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.subject], volume_manager, name="dataset", use_sh_coeffs=hyperparams["use_sh_coeffs"]
)
print("Dataset size:", len(dataset))
with Timer("Loading model"):
if hyperparams["model"] == "gru_regression":
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path, volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams["model"]))
with Timer("Building evaluation function"):
loss = loss_factory(hyperparams, model, dataset)
batch_scheduler = batch_schedulers.TractographyBatchScheduler(
dataset,
batch_size=1000,
noisy_streamlines_sigma=None,
use_data_augment=False, # Otherwise it doubles the number of losses :-/
seed=1234,
shuffle_streamlines=False,
normalize_target=hyperparams["normalize"],
)
loss_view = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
losses = loss_view.losses.view()
with Timer("Saving streamlines"):
tractogram = Tractogram(dataset.streamlines, affine_to_rasmm=dataset.subjects[0].signal.affine)
tractogram.data_per_streamline["loss"] = losses
nib.streamlines.save(tractogram, args.out)
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_argparser()
args = parser.parse_args()
print(args)
print("Using Theano v.{}".format(theano.version.short_version))
hyperparams_to_exclude = ['max_epoch', 'force', 'name', 'view', 'shuffle_streamlines']
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {'feed_previous_direction': False,
'normalize': False}
experiment_path, hyperparams, resuming = utils.maybe_create_experiment_folder(args, exclude=hyperparams_to_exclude,
retrocompatibility_defaults=retrocompatibility_defaults)
# Log the command currently running.
with open(pjoin(experiment_path, 'cmd.txt'), 'a') as f:
f.write(" ".join(sys.argv) + "\n")
print("Resuming:" if resuming else "Creating:", experiment_path)
with Timer("Loading dataset", newline=True):
trainset_volume_manager = VolumeManager()
validset_volume_manager = VolumeManager()
trainset = datasets.load_tractography_dataset(args.train_subjects, trainset_volume_manager, name="trainset", use_sh_coeffs=args.use_sh_coeffs)
validset = datasets.load_tractography_dataset(args.valid_subjects, validset_volume_manager, name="validset", use_sh_coeffs=args.use_sh_coeffs)
print("Dataset sizes:", len(trainset), " |", len(validset))
if args.view:
tsne_view(trainset, trainset_volume_manager)
sys.exit(0)
batch_scheduler = batch_scheduler_factory(hyperparams, dataset=trainset, train_mode=True)
print("An epoch will be composed of {} updates.".format(batch_scheduler.nb_updates_per_epoch))
print(trainset_volume_manager.data_dimension, args.hidden_sizes, batch_scheduler.target_size)
with Timer("Creating model"):
input_size = trainset_volume_manager.data_dimension
if hyperparams['feed_previous_direction']:
input_size += 3
model = model_factory(hyperparams,
input_size=input_size,
output_size=batch_scheduler.target_size,
volume_manager=trainset_volume_manager)
model.initialize(weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
with Timer("Building optimizer"):
loss = loss_factory(hyperparams, model, trainset)
if args.clip_gradient is not None:
loss.append_gradient_modifier(DirectionClipping(threshold=args.clip_gradient))
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
trainer.append_task(avg_loss)
# Print average training loss.
trainer.append_task(tasks.Print("Avg. training loss: : {}", avg_loss))
# if args.learn_to_stop:
# l2err_monitor = views.MonitorVariable(T.mean(loss.mean_sqr_error))
# avg_l2err = tasks.AveragePerEpoch(l2err_monitor)
# trainer.append_task(avg_l2err)
#
# crossentropy_monitor = views.MonitorVariable(T.mean(loss.cross_entropy))
# avg_crossentropy = tasks.AveragePerEpoch(crossentropy_monitor)
# trainer.append_task(avg_crossentropy)
#
# trainer.append_task(tasks.Print("Avg. training L2 err: : {}", avg_l2err))
# trainer.append_task(tasks.Print("Avg. training stopping: : {}", avg_crossentropy))
# trainer.append_task(tasks.Print("L2 err : {0:.4f}", l2err_monitor, each_k_update=100))
# trainer.append_task(tasks.Print("stopping : {0:.4f}", crossentropy_monitor, each_k_update=100))
# Print NLL mean/stderror.
# train_loss = L2DistanceForSequences(model, trainset)
# train_batch_scheduler = StreamlinesBatchScheduler(trainset, batch_size=1000,
# noisy_streamlines_sigma=None,
# nb_updates_per_epoch=None,
# seed=1234)
# train_error = views.LossView(loss=train_loss, batch_scheduler=train_batch_scheduler)
# trainer.append_task(tasks.Print("Trainset - Error : {0:.2f} | {1:.2f}", train_error.sum, train_error.mean))
# HACK: To make sure all subjects in the volume_manager are used in a batch, we have to split the trainset/validset in 2 volume managers
model.volume_manager = validset_volume_manager
valid_loss = loss_factory(hyperparams, model, validset)
valid_batch_scheduler = batch_scheduler_factory(hyperparams,
dataset=validset,
train_mode=False)
valid_error = views.LossView(loss=valid_loss, batch_scheduler=valid_batch_scheduler)
trainer.append_task(tasks.Print("Validset - Error : {0:.2f} | {1:.2f}", valid_error.sum, valid_error.mean))
# HACK: Restore trainset volume manager
model.volume_manager = trainset_volume_manager
lookahead_loss = valid_error.sum
direction_norm = views.MonitorVariable(T.sqrt(sum(map(lambda d: T.sqr(d).sum(), loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm))
# logger = tasks.Logger(train_error.mean, valid_error.mean, valid_error.sum, direction_norm)
logger = tasks.Logger(valid_error.mean, valid_error.sum, direction_norm)
trainer.append_task(logger)
if args.view:
import pylab as plt
def _plot(*args, **kwargs):
plt.figure(1)
plt.clf()
plt.show(False)
plt.subplot(121)
plt.plot(np.array(logger.get_variable_history(0)).flatten(), label="Train")
plt.plot(np.array(logger.get_variable_history(1)).flatten(), label="Valid")
plt.legend()
plt.subplot(122)
plt.plot(np.array(logger.get_variable_history(3)).flatten(), label="||d'||")
plt.draw()
trainer.append_task(tasks.Callback(_plot))
# Callback function to stop training if NaN is detected.
def detect_nan(obj, status):
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Stopping training now.")
sys.exit()
trainer.append_task(tasks.Callback(detect_nan, each_k_update=1))
# Callback function to save training progression.
def save_training(obj, status):
trainer.save(experiment_path)
trainer.append_task(tasks.Callback(save_training))
# Early stopping with a callback for saving every time model improves.
def save_improvement(obj, status):
""" Save best model and training progression. """
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Not saving the model. Crashing now.")
sys.exit()
print("*** Best epoch: {0} ***\n".format(obj.best_epoch))
model.save(experiment_path)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
trainer.append_task(tasks.PrintTime(each_k_update=100)) # Profiling
# Add stopping criteria
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
early_stopping = stopping_criteria.EarlyStopping(lookahead_loss, lookahead=args.lookahead, eps=args.lookahead_eps, callback=save_improvement)
trainer.append_task(early_stopping)
with Timer("Compiling Theano graph"):
trainer.build_theano_graph()
if resuming:
if not os.path.isdir(pjoin(experiment_path, 'training')):
print("No 'training/' folder. Assuming it failed before"
" the end of the first epoch. Starting a new training.")
else:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
def main():
parser = build_argparser()
args = parser.parse_args()
print(args)
print("Using Theano v.{}".format(theano.version.short_version))
hyperparams_to_exclude = ['max_epoch', 'force', 'name', 'view', 'shuffle_streamlines']
# 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,
'sort_streamlines': False,
'keep_step_size': False,
'use_layer_normalization': False,
'drop_prob': 0.,
'use_zoneout': False,
'skip_connections': False}
experiment_path, hyperparams, resuming = utils.maybe_create_experiment_folder(args, exclude=hyperparams_to_exclude,
retrocompatibility_defaults=retrocompatibility_defaults)
# Log the command currently running.
with open(pjoin(experiment_path, 'cmd.txt'), 'a') as f:
f.write(" ".join(sys.argv) + "\n")
print("Resuming:" if resuming else "Creating:", experiment_path)
with Timer("Loading dataset", newline=True):
trainset_volume_manager = VolumeManager()
validset_volume_manager = VolumeManager()
trainset = datasets.load_tractography_dataset(args.train_subjects, trainset_volume_manager, name="trainset",
use_sh_coeffs=args.use_sh_coeffs)
validset = datasets.load_tractography_dataset(args.valid_subjects, validset_volume_manager, name="validset",
use_sh_coeffs=args.use_sh_coeffs)
print("Dataset sizes:", len(trainset), " |", len(validset))
batch_scheduler = batch_scheduler_factory(hyperparams, dataset=trainset, train_mode=True)
print("An epoch will be composed of {} updates.".format(batch_scheduler.nb_updates_per_epoch))
print(trainset_volume_manager.data_dimension, args.hidden_sizes, batch_scheduler.target_size)
with Timer("Creating model"):
input_size = trainset_volume_manager.data_dimension
if hyperparams['feed_previous_direction']:
input_size += 3
model = model_factory(hyperparams,
input_size=input_size,
output_size=batch_scheduler.target_size,
volume_manager=trainset_volume_manager)
model.initialize(weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
with Timer("Building optimizer"):
loss = loss_factory(hyperparams, model, trainset)
if args.clip_gradient is not None:
loss.append_gradient_modifier(DirectionClipping(threshold=args.clip_gradient))
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
trainer.append_task(avg_loss)
# Print average training loss.
trainer.append_task(tasks.Print("Avg. training loss: : {}", avg_loss))
# if args.learn_to_stop:
# l2err_monitor = views.MonitorVariable(T.mean(loss.mean_sqr_error))
# avg_l2err = tasks.AveragePerEpoch(l2err_monitor)
# trainer.append_task(avg_l2err)
#
# crossentropy_monitor = views.MonitorVariable(T.mean(loss.cross_entropy))
# avg_crossentropy = tasks.AveragePerEpoch(crossentropy_monitor)
# trainer.append_task(avg_crossentropy)
#
# trainer.append_task(tasks.Print("Avg. training L2 err: : {}", avg_l2err))
# trainer.append_task(tasks.Print("Avg. training stopping: : {}", avg_crossentropy))
# trainer.append_task(tasks.Print("L2 err : {0:.4f}", l2err_monitor, each_k_update=100))
# trainer.append_task(tasks.Print("stopping : {0:.4f}", crossentropy_monitor, each_k_update=100))
# Print NLL mean/stderror.
# train_loss = L2DistanceForSequences(model, trainset)
# train_batch_scheduler = StreamlinesBatchScheduler(trainset, batch_size=1000,
# noisy_streamlines_sigma=None,
# nb_updates_per_epoch=None,
# seed=1234)
# train_error = views.LossView(loss=train_loss, batch_scheduler=train_batch_scheduler)
# trainer.append_task(tasks.Print("Trainset - Error : {0:.2f} | {1:.2f}", train_error.sum, train_error.mean))
# HACK: To make sure all subjects in the volume_manager are used in a batch, we have to split the trainset/validset in 2 volume managers
model.volume_manager = validset_volume_manager
model.drop_prob = 0. # Do not use dropout/zoneout for evaluation
valid_loss = loss_factory(hyperparams, model, validset)
valid_batch_scheduler = batch_scheduler_factory(hyperparams,
dataset=validset,
train_mode=False)
valid_error = views.LossView(loss=valid_loss, batch_scheduler=valid_batch_scheduler)
trainer.append_task(tasks.Print("Validset - Error : {0:.2f} | {1:.2f}", valid_error.sum, valid_error.mean))
if hyperparams['model'] == 'ffnn_regression':
valid_batch_scheduler2 = batch_scheduler_factory(hyperparams,
dataset=validset,
train_mode=False)
valid_l2 = loss_factory(hyperparams, model, validset, loss_type="expected_value")
valid_l2_error = views.LossView(loss=valid_l2, batch_scheduler=valid_batch_scheduler2)
trainer.append_task(tasks.Print("Validset - {}".format(valid_l2.__class__.__name__) + "\t: {0:.2f} | {1:.2f}", valid_l2_error.sum, valid_l2_error.mean))
# HACK: Restore trainset volume manager
model.volume_manager = trainset_volume_manager
model.drop_prob = hyperparams['drop_prob'] # Restore dropout
lookahead_loss = valid_error.sum
direction_norm = views.MonitorVariable(T.sqrt(sum(map(lambda d: T.sqr(d).sum(), loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm))
# logger = tasks.Logger(train_error.mean, valid_error.mean, valid_error.sum, direction_norm)
logger = tasks.Logger(valid_error.mean, valid_error.sum, direction_norm)
trainer.append_task(logger)
if args.view:
import pylab as plt
def _plot(*args, **kwargs):
plt.figure(1)
plt.clf()
plt.show(False)
plt.subplot(121)
plt.plot(np.array(logger.get_variable_history(0)).flatten(), label="Train")
plt.plot(np.array(logger.get_variable_history(1)).flatten(), label="Valid")
plt.legend()
plt.subplot(122)
plt.plot(np.array(logger.get_variable_history(3)).flatten(), label="||d'||")
plt.draw()
trainer.append_task(tasks.Callback(_plot))
# Callback function to stop training if NaN is detected.
def detect_nan(obj, status):
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Stopping training now.")
sys.exit()
trainer.append_task(tasks.Callback(detect_nan, each_k_update=1))
# Callback function to save training progression.
def save_training(obj, status):
trainer.save(experiment_path)
trainer.append_task(tasks.Callback(save_training))
# Early stopping with a callback for saving every time model improves.
def save_improvement(obj, status):
""" Save best model and training progression. """
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Not saving the model. Crashing now.")
sys.exit()
print("*** Best epoch: {0} ***\n".format(obj.best_epoch))
model.save(experiment_path)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
trainer.append_task(tasks.PrintTime(each_k_update=100)) # Profiling
# Add stopping criteria
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
early_stopping = stopping_criteria.EarlyStopping(lookahead_loss, lookahead=args.lookahead, eps=args.lookahead_eps, callback=save_improvement)
trainer.append_task(early_stopping)
with Timer("Compiling Theano graph"):
trainer.build_theano_graph()
if resuming:
if not os.path.isdir(pjoin(experiment_path, 'training')):
print("No 'training/' folder. Assuming it failed before"
" the end of the first epoch. Starting a new training.")
else:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
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_argparser()
args = parser.parse_args()
print(args)
print("Using Theano v.{}".format(theano.version.short_version))
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset(
args.subjects,
volume_manager,
name="dataset",
use_sh_coeffs=args.use_sh_coeffs)
print("Total streamlines: {}".format(len(dataset)))
with Timer("Running T-SNE", newline=True):
batch_scheduler = TractographyBatchScheduler(
dataset,
batch_size=min(len(dataset), 100000),
noisy_streamlines_sigma=False,
seed=1234,
normalize_target=True)
rng = np.random.RandomState(42)
rng.shuffle(batch_scheduler.indices)
# bundle_name_pattern = "CST_Left"
# batch_inputs, batch_targets, batch_mask = batch_scheduler._prepare_batch(trainset.get_bundle(bundle_name_pattern, return_idx=True))
inputs, _, mask = batch_scheduler._next_batch(0)
# Keep the same number of streamlines per subject
new_inputs = []
new_mask = []
for i in range(len(args.subjects)):
subset = inputs[:, 0, -1] == i
if subset.sum() < args.nb_streamlines_per_subject:
raise NameError(
"Not enough streamlines for subject #{}".format(i))
new_inputs += [inputs[subset][:args.nb_streamlines_per_subject]]
new_mask += [mask[subset][:args.nb_streamlines_per_subject]]
inputs = np.concatenate([new_inputs], axis=0)
mask = np.concatenate([new_mask], axis=0)
mask = mask.astype(bool)
idx = np.arange(mask.sum())
rng.shuffle(idx)
coords = T.matrix('coords')
eval_at_coords = theano.function([coords],
volume_manager.eval_at_coords(coords))
M = 2000 * len(dataset.subjects)
coords = inputs[mask][idx[:M]]
X = eval_at_coords(coords)
from sklearn.manifold.t_sne import TSNE
tsne = TSNE(n_components=2, verbose=2, random_state=42)
Y = tsne.fit_transform(X)
import matplotlib.pyplot as plt
plt.figure()
ids = range(len(dataset.subjects))
markers = ['s', 'o', '^', 'v', '<', '>', 'h']
colors = ['cyan', 'darkorange', 'darkgreen', 'magenta', 'pink', 'k']
for i, marker, color in zip(ids, markers, colors):
idx = coords[:, -1] == i
print("Subject #{}: ".format(i), idx.sum())
plt.scatter(Y[idx, 0],
Y[idx, 1],
20,
color=color,
marker=marker,
label="Subject #{}".format(i))
plt.legend()
plt.show()
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']))
