger.metrics['Valid_AUC']))
np.save(path_to_model + '/ACCS.npy',
np.array(logger.metrics['Valid_ACC']))
np.save(path_to_model + '/LOSSES.npy', np.array(logger.loss))
np.save(path_to_model + '/PARAMETERS.npy', np.array(hyperparameters))
return path_to_model
def main(train_output):
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print_config()
# Setup directories
dirs = setup_directories()
# Setup torch device
device, using_gpu = create_device("cuda")
# Load and randomize images
# HACKATON image and segmentation data
hackathon_dir = os.path.join(dirs["data"], 'HACKATHON')
map_fn = lambda x: (x[0], int(x[1]))
with open(os.path.join(hackathon_dir, "train.txt"), 'r') as fp:
train_info_hackathon = [
map_fn(entry.strip().split(',')) for entry in fp.readlines()
]
image_dir = os.path.join(hackathon_dir, 'images', 'train')
seg_dir = os.path.join(hackathon_dir, 'segmentations', 'train')
_train_data_hackathon = get_data_from_info(image_dir,
seg_dir,
train_info_hackathon,
dual_output=False)
large_image_splitter(_train_data_hackathon, dirs["cache"])
balance_training_data(_train_data_hackathon, seed=72)
# PSUF data
"""psuf_dir = os.path.join(dirs["data"], 'psuf')
with open(os.path.join(psuf_dir, "train.txt"), 'r') as fp:
train_info = [entry.strip().split(',') for entry in fp.readlines()]
image_dir = os.path.join(psuf_dir, 'images')
train_data_psuf = get_data_from_info(image_dir, None, train_info)"""
# Split data into train, validate and test
train_split, test_data_hackathon = train_test_split(_train_data_hackathon,
test_size=0.2,
shuffle=True,
random_state=42)
#train_data_hackathon, valid_data_hackathon = train_test_split(train_split, test_size=0.2, shuffle=True, random_state=43)
# Setup transforms
# Crop foreground
crop_foreground = CropForegroundd(
keys=["image"],
source_key="image",
margin=(5, 5, 0),
#select_fn = lambda x: x != 0
)
# Crop Z
crop_z = RelativeCropZd(keys=["image"], relative_z_roi=(0.07, 0.12))
# Window width and level (window center)
WW, WL = 1500, -600
ct_window = CTWindowd(keys=["image"], width=WW, level=WL)
spatial_pad = SpatialPadd(keys=["image"], spatial_size=(-1, -1, 30))
resize = Resized(keys=["image"],
spatial_size=(int(512 * 0.50), int(512 * 0.50), -1),
mode="trilinear")
# Create transforms
common_transform = Compose([
LoadImaged(keys=["image"]),
ct_window,
CTSegmentation(keys=["image"]),
AddChanneld(keys=["image"]),
resize,
crop_foreground,
crop_z,
spatial_pad,
])
hackathon_train_transfrom = Compose([
common_transform,
ToTensord(keys=["image"]),
]).flatten()
psuf_transforms = Compose([
LoadImaged(keys=["image"]),
AddChanneld(keys=["image"]),
ToTensord(keys=["image"]),
])
# Setup data
#set_determinism(seed=100)
test_dataset = PersistentDataset(data=test_data_hackathon[:],
transform=hackathon_train_transfrom,
cache_dir=dirs["persistent"])
test_loader = DataLoader(test_dataset,
batch_size=2,
shuffle=True,
pin_memory=using_gpu,
num_workers=1,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
# Setup network, loss function, optimizer and scheduler
network = nets.DenseNet121(spatial_dims=3, in_channels=1,
out_channels=1).to(device)
# Setup validator and trainer
valid_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
])
# Setup tester
tester = Tester(device=device,
test_data_loader=test_loader,
load_dir=train_output,
out_dir=dirs["out"],
network=network,
post_transform=valid_post_transforms,
non_blocking=using_gpu,
amp=using_gpu)
# Run tester
tester.run()
def main():
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
print_config()
# Setup directories
dirs = setup_directories()
# Setup torch device
device, using_gpu = create_device("cuda")
# Load and randomize images
# HACKATON image and segmentation data
hackathon_dir = os.path.join(dirs["data"], 'HACKATHON')
map_fn = lambda x: (x[0], int(x[1]))
with open(os.path.join(hackathon_dir, "train.txt"), 'r') as fp:
train_info_hackathon = [
map_fn(entry.strip().split(',')) for entry in fp.readlines()
]
image_dir = os.path.join(hackathon_dir, 'images', 'train')
seg_dir = os.path.join(hackathon_dir, 'segmentations', 'train')
_train_data_hackathon = get_data_from_info(image_dir,
seg_dir,
train_info_hackathon,
dual_output=False)
_train_data_hackathon = large_image_splitter(_train_data_hackathon,
dirs["cache"])
copy_list = transform_and_copy(_train_data_hackathon, dirs['cache'])
balance_training_data2(_train_data_hackathon, copy_list, seed=72)
# PSUF data
"""psuf_dir = os.path.join(dirs["data"], 'psuf')
with open(os.path.join(psuf_dir, "train.txt"), 'r') as fp:
train_info = [entry.strip().split(',') for entry in fp.readlines()]
image_dir = os.path.join(psuf_dir, 'images')
train_data_psuf = get_data_from_info(image_dir, None, train_info)"""
# Split data into train, validate and test
train_split, test_data_hackathon = train_test_split(_train_data_hackathon,
test_size=0.2,
shuffle=True,
random_state=42)
train_data_hackathon, valid_data_hackathon = train_test_split(
train_split, test_size=0.2, shuffle=True, random_state=43)
#balance_training_data(train_data_hackathon, seed=72)
#balance_training_data(valid_data_hackathon, seed=73)
#balance_training_data(test_data_hackathon, seed=74)
# Setup transforms
# Crop foreground
crop_foreground = CropForegroundd(keys=["image"],
source_key="image",
margin=(5, 5, 0),
select_fn=lambda x: x != 0)
# Crop Z
crop_z = RelativeCropZd(keys=["image"], relative_z_roi=(0.07, 0.12))
# Window width and level (window center)
WW, WL = 1500, -600
ct_window = CTWindowd(keys=["image"], width=WW, level=WL)
# Random axis flip
rand_x_flip = RandFlipd(keys=["image"], spatial_axis=0, prob=0.50)
rand_y_flip = RandFlipd(keys=["image"], spatial_axis=1, prob=0.50)
rand_z_flip = RandFlipd(keys=["image"], spatial_axis=2, prob=0.50)
# Rand affine transform
rand_affine = RandAffined(keys=["image"],
prob=0.5,
rotate_range=(0, 0, np.pi / 12),
shear_range=(0.07, 0.07, 0.0),
translate_range=(0, 0, 0),
scale_range=(0.07, 0.07, 0.0),
padding_mode="zeros")
# Pad image to have hight at least 30
spatial_pad = SpatialPadd(keys=["image"], spatial_size=(-1, -1, 30))
resize = Resized(keys=["image"],
spatial_size=(int(512 * 0.50), int(512 * 0.50), -1),
mode="trilinear")
# Apply Gaussian noise
rand_gaussian_noise = RandGaussianNoised(keys=["image"],
prob=0.25,
mean=0.0,
std=0.1)
# Create transforms
common_transform = Compose([
LoadImaged(keys=["image"]),
ct_window,
CTSegmentation(keys=["image"]),
AddChanneld(keys=["image"]),
resize,
crop_foreground,
crop_z,
spatial_pad,
])
hackathon_train_transform = Compose([
common_transform,
rand_x_flip,
rand_y_flip,
rand_z_flip,
rand_affine,
rand_gaussian_noise,
ToTensord(keys=["image"]),
]).flatten()
hackathon_valid_transfrom = Compose([
common_transform,
#rand_x_flip,
#rand_y_flip,
#rand_z_flip,
#rand_affine,
ToTensord(keys=["image"]),
]).flatten()
hackathon_test_transfrom = Compose([
common_transform,
ToTensord(keys=["image"]),
]).flatten()
psuf_transforms = Compose([
LoadImaged(keys=["image"]),
AddChanneld(keys=["image"]),
ToTensord(keys=["image"]),
])
# Setup data
#set_determinism(seed=100)
train_dataset = PersistentDataset(data=train_data_hackathon[:],
transform=hackathon_train_transform,
cache_dir=dirs["persistent"])
valid_dataset = PersistentDataset(data=valid_data_hackathon[:],
transform=hackathon_valid_transfrom,
cache_dir=dirs["persistent"])
test_dataset = PersistentDataset(data=test_data_hackathon[:],
transform=hackathon_test_transfrom,
cache_dir=dirs["persistent"])
train_loader = DataLoader(
train_dataset,
batch_size=4,
#shuffle=True,
pin_memory=using_gpu,
num_workers=2,
sampler=ImbalancedDatasetSampler(
train_data_hackathon,
callback_get_label=lambda x, i: x[i]['_label']),
collate_fn=PadListDataCollate(Method.SYMMETRIC, NumpyPadMode.CONSTANT))
valid_loader = DataLoader(
valid_dataset,
batch_size=4,
shuffle=False,
pin_memory=using_gpu,
num_workers=2,
sampler=ImbalancedDatasetSampler(
valid_data_hackathon,
callback_get_label=lambda x, i: x[i]['_label']),
collate_fn=PadListDataCollate(Method.SYMMETRIC, NumpyPadMode.CONSTANT))
test_loader = DataLoader(test_dataset,
batch_size=4,
shuffle=False,
pin_memory=using_gpu,
num_workers=2,
collate_fn=PadListDataCollate(
Method.SYMMETRIC, NumpyPadMode.CONSTANT))
# Setup network, loss function, optimizer and scheduler
network = nets.DenseNet121(spatial_dims=3, in_channels=1,
out_channels=1).to(device)
# pos_weight for class imbalance
_, n, p = calculate_class_imbalance(train_data_hackathon)
pos_weight = torch.Tensor([n, p]).to(device)
loss_function = torch.nn.BCEWithLogitsLoss(pos_weight)
optimizer = torch.optim.Adam(network.parameters(), lr=1e-4, weight_decay=0)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,
gamma=0.95,
last_epoch=-1)
# Setup validator and trainer
valid_post_transforms = Compose([
Activationsd(keys="pred", sigmoid=True),
#Activationsd(keys="pred", softmax=True),
])
validator = Validator(device=device,
val_data_loader=valid_loader,
network=network,
post_transform=valid_post_transforms,
amp=using_gpu,
non_blocking=using_gpu)
trainer = Trainer(device=device,
out_dir=dirs["out"],
out_name="DenseNet121",
max_epochs=120,
validation_epoch=1,
validation_interval=1,
train_data_loader=train_loader,
network=network,
optimizer=optimizer,
loss_function=loss_function,
lr_scheduler=None,
validator=validator,
amp=using_gpu,
non_blocking=using_gpu)
"""x_max, y_max, z_max, size_max = 0, 0, 0, 0
for data in valid_loader:
image = data["image"]
label = data["label"]
print()
print(len(data['image_transforms']))
#print(data['image_transforms'])
print(label)
shape = image.shape
x_max = max(x_max, shape[-3])
y_max = max(y_max, shape[-2])
z_max = max(z_max, shape[-1])
size = int(image.nelement()*image.element_size()/1024/1024)
size_max = max(size_max, size)
print("shape:", shape, "size:", str(size)+"MB")
#multi_slice_viewer(image[0, 0, :, :, :], str(label))
print(x_max, y_max, z_max, str(size_max)+"MB")
exit()"""
# Run trainer
train_output = trainer.run()
# Setup tester
tester = Tester(device=device,
test_data_loader=test_loader,
load_dir=train_output,
out_dir=dirs["out"],
network=network,
post_transform=valid_post_transforms,
non_blocking=using_gpu,
amp=using_gpu)
# Run tester
tester.run()