def main(model, training_cnf, data_dir, start_epoch, resume_lr, weights_from,
clean, visuals):
util.check_required_program_args([model, training_cnf, data_dir])
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO,
clean=clean)
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
training_iter, validation_iter = create_training_iters(
cnf, data_set, model_def.crop_size, start_epoch,
cnf.get('iterator_type', 'queued') == 'parallel')
trainer = SupervisedTrainer(model,
cnf,
training_iter,
validation_iter,
classification=cnf['classification'])
trainer.fit(data_set,
weights_from,
start_epoch,
resume_lr,
verbose=1,
summary_every=cnf.get('summary_every', 10),
clean=clean,
visuals=visuals)
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
resume_lr, gpu_memory_fraction, is_summary, num_classes,
log_file_name):
model_def = util.load_module(model)
model = model_def
cnf = util.load_module(training_cnf).cnf
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
standardizer = cnf.get('standardizer', NoOpStandardizer())
training_iter, validation_iter = create_training_iters(cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel)
trainer = SemiSupervisedTrainer(model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
is_summary=is_summary,
verbosity=1,
log_file_name=log_file_name)
trainer.fit(data_set,
num_classes,
weights_from,
start_epoch,
summary_every=399)
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
resume_lr, gpu_memory_fraction, is_summary):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO)
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
standardizer = cnf.get('standardizer', NoOpStandardizer())
training_iter, validation_iter = create_training_iters(cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel)
trainer = SupervisedTrainer(model,
cnf,
training_iter,
validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
is_summary=is_summary,
loss_type='kappa_log')
trainer.fit(data_set,
weights_from,
start_epoch,
verbose=1,
summary_every=399)
def __init__(self,
model_cnf,
data_dir,
image_size,
crop_size,
channel_dim=3,
start_epoch=1,
parallel=True):
self.cnf = util.load_module(model_cnf).cnf
self.image_size = image_size
self.crop_size = crop_size
self.channel_dim = channel_dim
self.data_set = DataSet(data_dir, image_size)
self.standardizer = self.cnf.get('standardizer', NoOpStandardizer())
self.training_iter, self.validation_iter = create_training_iters(
self.cnf,
self.data_set,
self.standardizer, [crop_size, crop_size],
start_epoch,
parallel=parallel)
self.training_X = self.data_set.training_X
self.training_y = self.data_set.training_y
self.validation_X = self.data_set.validation_X
self.validation_y = self.data_set.validation_y
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
resume_lr, gpu_memory_fraction, is_summary, num_classes):
model_def = util.load_module(model)
model = model_def
cnf = util.load_module(training_cnf).cnf
util.init_logging('train_ss.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO)
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
standardizer = cnf.get('standardizer', NoOpStandardizer())
training_iter, validation_iter = create_training_iters(cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel)
trainer = GenerativeLearner(model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
is_summary=is_summary,
verbosity=2)
trainer.fit(data_set,
num_classes,
weights_from,
start_epoch,
summary_every=399)
def try_config(args, cnf):
"""For trying out configurations.
Args:
args: command line arguments regarding training
cnf: training configuration sampled from hyperband search space
Returns:
a dictionary containing final loss value and early stop flag
"""
model_def = util.load_module(args['model'])
model = model_def.model
if args['weights_from']:
weights_from = str(args['weights_from'])
else:
weights_from = args['weights_from']
data_set = DataSet(args['data_dir'],
model_def.image_size[0],
mode=cnf.get('mode'),
multilabel=cnf.get('multilabel', False))
standardizer = cnf.get('standardizer', NoOpStandardizer())
cutout = cnf.get('cutout', None)
training_iter, validation_iter = create_training_iters(
cnf,
data_set,
standardizer,
model_def.crop_size,
args['start_epoch'],
parallel=args['parallel'],
cutout=cutout,
data_balancing=cnf.get('data_balancing', False))
learner = SupervisedLearner(
model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=args['resume_lr'],
classification=cnf['classification'],
gpu_memory_fraction=args['gpu_memory_fraction'],
num_classes=args['num_classes'],
is_summary=args['is_summary'],
loss_type=args['loss_type'],
weighted=args['weighted'],
log_file_name=args['log_file_name'],
verbosity=args['verbose'],
is_early_stop=cnf.get('is_early_stop', True))
_early_stop, _loss = learner.fit(data_set,
weights_from=weights_from,
start_epoch=args['start_epoch'],
weights_dir=args['weights_dir'],
summary_every=399)
return {'early_stop': _early_stop, 'loss': _loss}
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
weights_dir, resume_lr, gpu_memory_fraction, num_classes, is_summary,
loss_type, weighted, log_file_name):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir,
model_def.image_size[0],
mode=cnf.get('mode'),
multilabel=cnf.get('multilabel', False))
standardizer = cnf.get('standardizer', NoOpStandardizer())
cutout = cnf.get('cutout', None)
training_iter, validation_iter = create_training_iters(
cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel,
cutout=cutout,
data_balancing=cnf.get('data_balancing', False))
learner = SupervisedLearner(model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
num_classes=num_classes,
is_summary=is_summary,
loss_type=loss_type,
weighted=weighted,
log_file_name=log_file_name)
learner.fit(data_set,
weights_from,
start_epoch=start_epoch,
weights_dir=weights_dir,
summary_every=399)
def main(model, training_cnf, data_dir, start_epoch, resume_lr, weights_from,
weights_exclude_scopes, trainable_scopes, clean, visuals):
util.check_required_program_args([model, training_cnf, data_dir])
sys.path.insert(0, '.')
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO,
clean=clean)
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
training_iter, validation_iter = create_training_iters(
cnf, data_set, model_def.crop_size, start_epoch,
cnf.get('iterator_type', 'parallel') == 'parallel')
try:
input_shape = (-1, model_def.crop_size[1], model_def.crop_size[0],
model_def.num_channels)
except AttributeError:
input_shape = (-1, model_def.crop_size[1], model_def.crop_size[0], 3)
trainer = SupervisedTrainerQ(model,
cnf,
input_shape,
trainable_scopes,
training_iter,
validation_iter,
classification=cnf['classification'])
trainer.fit(data_set,
weights_from,
weights_exclude_scopes,
start_epoch,
resume_lr,
verbose=1,
summary_every=cnf.get('summary_every', 10),
clean=clean,
visuals=visuals)