def main():
parser = build_args_parser()
args = parser.parse_args()
print(args)
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(pjoin(experiment_path, "..", "hyperparams.json"))
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset([args.streamlines], volume_manager, name="dataset", use_sh_coeffs=hyperparams['use_sh_coeffs'])
print("Dataset size:", len(dataset))
with Timer("Loading model"):
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path, volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path, volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path, volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
print(str(model))
tractogram_file = pjoin(experiment_path, args.out)
if not os.path.isfile(tractogram_file) or args.force:
if args.method == 'prediction':
tractogram = prediction_tractogram(hyperparams, model, dataset, args.batch_size, args.prediction)
elif args.method == 'evaluation':
tractogram = evaluation_tractogram(hyperparams, model, dataset, args.batch_size, args.metric)
else:
raise ValueError("Unrecognized method: {}".format(args.method))
tractogram.affine_to_rasmm = dataset.subjects[0].signal.affine
nib.streamlines.save(tractogram, tractogram_file)
else:
print("Tractogram already exists. (use --force to generate it again)")
def main():
parser = build_args_parser()
args = parser.parse_args()
print(args)
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "..", "hyperparams.json"))
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset(
[args.streamlines],
volume_manager,
name="dataset",
use_sh_coeffs=hyperparams['use_sh_coeffs'])
print("Dataset size:", len(dataset))
with Timer("Loading model"):
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(
experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path,
volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
print(str(model))
tractogram_file = pjoin(experiment_path, args.out)
if not os.path.isfile(tractogram_file) or args.force:
if args.method == 'prediction':
tractogram = prediction_tractogram(hyperparams, model, dataset,
args.batch_size,
args.prediction)
elif args.method == 'evaluation':
tractogram = evaluation_tractogram(hyperparams, model, dataset,
args.batch_size, args.metric)
else:
raise ValueError("Unrecognized method: {}".format(args.method))
tractogram.affine_to_rasmm = dataset.subjects[0].signal.affine
nib.streamlines.save(tractogram, tractogram_file)
else:
print("Tractogram already exists. (use --force to generate it again)")
def main():
parser = build_parser()
args = parser.parse_args()
print(args)
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(pjoin(experiment_path, "..", "hyperparams.json"))
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset(args.subjects, volume_manager, name="dataset", use_sh_coeffs=hyperparams['use_sh_coeffs'])
print("Dataset size:", len(dataset))
with Timer("Loading model"):
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path, volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path, volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path, volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
with Timer("Building evaluation function"):
# Override K for gru_multistep
if 'k' in hyperparams:
hyperparams['k'] = 1
batch_scheduler = batch_scheduler_factory(hyperparams, dataset, train_mode=False, batch_size_override=args.batch_size)
loss = loss_factory(hyperparams, model, dataset, loss_type=args.loss_type)
l2_error = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
with Timer("Evaluating...", newline=True):
results_file = pjoin(experiment_path, "results.json")
results = {}
if os.path.isfile(results_file) and not args.force:
print("Loading saved results... (use --force to re-run evaluation)")
results = smartutils.load_dict_from_json_file(results_file)
tag = ""
if args.loss_type == 'expected_value' or hyperparams['model'] == 'gru_regression':
tag = "_EV_L2_error"
elif args.loss_type == 'maximum_component':
tag = "_MC_L2_error"
elif hyperparams['model'] == 'gru_mixture' or hyperparams['model'] == 'gru_multistep':
tag = "_NLL"
entry = args.dataset_name + tag
if entry not in results or args.force:
with Timer("Evaluating {}".format(entry)):
dummy_status = Status() # Forces recomputing results
results[entry] = {'mean': float(l2_error.mean.view(dummy_status)), 'stderror': float(l2_error.stderror.view(dummy_status))}
smartutils.save_dict_to_json_file(results_file, results) # Update results file.
print("{}: {:.4f} ± {:.4f}".format(entry, results[entry]['mean'], results[entry]['stderror']))
def main():
parser = build_parser()
args = parser.parse_args()
print(args)
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "..", "hyperparams.json"))
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {
'feed_previous_direction': False,
'predict_offset': False,
'normalize': False,
'keep_step_size': False,
'sort_streamlines': False,
'use_layer_normalization': False,
'drop_prob': 0.,
'use_zoneout': False
}
for new_hyperparams, default_value in retrocompatibility_defaults.items():
if new_hyperparams not in hyperparams:
hyperparams[new_hyperparams] = default_value
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset(
args.subjects,
volume_manager,
name="dataset",
use_sh_coeffs=hyperparams['use_sh_coeffs'])
print("Dataset size:", len(dataset))
with Timer("Loading model"):
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_gaussian':
from learn2track.models import GRU_Gaussian
model = GRU_Gaussian.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(
experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path,
volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
model.drop_prob = 0. # Make sure dropout/zoneout is not used when testing
with Timer("Building evaluation function"):
# Override K for gru_multistep
if 'k' in hyperparams:
hyperparams['k'] = 1
batch_scheduler = batch_scheduler_factory(
hyperparams,
dataset,
train_mode=False,
batch_size_override=args.batch_size)
loss = loss_factory(hyperparams,
model,
dataset,
loss_type=args.loss_type)
l2_error = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
with Timer("Evaluating...", newline=True):
results_file = pjoin(experiment_path, "results.json")
results = {}
if os.path.isfile(results_file) and not args.force:
print(
"Loading saved results... (use --force to re-run evaluation)")
results = smartutils.load_dict_from_json_file(results_file)
tag = ""
if args.loss_type == 'expected_value' or hyperparams[
'model'] == 'gru_regression':
tag = "_EV_L2_error"
elif args.loss_type == 'maximum_component':
tag = "_MC_L2_error"
elif hyperparams['model'] in [
'gru_gaussian', 'gru_mixture', 'gru_multistep'
]:
tag = "_NLL"
entry = args.dataset_name + tag
if entry not in results or args.force:
with Timer("Evaluating {}".format(entry)):
dummy_status = Status() # Forces recomputing results
results[entry] = {
'mean': float(l2_error.mean.view(dummy_status)),
'stderror': float(l2_error.stderror.view(dummy_status))
}
smartutils.save_dict_to_json_file(
results_file, results) # Update results file.
print("{}: {:.4f} ± {:.4f}".format(entry, results[entry]['mean'],
results[entry]['stderror']))
def model_factory(hyperparams, input_size, output_size, volume_manager):
if hyperparams['model'] == 'gru_regression' and hyperparams[
'learn_to_stop']:
raise NotImplementedError()
# from learn2track.models import GRU_RegressionAndBinaryClassification
# return GRU_RegressionAndBinaryClassification(batch_scheduler.input_size,
# hyperparams['hidden_sizes'],
# batch_scheduler.target_size)
elif hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
return GRU_Regression(
volume_manager=volume_manager,
input_size=input_size,
hidden_sizes=hyperparams['hidden_sizes'],
output_size=output_size,
activation=hyperparams['activation'],
use_previous_direction=hyperparams['feed_previous_direction'],
predict_offset=hyperparams['predict_offset'],
use_layer_normalization=hyperparams['use_layer_normalization'],
drop_prob=hyperparams['drop_prob'],
use_zoneout=hyperparams['use_zoneout'],
use_skip_connections=hyperparams['skip_connections'],
seed=hyperparams['seed'])
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
return GRU_Multistep_Gaussian(
volume_manager=volume_manager,
input_size=input_size,
hidden_sizes=hyperparams['hidden_sizes'],
target_dims=output_size,
k=hyperparams['k'],
m=hyperparams['m'],
seed=hyperparams['seed'],
use_previous_direction=hyperparams['feed_previous_direction'],
use_layer_normalization=hyperparams['use_layer_normalization'],
drop_prob=hyperparams['drop_prob'],
use_zoneout=hyperparams['use_zoneout'])
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
return GRU_Mixture(
volume_manager=volume_manager,
input_size=input_size,
hidden_sizes=hyperparams['hidden_sizes'],
output_size=output_size,
n_gaussians=hyperparams['n_gaussians'],
activation=hyperparams['activation'],
use_previous_direction=hyperparams['feed_previous_direction'],
use_layer_normalization=hyperparams['use_layer_normalization'],
drop_prob=hyperparams['drop_prob'],
use_zoneout=hyperparams['use_zoneout'],
use_skip_connections=hyperparams['skip_connections'],
seed=hyperparams['seed'])
elif hyperparams['model'] == 'gru_gaussian':
from learn2track.models import GRU_Gaussian
return GRU_Gaussian(
volume_manager=volume_manager,
input_size=input_size,
hidden_sizes=hyperparams['hidden_sizes'],
output_size=output_size,
use_previous_direction=hyperparams['feed_previous_direction'],
use_layer_normalization=hyperparams['use_layer_normalization'],
drop_prob=hyperparams['drop_prob'],
use_zoneout=hyperparams['use_zoneout'],
use_skip_connections=hyperparams['skip_connections'],
seed=hyperparams['seed'])
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
return FFNN_Regression(
volume_manager=volume_manager,
input_size=input_size,
hidden_sizes=hyperparams['hidden_sizes'],
output_size=output_size,
activation=hyperparams['activation'],
use_previous_direction=hyperparams['feed_previous_direction'],
predict_offset=hyperparams['predict_offset'],
use_layer_normalization=hyperparams['use_layer_normalization'],
dropout_prob=hyperparams['dropout_prob'],
use_skip_connections=hyperparams['skip_connections'],
seed=hyperparams['seed'])
else:
raise ValueError("Unknown model!")
