def make_crops(bids_folder, metadata_file, out_dir, out_file, new_size):
"""
Create a new dataset of crops from an existing dataset.
Given a folder of images, and a csv file containing the info
about the dataset, this function makes random crops of the images and
save them to disk
bids_folder: folder where the images are stored (in BIDS format)
metadata_file: path to csv file where the info about images is stored.
out_dir: directory where to save the cropped images
out_file: file where to store the info about the crops
resample_size: size to resample the images.
"""
df_metadata = pd.read_csv(metadata_file)
layout = bids.layout.BIDSLayout([(bids_folder, 'bids')])
# For each entry in the metadata file
rows_list = []
for subj in df_metadata.itertuples():
# locate the corresponding MRI scan
ptid = subj.PTID
ptid_bids = 'ADNI' + ptid[0:3] + 'S' + ptid[6:]
# Hardcoded baselines
try:
file = layout.get(subject=ptid_bids,
extensions='.nii.gz',
modality='anat',
session='M00',
return_type='file')[0]
except:
print('Ignoring subject ' + ptid)
# Actually perform the cropping
new_crops = slice_generator(file, out_dir, new_size)
# Iterate over all the new crops
for crop in new_crops:
dict = {"path": crop, "PTID": subj.PTID, "DX": subj.DX}
rows_list.append(dict)
# Save the new info about the image in df_crop
df_crop = pd.DataFrame(rows_list)
df_crop.to_csv(out_file)
else:
is_hpc = True
#
# Initial checks
#
# Check that bids directory is not empty(TODO)
project_root = args.in_dir[0]
print(project_root)
layout = bids.layout.BIDSLayout([(project_root, 'bids')],
exclude='derivatives/')
assert len(layout.get_subjects()) > 0, "No subjects in directory!"
# Create img list
files = layout.get(extensions='.nii.gz', modality='anat')
n_total_jobs = int(args.num_threads[0])
if not args.histmatch:
assert args.template_file is None and not args.template_norm, "Unnecessary template if not histmatch"
if args.histmatch:
assert args.template_file is not None, "Need template for histogram matching"
if args.template_file is not None:
assert os.path.exists(args.template_file[0]), "Template file not found"
# Get list of input images.
# metadata = pd.read_csv(args.in_metadata[0]).dropna()
# img_list = metadata["MRI_PATH"].values
def main(config_file, out_dir_name):
"""
Execute Main function for the classifier.
Trains the model with a given dataset.
"""
t0 = time.time()
# Load configuration
# Load the configuration of a given experiment.
config = configparser.ConfigParser()
config.read(config_file)
# Create output directory to store results
out_dir = (config["folders"]["EXPERIMENTS"] +
out_dir_name + os.sep)
# Create out directory
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Load tranining parameters
batch_size = int(config["model"]["batch_size"])
epochs = int(config["model"]["epochs"])
weights_file = config["model"]["weights"]
mean_file = config["model"]["mean"]
metadata_file = config["data"]["metadata"]
bids_folder = config["data"]["bids_folder"]
# Load BIDS layout
layout = bids.layout.BIDSLayout([(bids_folder, 'bids')])
# Divide between test, train and validation
df_metadata = pd.read_csv(metadata_file)
# add a new column with the path of each file
paths = []
for subj in df_metadata.itertuples():
# locate the corresponding MRI scan
ptid = subj.PTID
ptid_bids = 'ADNI' + ptid[0:3] + 'S' + ptid[6:]
# Hardcoded baselines
try:
file = layout.get(subject=ptid_bids, extensions='.nii.gz',
modality='anat', session='M00',
return_type='file')[0]
paths.append(file)
except:
print('Ignoring subject ' + ptid)
paths.append(np.nan)
# remove subjects with missing entries
df_metadata['path'] = paths
df_metadata = df_metadata.dropna()
print(len(df_metadata))
# Get list of unique subjects
subj = df_metadata.PTID.values
dx = df_metadata.DX.values
s = list(set(zip(subj, dx)))
# DEBUGGING: SELECT SMALL AMOUNT OF THINGS
x, y = zip(*s)
# Get train/test/val
# THOSE ARE SUBJECT NAMES
rd_seed = 1714
S_train, S_test, DX_train, DX_test = train_test_split(x, y, test_size=.2, random_state=rd_seed)
# Preprocess labels
label_dict = dict(zip(["NL", "MCI", "AD"], range(0, 3)))
# GET CORRESPONDING DX AND PATHS OF SAID SUBJECTS
X_train = df_metadata[df_metadata["PTID"].isin(S_train)].path.values
Y_train = df_metadata[df_metadata["PTID"].isin(S_train)].DX.map(label_dict, na_action='ignore').values
X_test = df_metadata[df_metadata["PTID"].isin(S_test)].path.values
Y_test = df_metadata[df_metadata["PTID"].isin(S_test)].DX.map(label_dict, na_action='ignore').values
# Create sequences of train/test (no really need for validation here)
BrainSeq = BrainSequence(X_train, to_categorical(Y_train), batch_size, norm='hist',
norm_param=mean_file, train=True, crop=True)
BrainSeq_test = BrainSequence(X_test, to_categorical(Y_test), batch_size, norm='hist',
norm_param=mean_file, train=False, crop=True)
# Load model
model = HighRes3DNet_base(input_shape=(96, 96, 96, 1), weights=True, summary=True,
weights_dir=config['model']['weights'])
# Extract representations and train the simple model
img_train = extractRepresentation(model, BrainSeq)
img_test = extractRepresentation(model, BrainSeq_test)
ad_svm = SVC()
ad_lr = LogisticRegression()
is_hpc = True
#
# Initial checks
#
os.environ["ANTSPATH"] = "/homedtic/gmarti/LIB/ANTsbin/bin"
os.environ["ANTSSCRIPTS"] = "/homedtic/gmarti/LIB/ANTs/Scripts"
# Check that bids directory is not empty(TODO)
project_root = args.in_dir[0]
layout = bids.layout.BIDSLayout([(project_root, 'bids')], exclude='derivatives/')
assert len(layout.get_subjects()) > 0, "No subjects in directory!"
# Create img list
files = layout.get(extensions='.nii.gz', modality='anat', session='M00')
# Keep only the baselines and the files from the subject_file
df_subjects = pd.read_csv(args.subject_file[0])
files_true = [x for x in layout.get_subjects() if str(x[4:7] + "_S_" + x[8:12]) in df_subjects.PTID.values]
print(len(files))
print(len(files_true))
# create output directory
out_dir = args.output_path[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Main loop
#
wait_jobs = [os.path.join(os.environ['ANTSSCRIPTS'], "waitForSlurmJobs.pl"), '0', '10']
def train(config_file, out_dir_name):
"""
Execute Main function for training.
Trains the model with a given dataset.
"""
t0 = time.time()
# Load configuration
# Load the configuration of a given experiment.
config = configparser.ConfigParser()
config.read(config_file)
# Create output directory to store results
out_dir = (config["folders"]["EXPERIMENTS"] +
out_dir_name + os.sep)
# Create out directory
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Load tranining parameters
batch_size = int(config["model"]["batch_size"])
epochs = int(config["model"]["epochs"])
weights_file = config["model"]["weights"]
mean_file = config["model"]["mean"]
metadata_file = config["data"]["metadata"]
bids_folder = config["data"]["bids_folder"]
# Load BIDS layout
layout = bids.layout.BIDSLayout([(bids_folder, 'bids')])
# Divide between test, train and validation
df_metadata = pd.read_csv(metadata_file)
# add a new column with the path of each file
paths = []
for subj in df_metadata.itertuples():
# locate the corresponding MRI scan
ptid = subj.PTID
ptid_bids = 'ADNI' + ptid[0:3] + 'S' + ptid[6:]
# Hardcoded baselines
try:
file = layout.get(subject=ptid_bids, extensions='.nii.gz',
modality='anat', session='M00',
return_type='file')[0]
paths.append(file)
except:
print('Ignoring subject ' + ptid)
paths.append(np.nan)
# remove subjects with missing entries
df_metadata['path'] = paths
df_metadata = df_metadata.dropna()
print(len(df_metadata))
# Get list of unique subjects
subj = df_metadata.PTID.values
dx = df_metadata.DX.values
s = list(set(zip(subj, dx)))
# DEBUGGING: SELECT SMALL AMOUNT OF THINGS
x, y = zip(*s)
# Get train/test/val
# THOSE ARE SUBJECT NAMES
rd_seed = 1714
S_train, S_test, DX_train, DX_test = train_test_split(x, y, test_size=.2, random_state=rd_seed)
S_train, S_val, DX_train, DX_val = train_test_split(S_train, DX_train, test_size=.2, random_state=rd_seed)
# Preprocess labels
label_dict = dict(zip(["NL", "MCI", "AD"], range(0, 3)))
# GET CORRESPONDING DX AND PATHS OF SAID SUBJECTS
X_train = df_metadata[df_metadata["PTID"].isin(S_train)].path.values
Y_train = df_metadata[df_metadata["PTID"].isin(S_train)].DX.map(label_dict, na_action='ignore').values
X_valid = df_metadata[df_metadata["PTID"].isin(S_val)].path.values
Y_valid = df_metadata[df_metadata["PTID"].isin(S_val)].DX.map(label_dict, na_action='ignore').values
X_test = df_metadata[df_metadata["PTID"].isin(S_test)].path.values
Y_test = df_metadata[df_metadata["PTID"].isin(S_test)].DX.map(label_dict, na_action='ignore').values
# Test: create list of images
# X_train_img = [load_img(x) for x in X_train]
# X_train_val = [load_img(x) for x in X_valid]
# Create generator File
BrainSeq = BrainSequence(X_train, to_categorical(Y_train), batch_size, norm='hist',
norm_param=mean_file, train=True, crop=True)
BrainSeq_val = BrainSequence(X_valid, to_categorical(Y_valid), batch_size, norm='hist',
norm_param=mean_file, train=False, crop=True)
# Initialize model
model = HighRes3DNet_cs(input_shape=(96, 96, 96, 1), weights=True, summary=True,
weights_dir=config['model']['weights'])
# sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
adam = Adam(lr=0.001, amsgrad=False)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
# Train
callb = TensorBoard(log_dir=out_dir + 'logs/', histogram_freq=0, batch_size=batch_size,
write_graph=True, write_grads=False, write_images=False,
embeddings_freq=0, embeddings_layer_names=None,
embeddings_metadata=None, embeddings_data=None)
model.fit_generator(BrainSeq,
steps_per_epoch=None,
epochs=epochs,
shuffle=True,
callbacks=[callb],
verbose=1,
validation_data=BrainSeq_val)
# TODO: Validate the model with a custom predictive function
BrainSeq_test = BrainSequence(X_test, to_categorical(Y_test), batch_size, norm='hist',
norm_param=mean_file, train=False, crop=True)
# evaluate
score = model.evaluate_generator(BrainSeq_test)
print(score)
print('Proces finished.')
t1 = time.time()
print('Time to compute the script: ', t1 - t0)
out_dir = args.in_dir[0] + 'derivatives/' + args.out_name[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Main loop
#
antsregistration_path = os.path.join(os.environ['ANTSPATH'],
'antsRegistration')
wait_jobs = [
os.path.join(os.environ['ANTSSCRIPTS'], "waitForSlurmJobs.pl"), '0', '10'
]
for s in subjects:
# Get baseline subject
files = layout.get(subject=s, session='M00', extensions='.nii.gz')
try:
baseline = files[0]
except:
print('Error in ' + s + ', no baseline.')
continue
baseline_path = baseline.filename
# Get list of all subjets
files = layout.get(subject=s, extensions='.nii.gz')
for img in files:
if img.session == baseline.session:
# If baseline is the same, remove it
continue
img_path = img.filename
img_file = os.path.basename(img_path)
# # Check platform
if platform == 'darwin':
is_hpc = False
else:
is_hpc = True
# Check that bids directory is not empty(TODO)
project_root = args.input_dir[0]
print(project_root)
layout = bids.layout.BIDSLayout([(project_root, 'bids')])
assert len(layout.get_subjects()) > 0, "No subjects in directory!"
# Create img list
files = layout.get(extensions=args.input_suffix[0], modality='anat')
# create output directory
# output directory is of the form:
out_dir = args.out_dir[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
wait_jobs = [
os.path.join(os.environ['ANTSSCRIPTS'], "waitForSGEQJobs.pl"), '0', '30'
]
for img in files:
img_path = img.filename
img_file = os.path.basename(img_path)
img_name = img_file.split(args.input_suffix[0])[0]
MRI_BIDS.append("No")
continue
f = glob.glob(ADNI_DIR + subj + "/*/*/*/*I" + imageid + ".nii")
# if found, add information to columns
if f:
MRI_ADNI.append("Yes")
# If not, add missing data
else:
MRI_ADNI.append("No")
# Test for BIDS
# Get session name
session = ''
if row.VISCODE == 'bl':
session = 'M00'
else:
session = 'M' + row.VISCODE[1:]
patient_id = 'ADNI' + subj[0:3] + 'S' + subj[6:]
imgs = layout.get(subject=patient_id, modality='anat', session=session)
# If exists
if imgs:
MRI_BIDS.append("Yes")
else:
MRI_BIDS.append("No")
df_metadata.loc[:, "MRI_ADNI"] = MRI_ADNI
df_metadata.loc[:, "PET_ADNI"] = PET_ADNI
df_metadata.loc[:, "MRI_BIDS"] = MRI_BIDS
df_metadata.loc[:, "PET_BIDS"] = PET_BIDS
df_metadata.to_csv("summary_files.csv")
def main(config_file, out_dir_name):
"""
Execute Main function for training.
Trains the model with a given dataset.
"""
t0 = time.time()
rd_seed = 1714
np.random.seed(rd_seed)
# Load configuration
# Load the configuration of a given experiment.
config = configparser.ConfigParser()
config.read(config_file)
# Create output directory to store results
out_dir = (config["folders"]["EXPERIMENTS"] + out_dir_name + os.sep)
# Create out directory
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Load tranining parameters
batch_size = int(config["model"]["batch_size"])
epochs = int(config["model"]["epochs"])
metadata_file = config["data"]["metadata"]
bids_folder = config["data"]["bids_folder"]
# Load BIDS layout
layout = bids.layout.BIDSLayout([(bids_folder, 'bids')])
## Load data (THIS NEED TO BE CHANGED)
# ALL THE DATA LOADING MUST BE CHANGED
# Divide between test, train and validation
df_metadata = pd.read_csv(metadata_file)
# add a new column with the path of each file
paths = []
for subj in df_metadata.itertuples():
# locate the corresponding MRI scan
ptid = subj.PTID
# If it is not NL or AD, ignore
if subj.DX not in ['NL', 'AD']:
paths.append(np.nan)
continue
ptid_bids = 'ADNI' + ptid[0:3] + 'S' + ptid[6:]
# Hardcoded baselines
try:
file = layout.get(subject=ptid_bids,
extensions='.nii.gz',
modality='anat',
session='M00',
return_type='file')[0]
paths.append(file)
except:
print('Ignoring subject ' + ptid)
paths.append(np.nan)
# remove subjects with missing entries
df_metadata['path'] = paths
df_metadata = df_metadata.dropna()
print(len(df_metadata))
# Get list of unique subjects
subj = df_metadata.PTID.values
dx = df_metadata.DX.values
s = list(set(zip(subj, dx)))
x, y = zip(*s)
# Get train/test/val
# THOSE ARE SUBJECT NAMES
S_train, S_test, DX_train, DX_test = train_test_split(x,
y,
test_size=.1,
random_state=rd_seed)
# Preprocess labels
label_dict = dict(zip(["NL", "AD"], range(0, 2)))
# GET CORRESPONDING DX AND PATHS OF SAID SUBJECTS
X_train = df_metadata[df_metadata["PTID"].isin(S_train)].path.values
Y_train = df_metadata[df_metadata["PTID"].isin(S_train)].DX.map(
label_dict, na_action='ignore').values
X_test = df_metadata[df_metadata["PTID"].isin(S_test)].path.values
Y_test = df_metadata[df_metadata["PTID"].isin(S_test)].DX.map(
label_dict, na_action='ignore').values
# Create sequences of train/test (no really need for validation here)
BrainSeq = BrainSequence(X_train,
to_categorical(Y_train),
batch_size,
norm='none',
train=True,
crop=False,
new_size=(193, 229, 193))
BrainSeq_val = BrainSequence(X_test,
to_categorical(Y_test),
batch_size,
norm='none',
train=False,
crop=False,
new_size=(193, 229, 193))
# Load data (THIS NEEDS TO BE CHANGED)
# Create model
model = CNN3D(input_shape=(193, 229, 193, 1))
opt = Adam(lr=0.0001)
# Compile model
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
# Create callbacks
# Model checkpoint to save the training results
checkpointer = ModelCheckpoint(filepath=out_dir + "model_trained.h5",
verbose=0,
save_best_only=True,
save_weights_only=True)
# CSVLogger to save the training results in a csv file
csv_logger = CSVLogger(out_dir + 'csv_log.csv', separator=';')
# Callback to reduce learning rate
def lr_scheduler(epoch, lr):
if epoch == 15:
return lr
elif epoch == 25:
return lr * .1
elif epoch == 35:
return lr * .1
else:
return lr
lrs = LearningRateScheduler(lr_scheduler)
# Callback to terminate on NaN loss (so terminate on error)
NanLoss = TerminateOnNaN()
callbacks = [checkpointer, csv_logger, NanLoss, lrs]
# Train model
model.fit_generator(BrainSeq,
steps_per_epoch=None,
epochs=epochs,
shuffle=True,
callbacks=callbacks,
verbose=1,
validation_data=BrainSeq_val)
# Model is saved due to callbacks
print('The end.')
t1 = time.time()
print('Time to compute the script: ', t1 - t0)
