def use_orcanet():
temp_folder = "output/"
os.mkdir(temp_folder)
make_dummy_data(temp_folder)
list_file = "example_list.toml"
organizer = Organizer(temp_folder + "sum_model", list_file)
organizer.cfg.train_logger_display = 10
model = make_dummy_model()
organizer.train_and_validate(model, epochs=3)
organizer.predict()
def train(directory,
list_file=None,
config_file=None,
model_file=None,
to_epoch=None):
from orcanet.core import Organizer
from orcanet.model_builder import ModelBuilder
from orcanet.misc import find_file
orga = Organizer(directory, list_file, config_file, tf_log_level=1)
if orga.io.get_latest_epoch() is None:
# Start of training
print("Building new model")
if model_file is None:
model_file = find_file(directory, "model.toml")
model = ModelBuilder(model_file).build(orga, verbose=False)
else:
model = None
return orga.train_and_validate(model=model, to_epoch=to_epoch)
def orca_train(output_folder,
list_file,
config_file,
model_file,
recompile_model=False):
"""
Run orga.train with predefined ModelBuilder networks using a parser.
Parameters
----------
output_folder : str
Path to the folder where everything gets saved to, e.g. the summary
log file, the plots, the trained models, etc.
list_file : str
Path to a list file which contains pathes to all the h5 files that
should be used for training and validation.
config_file : str
Path to a .toml file which overwrites some of the default settings
for training and validating a model.
model_file : str
Path to a file with parameters to build a model of a predefined
architecture with OrcaNet.
recompile_model : bool
If the model should be recompiled or not. Necessary, if e.g. the
loss_weights are changed during the training.
"""
# Set up the Organizer with the input data
orga = Organizer(output_folder, list_file, config_file, tf_log_level=1)
# Load in the orga sample-, label-, and dataset-modifiers, as well as
# the custom objects
update_objects(orga, model_file)
# If this is the start of the training, a compiled model needs to be
# handed to the orga.train function
if orga.io.get_latest_epoch() is None:
# The ModelBuilder class allows to construct models from a toml file,
# adapted to the datasets in the orga instance. Its modifiers will
# be taken into account for this
builder = ModelBuilder(model_file)
model = builder.build(orga, log_comp_opts=True)
elif recompile_model is True:
builder = ModelBuilder(model_file)
path_of_model = orga.io.get_model_path(-1, -1)
model = ks.models.load_model(
path_of_model, custom_objects=orga.cfg.get_custom_objects())
print("Recompiling the saved model")
model = builder.compile_model(
model, custom_objects=orga.cfg.get_custom_objects())
builder.log_model_properties(orga)
else:
model = None
try:
# Use a custom LR schedule
user_lr = orga.cfg.learning_rate
lr = orca_learning_rates(user_lr, orga.io.get_no_of_files("train"))
orga.cfg.learning_rate = lr
except NameError:
pass
# start the training
orga.train_and_validate(model=model)
